C60o gamma beam, in comparison with reference chambers whose absorbed dose‐to‐water calibration coefficients, ND,w, are known. In this work, we present the results of the calibration of 10 Farmer‐like ionization chambers calibrated in three water phantoms  and two plastic phantoms (size 20×20×20 cm3) polymethyl methacrlyate (PMMA) and polystyrene). Calibrations are performed by the substitution method using an ionization chamber whose ND,w has been supplied by the IAEA's reference laboratory. It is shown that the results, expressed as the percentage ratio of the calibration coefficient in a given phantom to that of the standard IAEA phantom, is less than 0.35% for all investigated chambers, and that the standard deviation of the mean of the ND,w calibration coefficients determined in all five phantoms is less than 0.06%, except for one nylon‐walled ionization chamber, where the observed 0.34% value could be explained by the hygroscopic properties of nylon. Furthermore, a chamber‐to‐chamber dependence of the calibration coefficient has been shown to vary by up to 2.8%. These results emphasize that the phantom dimensions and its material are not sensitive criteria for the calibration of cylindrical ionization chambers in terms of absorbed dose to water. The results also show that generic calibration coefficients could not be considered for a given type of chamber.In the International Atomic Energy Agency's (IAEA) code of practice (TRS 398) and the American Association of Physicists in Medicine's dosimetry protocol (TG‐51), full‐scatter water phantoms are recommended for the determination of the absorbed dose for both photon and electron beams and, consequently, for the calibration of the user's ionization chambers. This procedure is applied in the Secondary Standard Dosimetry Laboratory, where the calibration is performed on a PACS number: 87.53.Dq C60o gamma ray beam.It has been established that the required accuracy of the absorbed dose delivered to the target volume in radiotherapy is considerably improved if the calibration of a clinical beam is performed with ionization chambers calibrated in terms of absorbed dose to water.C60o gamma ray beam, and water is recommended as the reference medium for absorbed dose measurements.Both cylindrical and plane‐parallel ionization chambers are recommended as reference instruments for the calibration of the user's The accuracy and traceability of the calibration of radiotherapy dosimeters are of great concern, since absorbed dose cannot be obtained with non‐calibrated instruments. The IAEA's code of practice recommends that the calibration coefficient used to determine the absorbed dose be obtained in a water phantom. The recommended water phantoms should be full‐scatter ones extending at least 5 cm beyond all four sides of the largest field size used at the depth of measurement and with at least 5 cm beyond the maximum depth of measurement.In this work, we investigate the influence of the phantom size and its material over the calibration coefficient. For this purpose, 10 Farmer‐like ionization chambers were calibrated in five different phantoms using the substitution method, and the calibration coefficients were compared to those obtained in the IAEA standard water phantom.C60o gamma beams using an Eldorado 78 unit. The output in terms of absorbed dose to water was 1120 mGy/min, at a source‐to‐surface distance (SSD) of 80 cm, a depth of 5 cm, and a field size of 10×10 cm2 at the phantom surface , established at the conditions T=20 °C and P=101.325 kPa, is thus traceable to the Bureau International des Poids et Mesures .Five types of commonly used ionization chambers were provided by radiotherapy centers for the present work, giving an overall total of 10 chambers. The characteristics of these chambers are shown in Table The reference ionization current was measured with a Keithley 6517 electrometer, and the chambers listed in Table The calibrations were carried out in three water phantoms of different sizes and two plastic phantoms (PMMA and polystyrene). The characteristics of these phantoms are given in Table 10×10 cm2  the response of such chambers is strongly affected by environmental conditions due to the hygroscopic properties of nylon.The results are less satisfactory with the NE 2581 ionization chamber, which has not been included in Fig. ,5 thND,w mean is 0.15%. This appears to be a good result; however, if we consider the mean values of the calibration coefficients obtained in the four phantoms, the deviation between the maximum and the minimum values is around 2.8%. This stresses the need for individual calibration of each chamber of the same type and allows us to recommend not using generic calibration coefficients. For the polystyrene phantom, only four chambers have been made available; thus the chamber‐to‐chamber study could not be extended for this solid phantom.The chamber‐to‐chamber variation of the calibration coefficients is illustrated in Fig. Many factors affect the absorbed dose‐to‐water calibration coefficient. Some of these factors have been identified, and their effect, evaluated in terms of uncertainties, is analyzed. The overall uncertainty is calculated according to the recommendations of the ISO guide for the expression of uncertainty.(yi) in which an estimate, s(σ), of the standard deviation, σ, is obtained by a series of n measurements, applyingAccording to the ISO guide,ym is the mean value of y.where s(σ)/n, is the value to be used for the summation in quadrature for obtaining the combined standard uncertainty (see below).The standard deviation of the mean, All uncertainties that cannot be determined by a series of repeated measurements are type B uncertainties. They arise from a variety of sources, and the probability distributions may take a variety of shapes. Practical guidance on evaluating type B components is given in Ref. 6.u(y), is derived by the summation in quadrature of all type A and type B standard uncertainties due to the input parameters:The combined standard uncertainty of the output quantity, U, which represents the total uncertainty for a specific level of confidence. It is derived by multiplying the combined standard uncertainty, u(y), by a coverage factor, k, which is selected to give the desired level of confidence for a normal distribution. Typical choices for k are the integers 1, 2, or 3, which correspond to confidence levels of 67.7%, 95.5%, and 99.7%, respectively. For most radiological applications, a 95% confidence level, for which k=1.96, is recommended. For convenience, this is rounded upwards to a value of exactly 2.0.The overall uncertainty to be quoted for the calibration coefficient is the expanded uncertainty, Table For the reference measurements, a series of 20 readings is taken, and the standard deviation of the mean obtained is of the order 0.01%. The resolution of the reference instruments is equal to 0.02%. Assuming a rectangular distribution, the uncertainty is 0.01%. This uncertainty is taken to be of type B.The uncertainty over the calibration coefficient of the SSDL working standard, taken from the calibration certificate, is reported as a type B uncertainty.Constancy of the secondary standard is obtained by evaluating the long‐term stability of the secondary standard system. This uncertainty, estimated from a series of output rate measurements, is dominated by a component having a type A evaluation (standard deviation of the mean of the measurements). Because this component is not directly determined during each measurement, it is treated as a type B evaluation.±0.1%. This is evaluated as a type B component. Assuming a rectangular distribution, the relative uncertainty is 0.06%.Typically, the resolution of the therapy level dosimeters is within The leakage is evaluated before each calibration and is subtracted from the instrument reading. The uncertainty over this factor is evaluated to be 0.02%.For corrections using Eq. ±0.1%. Assuming a rectangular distribution, the type B relative uncertainty is 0.06%. The uncertainty component related to the resolution of the barometer is negligible.In Eq. This uncertainty, taken as type A, is evaluated to be 0.04%.As can be seen in Table Compared to the overall uncertainty over the absorbed dose‐to‐water calibration coefficient, which is around 1.1% (given at 95% confidence level), the difference between the calibration coefficients obtained in this study for the different phantoms and those for all the investigated ionization chambers is not significant. The most important discrepancy is observed for the NE 2581 chamber, whose response is strongly affected by environmental conditions due to the composition of its wall (nylon). Therefore, the deviations observed in this study for this particular chamber was expected.20×20×20 cm3. Smaller phantoms were not available during our study; thus, no conclusions could be drawn for these phantoms. The results concerning plastic phantoms do not seem to be consistent with modern dosimetry protocols, which recommend using water phantoms for the calibration of ionization chambers in terms of absorbed dose to water. However, we would like to point out that with the results obtained in this work, we are not advocating the calibration of ionization chambers in plastic phantoms. This should be performed according to the internationally recognized codes of practice.Our study shows that the type and dimensions of the phantom used are not critical parameters. The absorbed dose‐to‐water calibration coefficients can be determined in any water or solid phantom whose dimensions are at least Furthermore, we have shown an important chamber‐to‐chamber dependence of the calibration coefficients obtained in different phantoms, for the widely used NE 2571 ionization chamber, which demonstrates the individual physical properties of each ionization chamber of the same type and stresses the need of carrying out individual calibrations for each type of ionization chamber.This work was carried out in the framework of an IAEA Coordinated Research project (ALG 11623).


In this paper, we construct a linear differential system in both continuous time and discrete time to model HIV transmission on the population level. The main question is the determination of parameters based on the posterior information obtained from statistical analysis of the HIV population. We call these parameters dynamic constants in the sense that these constants determine the behavior of the system in various models. There is a long history of using linear or nonlinear dynamic systems to study the HIV population dynamics or other infectious diseases. Nevertheless, the question of determining the dynamic constants in the system has not received much attention. In this paper, we take some initial steps to bridge such a gap. We study the dynamic constants that appear in the linear differential system model in both continuous and discrete time. Our computations are mostly carried out in Matlab. Patients infected with Human Immunodeficiency Virus (HIV) are very likely to develop Acquired Immunodeficiency Disease Syndrome- (AIDS-) related diseases that are usually fatal if not treated with effective antiretroviral therapies. Since the discovery of HIV in 1983, an efficacious vaccine is yet to be developed to fight the deadly virus. Although highly active antiretroviral therapies (HAART) invented in mid-1990s have saved millions of lives and deterred the disease progression of those infected, HIV infection remains a public health threat. Reducing the risk of HIV transmission is of top priority.One particular challenge in HIV prevention is its long period of latency period. The average time of an HIV infected patient to become symptomatic with AIDS-related diseases can be more than 10 years  are correlated with each other. Hence each estimate T will be biased. We shall correct this and give a more robust estimate later.Now we may average all T is the least square method. We write T is Ni's and the least square solution does not change, we also avoid the pitfall that Ni and Ni+1 are correlated. We have our posterior estimates: T2, T3 can also be estimated and shall be taken into consideration when we estimate T. We shall offer one remedy that avoids this issue.Perhaps a good way to estimate Nt and Nt+1 are correlated to each other. As a remedy, we pick N1 and N6 as far from each other as possible. We observe that One of the problems with our estimate is that We compute M is quite consistent with the least square estimate. Our estimate seems to suggest that the transmission rates for N0 and N1 may be in the similar range. By [ϵ0 = ϵ1 = 0.019. We have N1 group become aware of their infection. This seems to be consistent with some of the observations in [N1 and N0 are very close. Figures TNt and Nt+1.This estimate of ange. By , assume tions in , with onN(t), suppose that N(t + 1) = TN(t) with transitional matrix T. How should one estimate the matrix T?Now we may state our problem in greater generality. Given a temporal vector T, in some sense, is very similar to the arithmetic mean of the transitional matrix T. But what makes better sense is a geometric mean. More precisely, we have to take into consideration that Tt is the transitional matrix at time t. Then a good estimate of T should be the “geometric average” of Tt. For scalars, one can define the geometric average of p1, p2,…, pn to be the nth root of ∏pi. But matrix multiplications are not commutative and one cannot define the geometric average of matrices. It remains a challenging problem to define computationally a geometric mean of Tt.As we discussed earlier, we can use least squares with the equations Tentatively, we may define the geometric mean by taking roots. For example, we may now consider We can also considerT are consistent with the least square estimate and the estimates in the previous section. Above all, all our estimates point to the same range of transmission rates for both N0 and N1.Both estimates of Now we shall compare our dynamic constant estimates in the linear differential model in continuous time and in discrete time.ϵ1 of about 4% for the N1 group, those who were aware of their HIV infection. Nevertheless, δ the rate of flow from N0 to N1 due to HIV testing turned out to be too low and ϵ0 often came out to be negative, which cannot be the case. The best results are obtained when we assume the two eigenvalues are complex. In this case δ seems to be quite low. According to the CDC report [δ is estimated at about 25%.In the continuous time model, we obtain the transmission rate is case 5γ1≅0.029,N0 and N1 population sizes, particularly in more recent years. The CDC estimates the sizes of the populations infected with HIV by back calculation. The estimates for any given year will increase as new diagnoses are obtained. HIV may go undiagnosed for up to 10 years without causing the death of the patient  . This suδ≅0.184, not too different from the CDC estimate 0.25. We see that the dynamic constants in the discrete time model are less affected by the underestimation caused by back calculation. It is also true that the relative size of susceptibles has less effect on the discrete time model than on the continuous time model.The discrete dynamics model seems to be most robust against the bias caused by back calculation and the size of the susceptibles. By ignoring the 2013 year's data, we are able to determine the transmission rates as Finally, our estimates of the transmission rates for diagnosed and undiagnosed HIV population are relatively close. This is very different from the previous estimate, where the transmission rate of the undiagnosed population is about 4 times as high as the diagnosed population . This im


EDW). An MLC‐based penumbra softener was developed in the developer mode of TrueBeam linacs to reduce dose gradients across the side border of EDWs. The movement of each leaf was individually synchronized with the movement of the dynamic Y jaw to soften the penumbra in the same manner along the entire field border, in spite of the presence of the dose gradient of the EDW. Junction homogeneity upon field misalignment for side‐matched EDWs was examined with the MV imager. The fluence inhomogeneities were reduced from about 30% per mm of shift of the field borders for the conventional EDW to about 2% per mm for the softened‐penumbra plan. The junction in a four‐field monoisocentric breast plan delivered to the Rando phantom was assessed with film. The dose inhomogeneities across the junction in the superior‐inferior direction were reduced from about 20% to 25% per mm for the conventional fields to about 5% per mm. The dose near the softened junction of the breast plan with no shifts did not deviate from the conventional plan by more than about 4%. The newly‐developed softened‐penumbra junction of EDW (and/or open) fields was shown to reduce sensitivity to misalignments without increasing complexity of the planning or delivery. This methodology needs to be adopted by the manufacturers for clinical use.Junctions of fields are known to be susceptible to developing cold or hot spots in the presence of even small geometrical misalignments. Reduction of these dose inhomogeneities can be accomplished through decreasing the dose gradients in the penumbra, but currently it cannot be done for enhanced dynamic wedges ( Similarly, a misalignment‐caused dose peak in an organ at risk (OAR) should be avoided too. The sensitivity to such misalignment can be reduced by softening the dose gradient in the penumbra region. While a steep penumbra is normally desired, it cannot be easily softened on a typical linac to smooth out the junction. Clinical consequences of setup and machine uncertainties on the dose distribution in the junction region were described, e.g., by Holupka et al.Fraas et al. utilized a match‐line physical wedge to soften the gradients in the penumbra of diverging fields forming a junction,Shackford et al. softened the dose gradient of the penumbra by dynamically moving the jaws, and implemented this technique for the open fields in craniospinal treatment.Hong et al. used overlapping IMRT‐planned regions instead of a side‐by‐side matching of diverging fields.Yom et al. employed field‐in‐field homogenization together with interfraction isocenter shifts to homogenize the dose distribution at the junction of diverging fields.Application of the feathering concept to craniospinal irradiation by using three isocenters without beam edge matching was described by Cao et al.Duan et al. described a technique of smoothing the junction between an open field and IMRT fields.Zeng et al.Garcia et al. applied the concept of stepped doses in sub‐PTVs to longitudinally adjacent PTVs treated with helical TomoTherapy.Wu et al. split the desired intensity (generated through optimization) in a large IMRT plan into two (or more) slightly overlapping intensity distributions such that the intensity in the junction area would be gradually reduced to zero in each region, and used the leaf sequence generator to obtain the leaf sequences.The techniques described above add to the complexity of the plans. Moreover, most of them cannot be applied to junctions of fields employing enhanced dynamic wedges (EDW).In this paper we describe development of an MLC‐based penumbra softener for 3DCRT that can be used concurrently with delivery of a combination of EDW and open fields. This design is an extension of the idea of a physical penumbra modifier, but we employ the MLC to dynamically modify the fluence instead of using a physical modifier. Use of the MLC instead of a physical modifier eliminates the need to mount the modifier at the gantry when softer penumbra is desired. The proposed solution was designed to restrict the magnitude of the dose dip or peak at a junction under 1 mm misalignment to no more than about 5%. This design goal was loosely based on the ICRU 50 recommendation to keep the dose in the PTV within 95% and 107% of the prescribed dose,2The softened penumbra fields and the conventional fields  were delivered in the developer mode on a Varian TrueBeam linac, version 2.0 MR1, with Millennium 120 MLC (with 0.5 cm wide leaves near the center and 1 cm wide leaves elsewhere). The instructions for moving the MLC leaves and the jaws during beam delivery, as well as instructions for the MV imager, were encoded into xml scripts using in‐house written Matlab scripts, and executed in the developer mode. The conventional clinical‐like plans were planned with Varian Eclipse, and delivered in clinical mode of the TrueBeam linac.2.A.2Δ can be achieved by moving (at constant velocity) the jaw forming the field border at the junction from x0 − Δ to x0 + Δ, where x0 is the center of the junction. This strategy does not work for side matching of EDW fields. The problem is explained in Fig. x = 0. Initially, the X jaw is retracted from the field by Δ. The X jaw starts moving into the field as the dynamic Y jaw (from EDW) begins closing, and the X jaw completes the movement at x = −Δ when the dynamic Y jaw completes its movement at y = ‘E’. The corresponding fluence profile across the x direction at y = ‘E’ is shown in Fig. y positions, and an example of a highly non‐uniform summed profile is shown in Fig. y = ‘M’. A triangular peak develops in the summed fluence because after the Y jaw passes beyond y = ‘M’, no more fluence is added to this profile. To obtain a flat summed profile at y = ‘M’, one could adjust movement of the X jaw such that its movement is completed at −Δ when the Y jaw reaches y = ‘M’, instead of when the dynamic Y jaw completes its movement, see Fig. y = ‘E’ is no longer flat, because after passing y = ‘M’, the remaining fluence is delivered only outside of the penumbra‐widening zone, see Fig. In a typical junction of static fields, reduction of sensitivity to misalignment from the nominal position (where the positions of 50% fluence coincide for both fields) can be accomplished through widening the penumbra of the individual fields. This is illustrated in Fig. 2Δ‐wide penumbra centered at x = 0 for any value of y , the portion of the X collimator at y = yi needs to move from x = Δ to −Δ, but at a modified time sequence. Movement of this portion of the X collimator needs to be completed precisely at the instant ti when the dynamic Y jaw begins to over‐shadow it. Considering ti depends on position yi, to provide independence of the penumbra width on yi, velocity, vi, of movement of each portion of the X collimator must be:To avoid non‐uniformities in the summed fluence profile as in Figs. dynamic = 10 cm and Ystatic = 0 cm, to obtain 4 cm wide penumbra at the junction at x = 0 cm. The open‐beam phase begins with all leaves (from the bank at the softened penumbra) over‐traveling the junction center by the half‐width of the penumbra, Δ, equal 2 cm, see Fig. c0, until reaching Δ beyond x = 0 cm. This means:MU0, and velocity c0 of the leaf is expressed in the units of cm/MU, not cm/s to reflect independence of the delivered MUs on the repetition rate (a convention used in the developer mode). A snapshot of the leaf positions in the middle of the open‐beam phase is shown in Fig. Δ (the start position of the entire sequence), they will not complete the movement simultaneously. As explained earlier, each section of the bendable collimator, i.e., each leaf, needs to move from x = Δ at the start of the dynamic phase to x = −Δ at the instant when the dynamic Y jaw over‐shadows the leaf. To accomplish this, the velocity ci of leaf i should be:MU0 and the dynamic Y jaw overshadows the leaf i at MUi. This is illustrated in Fig. y = −9.75 cm, −9.25 cm and −8.75 cm have completed their movement (after being over‐shadowed by the Y1 jaw) and the leaves at y > −8.75 cm continue traveling to their final destination. In both phases the X2 jaw trails 0.25 cm behind the slowest‐moving leaf in order to reduce the inter‐leaf leakage.We obtained the dynamic bending effect by employing the MLC. As in the conventional EDW,While the main concept is explained above, below are additional details:Because the width of each leaf is non‐zero, the MU when the dynamic Y jaw is over‐shadowing a given leaf is not uniquely defined. We defined such MU when the dynamic Y jaw meets the center of the leaf.The control points governing movement of the dynamic Y jaw, which are defined in the segmented treatment tables (STT) for each wedge, are not appropriate for implementation of the algorithm described above. Instead, we defined the control points at the MUs, for which the dynamic Y jaw meets the centers of the leaves, as seen in the beam's eye view (BEV). These MUs were linearly interpolated from the values in the STT.By design of the EDW, the dynamic Y jaw stops movement 0.5 cm from the stationary jaw. As the dynamic Y jaw cannot reach the center of the leaf in this 0.5 cm gap, movement of that leaf was chosen to mimic movement of the neighboring leaf (on the dynamic Y jaw side). The positions of the leaves permanently shadowed by the Y jaws are not important, and we set all leaves permanently shadowed by the Y jaws to the initial position of all leaves.For nominally static (non‐wedged) fields, the penumbra softening may be achieved by moving all leaves at a constant speed as a uniform front . This is similar to the open phase of the softened‐penumbra EDW, and similarly, the X jaw trails 0.25 cm behind the leaves.Due to the effects associated with the rounded endings of the leaves, it is necessary to add an offset to the leaf positions in order to optimize fluence homogeneity across the junctions. The method of adjusting this offset is described in section 2.B.x = 0 cm) formed by two softened‐penumbra fields to the corresponding fluence in the combined junction‐free field. The time‐integrated fluence was measured with the MV imager collecting data in the “dosimetry” mode. The imager was placed at its center position. The two image halves of the junction image were collected one after another without interrupting image acquisition to avoid uncertainties associated with background subtraction required prior to summing images. The fields corresponding to the largest available opening of the Y jaws during delivery of EDW were used: Y1 = 20 cm and Y2 = 10 cm. 100 MU were delivered for each field at 6 MV, 400 MU/min. The field size in the X direction of each half image was limited to ± 10 cm from the central axis (CAX), and the collimator was set to 270°. Open beams and EDW 60°, Y1‐in, were analyzed.The value of the offset added uniformly to all leaf positions  were simulated by moving the imager longitudinally between the first and the second half‐image. The moves of the imager were verified to be accurate within no more than 0.25 mm within the applicable range of movement. The same EDW fields were used as during the adjustment of the offset of the leaves.http://rsb.info.nih.gov/ij). To improve precision, each profile point was averaged along the junction within ±1 mm from the nominal position, and ±0.4 mm across the junction.The fluence profiles were read from the DICOM images from the MV imager using ImageJ 1.43 u  breast plan delivered to a modified Rando phantom . EBT3 film  was used to measure the dose distribution in the junction region. The phantom, with right breast attached, was placed on the breast board (tilted by 13°), and was wrapped with superflab layers to facilitate placement of film in approximately coronal plane at the depth of 1.5 cm and 3.0 cm, see Fig. The film irradiated in the phantom, and the dose calibration film pieces  were scanned 24 hrs after the irradiation with an Epson Perfection V700 Photo flat‐bed scanner . The color corrections were disabled, only the red color was analyzed, and 150 dpi resolution was selected. Film orientation with respect to the scanner bed was the same for all scans. The scanner bow‐tie effect along the scanner light source was neutralized by multiplying by a dose‐independent empirically‐determined function (an inverted Gaussian), similarly to Menegotti et al.33.A.In this section we demonstrate that the fluence in the junction of softened‐penumbra EDW fields and/or open fields closely resembles the fluence of a combined (junction‐free) conventional field. Such data validates that the bendable‐collimator design allows avoiding fluence inhomogeneities along the junction [as seen in Fig. The fluence profiles along the junction of the softened‐penumbra fields after adjustment of the leaf offset are plotted in Fig. 3.B.In this section we evaluate the design for sensitivity of the summed fluence to field misalignment, and demonstrate improvement over conventional junctions.The summed‐fields fluence profiles (collected with the MV imager) across the junction of side‐matched softened‐penumbra 60° EDWs are plotted in Fig. 3.C.In this section we examine a clinically‐relevant four‐field monoisocentric breast plan. The correspondence of the softened‐penumbra plan to the conventional plan is inspected, as well as sensitivity of the dose distribution to introduced shifts (away and towards the junction) of the fields forming the junctions. Reduction in sensitivity of the dose variations to shifting the fields is demonstrated over conventional junctions.The dose distribution of the softened‐penumbra junction does not need to be identical to the distribution in a conventional‐junction plan as long as it meets the planning objectives. Nevertheless, similarity of the two distributions is desired, as it will allow replacing the conventional plans with the softened‐penumbra plans. Similarity of the two plans  is demonstrated in Figs. The improvement in the dose uniformity upon misalignment of the fields in the junction between the nodal fields and the tangents is illustrated in Fig. 4The design described above met the design goal of keeping the dose changes within ±5% upon 1 mm misalignment of the fields forming the junction. As such, the design adds another option to minimize dosimetric consequences of geometrical misalignments of fields forming junctions. It should be noted that in general, this design goal cannot be achieved with conventional junctions, as demonstrated in section While the dose distribution across the junction of the softened‐penumbra junction is close to the distribution from a conventional plan, it is not identical. This should not be a problem clinically, as it is the plan with the softened penumbra that is to be evaluated for meeting the plan objectives, including dose heterogeneity inside the PTV within −5% and 7% of the prescribed dose, and absence of hot spots. In some cases, the softened‐penumbra plan may be actually preferred over the conventional plan, even in the absence of geometrical misalignments. This may be the case of four‐field monoisocentric breast plans, where the softened‐penumbra junction feathers out dose gradients in the interpectoral axillary nodes region located in the vicinity of the junction.Clinical implementation of the softened‐penumbra requires adaptation by the manufacturers of linacs and treatment planning software. In the clinical mode of Varian's TrueBeam v2.0 MR1, concurrent movement of the jaws and the MLC is not allowed. The dose distribution in the presence of simultaneous movement of the dynamic jaw of the softened‐penumbra EDW and the leaves of the MLC can be computed similarly as in the step‐and‐shoot method with the control points matching those in the softened‐penumbra design. Refinement is possible by introducing interpolated control points .This design relies on orthogonality of the junction plane to the skin at the beam entrance, and this limitation is shared with many other designs employing penumbra softening. This is because adding the doses from the ramped penumbras of the fields on the opposite sides of the junction yields a flat dose profile only under such orthogonality. This orthogonality requirement is approximately satisfied for four‐field monoisocentric breast plans. The anterior, the posterior and the medial tangent fields typically satisfy the orthogonality requirement, and the lateral tangent field approximately. The latter is true because the ratio of the tissue‐phantom ratios (TPRs) for the depths at the superior‐inferior coordinate varied by ±2 cm is typically no more than a few %. In any case, inspection of the computed dose distribution would allow confirming the absence of artifacts at the junction potentially caused by the design.Blocking OARs may be partially affected by the design because the leaves of the MLC need to move across the junction, and because the leaves used to soften the penumbra cannot be simultaneously used to block an OAR. In many cases, the leaves at the edge of the field may be used to block an OAR instead of softening the penumbra, without any noticeable influence on the junction. This is the case of blocking the spinal canal in the nodal fields of four‐field monoisocentric breast plans, where the spinal canal is entirely outside of the BEV of the tangent fields. There are no restrictions on blocking OARs using the other bank of the MLC , as those leaves are not employed to soften the penumbra. Regardless, the computed dose distribution should be examined to confirm the plan meets the planning objectives.The choice of ±2 cm as the width of the softening zone is not unique. Increasing the softening zone allows reducing sensitivity to misalignment of the fields at the junction even further, even though the value of ±2 cm allowed meeting the design goal. On the other hand, it is not recommended to extend the softening zone much beyond ±2 cm. This is in part because widening the penumbra results in enlarging the underdosed zones in the high‐dose portion of the penumbra and the overdosed zones in the low‐dose portion in the regions where the fields across the junction do not overlap. This phenomenon is not unique to this design, and it may also be present in other methods that soften a junction. Extending the width of the softening zone also reduces the maximum usable field length, which is reduced by the half‐width of the zone (2 cm here) from the machine limit . Longer fields are possible (up to 38 cm) for non‐half‐blocked fields, but couch rotation would be needed to maintain orthogonality of the junction plane to the skin, as well as subfields. It should be noted the usable field length is not restricted by the range of movement of the MLC carriage (15 cm on Varian linacs), because the leaves move only 4 cm during the sequence.To eliminate the residual inter‐leaf leakage (not blocked by the trailing X jaw) in the open‐beam phase of the softened‐penumbra EDW or softened‐penumbra nominally static fields, we considered using the X jaw instead of the MLC. Unfortunately, combining such fields into junctions introduced additional artifacts due to differences in the shape of the dose penumbra of a square jaw and round‐ended leaves of the MLC.While there is a current trend to choose IMRT or VMAT over 3DCRT, there are situations when inverse planning techniques are not practical, and junctions of conventional EDWs and open fields are used. In particular, IMRT will suffer from uncertainties in the regions where the PTV is narrow in the BEV, and the instantaneous leaf gaps are small, in which case accuracy of modeling the rounded‐leaf ends in Eclipse is reduced. Also, due to the presence of the isocenter walk upon gantry or collimator rotation, combining IMRT fields across a junction may be susceptible to dose spikes/dips at the junction when different gantry and/or collimator angles are used for the fields across the junction. Use of VMAT for some sites is arguably not ideal, e.g., for breast. This is because of unnecessary irradiation of the lung from the beam exit when the beam incidence is approximately normal to the skin.It may be argued that fractionation averages junction uncertainties, which eliminates a need for improving junction quality. This is not always true, because a systematic misalignment will not average out. In addition, there is a growing tendency to reduce the number of fractions, and consequently diminish the averaging effects from the process.Compensation for organ movement can lead to additional uncertainties at the junctions. This is the case for the junction in four‐field monoisocentric breast plans delivered during deep inhalation breath hold (DIBH). The reason is the nodal fields are delivered on a different breath hold than the tangents, and for practical reasons, the chest position  is only held within a range of positions, not at the same position on every breath. The softened‐penumbra design should reduce uncertainties related to organ‐movement.Softened penumbra field borders might also be considered when disease progresses, and a larger PTV might be needed in the future. This would happen when treating a breast as tangents, with a possibility of treating the axillary nodes in the future: the superior borders of the tangents would be softened out initially, and the nodal fields with softened inferior borders would be added in the future when required. Half‐body irradiation could also benefit from this approach through reduction of junction artifacts upon matching the opposite body half in the future. Ideally, the fields would be delivered under extended source‐to‐skin distance (SSD) with rotated couch to keep the (future) junction in the axial plane, and subfields would be employed to even out dose gradients caused by variations of the SSD. While this approach does not require use of wedges, the penumbra softener developed in this project allows delivery of open fields too.The softened‐penumbra technique may be also useful in matching long fields employing hinged wedge pairs. A hinged wedge pair offers better control of the dose homogeneity at depth over a single beam or a parallel‐opposed pair, and clinical applications could include leg sarcoma or spinal canal. The softened‐penumbra fields for treatment of the spinal canal could be also matched to the brain fields  for complete cranio‐spinal treatment with the junction(s) practically insensitive to misalignments. This would be similar to the solution of Duan et al.,Softened‐penumbra fields might also improve junction quality for junctions that are not geometrically matched, e.g., when large fields are delivered without couch rotation, and the fields are only matched at a single depth, e.g., at the skin.5A prototype penumbra softener was developed to reduce misalignment‐caused dose heterogeneities at junctions of a side border of EDWs and/or open fields in 3DCRT. This penumbra softener employs the MLC, such that the movement of the leaves is synchronized with movement of the dynamic Y jaw of the EDW. Performance was evaluated in the four field monoisocentric breast plan delivered to a Rando phantom, and approximately four‐fold reduction of the dose dips or spikes in the junction was observed when compared to the plan with the conventional junction. The time required for planning and complexity of the plan with the softened penumbra junction should be similar as for the conventional‐junction plan. The system requires adoption by the manufacturers for clinical applications.A provisional patent for the design was filed with USPTO.


In research evaluation of single researchers, the assessment of paper and journal impact is of interest. High journal impact reflects the ability of researchers to convince strict reviewers, and high paper impact reflects the usefulness of papers for future research. In many bibliometric studies, metrics for journal and paper impact are separately presented. In this paper, we introduce two graph types, which combine both metrics in a single graph. The graphs can be used in research evaluation to visualize the performance of single researchers comprehensively. Science or Nature, or subject-specific journals, like Angewandte Chemie—International Edition. Since the impact of the journals depends on their reputation (besides other factors), the ability to publish papers in high-impact journals reflects one aspect of high performance. Several years subsequent to the publication of a manuscript in a journal (at least 3 years), the citation impact of the published manuscript can be determined. Citations are seen as a proxy of quality, which reflects at least impact . Thus, high citations for the researcher’s papers indicate his or her ability to publish influential research. This is a second success, which can be reached by researchers.Publication success of single researchers can be assessed bibliometrically on the paper and journal basis. Both perspectives provide different insights. Researchers submit their manuscripts to journals and these journals can be high-impact or low-impact journals. Thus, it is a first success of researchers if they are able to get a high share of their manuscripts accepted in high-impact journals. For the publication in high impact journals, very skeptical reviewers and editors have to be convinced Bornmann . High-imIn research evaluation of single researchers, the assessment of both events is interesting. High journal impact reflects the ability to convince strict reviewers and editors, and high paper impact reflects the usefulness of papers for future research /n * 100) is frequently used for the calculation of percentiles, whereby n is the number of papers in the set and i is the rank position of the papers in the set (concerning their citations). In bibliometrics, the set of papers is defined field- and time-specific, e.g., by using Web of Science  subject categories and publication years. The corresponding citation percentile for each paper is a field- and time-normalized impact value, which can be used for cross-field comparisons.Percentiles have been introduced in bibliometrics to overcome a certain problem with frequently used citation indicators: the indicators are based on the arithmetic average of citation counts. However, since, as a rule, the distribution of citations across publications is skewed, the arithmetic average should not be used with raw citation data. The citation percentile of a single paper is an impact value below which a certain share of papers fall  between 2004 and 2013; researcher 2 has published 427 papers between 1997 and 2013. Citations were counted until the end of 2016 for calculation of Hazen percentiles. For field classification, we used the WoS subject categories. In the case of multiple assignments of papers to subject categories, the average value of the percentile in each subject category was used to obtain a paper-based percentile value (Haunschild and Bornmann Table k − rj + 1)/k * 100) where rj is the descending rank of journal j in its subject category and k is the number of journals in the category. In contrast to the usual Journal Impact Factor  as journal percentiles. By using both percentiles for a publication set of a single researcher, we have similar field-normalized metrics available, which can be used for the comparison of two events: the success of publishing in journals with a high reputation and the success of receiving high citation counts. If average percentiles are reported in the following, these averages are medians.We provide a Stata command (babibplot.ado) and an R package (BibPlots), which produce the graphs proposed in this study. Both can be found in Statistical Software Components (SSC) Archive (in the case of Stata) and Comprehensive R Archive Network , respectively.Bornmann and Marx , b introFigure nr) and column (nc) as well as quadrant (nq) of the scatter plot is labeled with the number and percentage of data points in the corresponding section, e.g., nr1 = 90; 91% for 90 papers in row 1 of researcher 1. The values of nc1 correspond to the number and proportion of papers belonging to the 50% most frequently cited papers in the corresponding subject categories and publication years. The red squares show the average value (median) of all data points in each quadrant. The results in Fig. Although the annual ability to publish high impact papers and papers in high impact journals can be seen in the beamplots of both researchers, the connection between the two is lost for the years where multiple papers with different paper impact were published. Thus, we propose the possibility to keep the connection between paper and journal percentiles for each data point in a scatter plot, as shown in Fig. Figure Often, one is interested in identifying the papers which belong to the 10% most frequently cited papers in the corresponding subject categories and publication years . We think that the plots also allow interesting insights into the publication and citation profiles of single scientists and can be used for research evaluation purposes. The plots can be applied to inspect the amount of papers in a set, which—more or less—agree in a high or low paper and journal impact. This is the average dimension of the plot. The other dimension focusses on the differences between paper and journal impact. Are there many papers with large differences between both metrics? Is the researcher more able to publish in high-impact journals or papers with high impact? If we use journal percentiles as an expected value, which can be contrasted with paper percentiles, a higher paper percentile demonstrates that the paper received more impact than could be expected on the basis of the publishing journal.nq1 and nq4). High numbers and proportions of data points in quadrants 2 and 3  indicate a low impact in general. The differences between nq1 and nq4 as well as nq2 and nq3 indicate if the papers show a higher journal or higher paper impact. With the categorization of journal and citation impact into four quadrants, the plots follow approaches in bibliometrics, such as the Characteristics Scores and Scales (CSS) method (Glänzel et al. In the scatter and difference against mean plots, which we present in this study, paper and journal impact are categorized into four quadrants. A high general impact of the papers in the set is indicated by high numbers and proportions of data points in quadrants 1 and 4 (i.e., high values of By exploring paper impact and journal impact using combined plots, the user should be aware that both perspectives on researchers’ performance are frequently related. Many studies have investigated the correlation between journal metrics and citation impact on the single paper level. Overviews of these studies can be found in Bornmann and Daniel , Onodera


For an individual with tetraplegia assistive robotic arms provide a potentially invaluable opportunity for rehabilitation. However, there is a lack of available control methods to allow these individuals to fully control the assistive arms.Here we show that it is possible for an individual with tetraplegia to use the tongue to fully control all 14 movements of an assistive robotic arm in a three dimensional space using a wireless intraoral control system, thus allowing for numerous activities of daily living. We developed a tongue-based robotic control method incorporating a multi-sensor inductive tongue interface. One abled-bodied individual and one individual with tetraplegia performed a proof of concept study by controlling the robot with their tongue using direct actuator control and endpoint control, respectively.After 30 min of training, the able-bodied experimental participant tongue controlled the assistive robot to pick up a roll of tape in 80% of the attempts. Further, the individual with tetraplegia succeeded in fully tongue controlling the assistive robot to reach for and touch a roll of tape in 100% of the attempts and to pick up the roll in 50% of the attempts. Furthermore, she controlled the robot to grasp a bottle of water and pour its contents into a cup; her first functional action in 19 years.To our knowledge, this is the first time that an individual with tetraplegia has been able to fully control an assistive robotic arm using a wireless intraoral tongue interface. The tongue interface used to control the robot is currently available for control of computers and of powered wheelchairs, and the robot employed in this study is also commercially available. Therefore, the presented results may translate into available solutions within reasonable time. Advanced assistive robotic arms have been developed to support the daily living of disabled individuals, –6 but inTherefore, we developed a new assistive robotic control method for individuals with C-level spinal cord injury, who usually have preserved full sensory-motor control of their tongue.The tongue was used for volitional input to an assistive robotic arm in order to provide full control of the robots 12 possible movements and of its three-finger gripper.One 37-year-old able-bodied female, E1, performed direct actuator control. This participant had previously used the tongue control system to control a computer , 17 and One 64-year-old female, E2, with tetraplegia performed endpoint robotic control. Her tetraplegia was due to a spinal cord tumour at level C1-C2 resulting in SCI  19 years prior to the current study. Her SCI was incomplete, but she was unable to move her extremities and trunk except from slight non-functional motions of her fingers.This individual participated in earlier experiments and she was pierced 6 years ago at Aalborg University Hospital; due to comorbidity of these individuals, their piercings were performed by health professionals, ie. a dentist and an otolaryngology specialist, and the she was admitted for 24-h in the hospital for observation of swelling and bleeding of the tongue . The tonThe tongue-robot interface developed in this study was based on the inductive tongue control system, which has previously been developed for control of computers , 12, 17,The inductive tongue control system consists of two main parts: a tongue interface resembling an intraoral dental appliance mounted at the hard palate Fig.  and an eIn order to employ the inductive tongue-computer interface for robotic control several changes of the iTongue system were implemented:Firstly, direct robotic control of the 7 degrees of freedoms of the robot with the three-finger gripper required the use of 14 out of the 18 sensors of the inductive tongue-computer interface as single buttons providing a continuous control signal as long as the sensors were activated. Therefore, a robotic keypad configuration was implemented, which employed sensors from both the keypad area and the mouse pad area of the tongue interface Fig. . FurtherThe central unit of the iTongue system was further modified to transmit all received sensor data to the COM port of the computer in addition to the emulated keyboard characters. This included amplitudes of all sensor signals and was implemented through a software program, which in addition provided feedback to the user showing which sensor was activated  completion time and number of issued command were 70.1 ± 15.3 s and 6.0 ± 1.5 respectively (Table At the end of the experiment, E2 decided to pick up a bottle of water with the assistive robotic arm and to pour its contents into a plastic cup. She was successful in performing this action in her first attempt Fig. . She expThe average time spent between two commands is shown in Fig. To our knowledge, this study is the first to demonstrate that it is possible for an individual with tetraplegia to use the tongue intraorally to fully control all 14 motions of a robotic arm with a mobility comparable to the human arm. This facilitates the use of any task the robotic arm can perform including activities of daily living. During use, the tongue interface is practically invisible due to its intraoral location and thus aesthetically acceptable for user.It is expected that further training and increase of robotic velocity will improve the completion time of the different tasks performed with the robot. We suggest that future implementations of the system facilitate a more joystick-like control by incorporating the mousepad of the tongue control system since such control may be more useful  and a coSince both the robotic arm and a similar version of the inductive tongue control system as deployed in this study are currently available for individuals with tetraplegia, the results of this study may very well translate into an available solution within reasonable time.The presented method provides a solution in which a paralyzed individual can control a computer, , 12, 17


The left atrial appendage (LAA) has received increasing attention in recent years because of thrombi formation in patients with atrial fibrillation, which increases the risk of stroke. In patients who have contraindications for long-term oral anticoagulation therapy, percutaneous procedures are used to occlude the LAA and there are now several devices available for implantation, both endocardially and epicardially. Despite the high-resolution imaging techniques on hand today, limitations remain in providing information about wall thickness and neighboring structures; therefore, in-depth knowledge of the normal atrial anatomy is mandatory when considering such interventions. Here, the anatomy of the right and left atria is reviewed with relevance to interventional procedures required for LAA occlusion. The components of the atria, particularly the LAA as well as the atrial septum, are described with emphasis on their spatial relationships to neighboring cardiac and extracardiac structures. Sound knowledge of the atrial anatomy including endocardial and epicardial aspects is necessary. This will help interventionists take full advantage of imaging techniques when assessing the suitability of the LAA anatomy for closure, selecting the optimal device types and sizes, and guiding the LAA closure procedure, thereby reducing potential complications and increasing procedural success. The left atrial appendage (LAA) is a highly complex structure that has received increasing attention in recent years, primarily because over 90% of thrombi in patients with atrial fibrillation (AF) occur here , which iPreviously, the LAA was ligated, stapled, or amputated during elective open-heart surgery. Surgical techniques continue to evolve with the type of sutures used and the delivery of clips through sternotomy or thoracotomy. In recent years, however, percutaneous procedures are increasingly used and there are now several devices available for implantation within the LAA to occlude its orifice. In addition, there is an epicardial LAA exclusion system that enables percutaneous suture ligation of the LAA via combined pericardial and transseptal access , 6. TherWhen delivering catheters into the heart, the first chamber encountered is the right atrium. The atrium is best considered in terms of three components: the venous part, the appendage, and the vestibule Fig. a. The reThe eustachian valve, guarding the entrance of the inferior caval vein, is a variably developed flap. Usually it is a triangular flap of fibrous or fibromuscular tissue that inserts medially to the eustachian ridge (or sinus septum), which is the border between the oval fossa and the coronary sinus. Occasionally it is a filigreed mesh, or a perforated flap, which can be so extensive as to stretch across the superior caval vein orifice and is described as Chiari’s network . The freand The atrial septum separates the atrial chambers Figs. a and 2. In reality, the site of the true septum is the area of the thin flap valve of the oval fossa and its apposition to the muscular rim (limbus) that surrounds it Fig. b, c. DurThe superior rim of the fossa is then the infolded wall between the superior caval vein and the right pulmonary veins Fig. b. SandwiThe ideal site for crossing the septum to safely reach the left atrium is through the thin valve of the fossa. Although generally expected to be in the middle of the septal wall, the location and size of the oval fossa varies from patient to patient . FurtherEven with a transseptal puncture through the fossa valve, the relationship of the fossa to the superior wall or roof of the left atrium, the orifices of the pulmonary veins, and the mitral valve are important considerations for interventional procedures, especially in LAA closure procedure where the entrance of the needle should be directed toward the plane of the LAA ostium. In cases where the fossa is situated more superiorly than usual, the puncture site could lead to the atrial roof. The location is comparable to crossing at the site of a PFO, although the latter may also direct the catheter toward the anterosuperior wall of the left atrium Fig. d, affectFollowing the direction of blood flow inside the left atrium, the atrial chamber begins at the pulmonary veno-atrial junctions and terminates at the fibro-fatty tissue plane that marks the atrioventricular junction at the mitral orifice. A distinctive appendage, considerably smaller than its counterpart on the right side, extends from the aforementioned main body of the atrium Fig. . Apart fThe posterior part of the left atrium receiving the pulmonary veins is its venous component. Mainly, the wall is smooth on the endocardial surface, without a clear distinction between venous and atrial walls, especially where the terminal parts of the veins are funnel-shaped. One of the most important aspects is that the posterior left atrial wall is not uniform in thickness. The transmural musculature of the wall shows changes in orientation of the myocardial strands; usually the areas of abrupt change are accompanied by a change in wall thickness .Viewed from within the atrial cavity, the endocardial surface has the appearance of ridges in between the superior and inferior venous orifices. In addition, there is a ridge-like structure between the entrance of the left superior pulmonary vein and the os of the LAA, first described by Keith in 1907  and todaPassing across the subepicardium of the anterior left atrial wall is Bachmann’s bundle, also known as the interauricular band, which is the most prominent muscular interatrial bridge . MyocardIn most hearts the appendage extends from between the anterior and lateral walls of the left atrium and its tip is directed anterosuperiorly, overlapping the left border of the right ventricular outflow tract or the pulmonary trunk and the main stem of the left coronary or the circumflex artery Fig. . It is nAs shown in Fig. Patients with chronic AF frequently have LAA remodeling in which there is dilation, stretching, and reduction in pectinate muscle volume, as well as endocardial fibroelastosis . BecausePrevious studies , 32 showRamondo et al. stated that the diameter of the ostium is in relation to the length of the LAA, which is a crucial measurement in order to permit complete deployment of the device into the LAA . AnotherAside from the paper-like thickness of the LAA wall itself, the morphology of the atrial wall in the proximity of the LAA orifice is also an important consideration when deploying catheters in the left atrium. In almost 50% of heart specimens there are pits or troughs that tend to occur in isolation or in clusters located on the anterolateral and lateral atrial wall . InterveType I, with the LAA ostium (neck) cephalad and anterior to the left superior pulmonary vein and usually separated by a wide lateral ridge (LLR)Type II, when the most posterior part of the ostium is very close to the lateral ridge, which is thinner and more marked than in type I and demarcates a well-defined limbusType III in which the ostium is at the level of the left inferior pulmonary vein Fig. .Finally, we should not forget that the entrances of the pulmonary veins, the atrial appendage, and part of the left atrial body are in close vicinity to important structures surrounding the heart Figs.  and 4. TCMR or CT studies before intervention may help to classify the different types of ostium (or neck) and therefore may be useful for selecting the devices and for the site in which to perform the transseptal puncture. Type I is probably the most suitable for device occlusion since the broad LLR could favor a stable implant as well as having more distance from the pulmonary vein. By contrast, types II and III could present with an extremely narrow and pointed LLR, in which case it could be more challenging to achieve a balance between device stability without risking pulmonary venous obstruction if a larger device is deployed .Fig. 5ClApart from the left pulmonary veins in proximity to the ostium of the appendage, the mitral valve is also in its vicinity in hearts where there is a narrow vestibule. An oversized device may impinge on the mitral orifice.Moreover, it is also important to consider neighboring outer structures whether attempting to close the LAA ostium from within the atrium or from the pericardial space. Owing to its slightly flattened shape, the lower surface of the LAA usually overlies the summit of the left ventricle while the upper surface is beneath the fibrous pericardium. A previous study  showed tA previous anatomical study on the course of the phrenic nerves has demonstrated that the left phrenic nerve runs along the pericardium overlying the LAA . This nePercutaneous LAA closure is a relatively new, but evolving treatment strategy to prevent embolic events in patients suffering with nonvalvular AF. Sound knowledge of atrial anatomy from both endocardial and epicardial aspects is necessary. This knowledge will help interventionists take advantage of imaging techniques when assessing the suitability of the LAA anatomy for the closure, for selecting the optimal device types and sizes, and for guiding the LAA closure procedure in order to reduce complications and increase procedural success.


Due to limitations of our treatment planning system in calculating dose for this treatment, we have developed a fast Monte Carlo code to calculate dose distributions within the patient. The algorithm is written in C and uses voxel density information from CT images to calculate dose in heterogeneous media. To test the algorithm, film‐based dose measurements were made separately in a simple water phantom with a high‐density insert and a RANDO phantom and then compared to doses calculated by the Monte Carlo algorithm. In addition, a separate simulation in GEANT4 was run for the RANDO phantom and compared to both film and the in‐house simulation. All results were analyzed using RIT113 film analysis software. Simulations in the water phantom accurately predict the depth of maximum dose in the phantom at 0.5 cm. The measured PDD along the central axis of the beam closely matches the PDD generated from the Monte Carlo code, deviating on average by only 3% along the depth of the water phantom. Dose measured at planes inside the high‐density insert had a mean difference of 4.9% on cross‐profile measurement. In the RANDO phantom, gamma pass rates vary between 91% and 99% at 3 mm, 3%, and were >99% at 5 mm, 5% for the four film planes measured. Profiles taken across the film and both simulations resulted in mean relative differences of <2% for all profiles in each slice measured. The Monte Carlo algorithm presented here is potentially a viable method for calculating dose distributions delivered in TBI treatments at our center. While not yet refined enough to be the primary method of treatment planning, the algorithm at its current resolution determines the dose distribution for one patient within a few hours, and provides clinically useful information in planning TBI. With appropriate optimization, the Monte Carlo method presented here could potentially be implemented as a first‐line treatment planning option for  60Co TBI.Our institution delivers TBI using a modified Theratron 780 PACS number: 87.10.Rt I. 60Co unit which has been fitted with a custom collimator and treatment table.In a manner similar to other institutions, our center currently offers total body irradiation (TBI) treatment using a Theratron 780 50 cm×200 cm field sizes — prevents the use of Eclipse for dose calculation for a typical TBI treatment as delivered at our center. While other, more flexible, treatment planning systems will allow for extended SSD calculations, it is clinically undesirable to maintain multiple commercial treatment planning systems for individual use cases. As such, we have investigated developing an in‐house Monte Carlo‐based solution for TBI dose calculation.Our current commercial treatment planning software, Varian's Eclipse 8.9, cannot be used in this application. The calculation algorithms currently available in Eclipse 8.9 are based on beam models derived from data measured at the treatment machine. As Eclipse is designed around Varian's linear accelerators, the calculation algorithms have been optimized for treatment geometries associated with their accelerators. The nonstandard geometry presented by our TBI unit — Monte Carlo frameworks like GEANT4 and EGS4 have been used successfully in dose calculations at the photon energy in question, however complete treatment planning in this way has been limited by long calculation times. 60Co unit. We will describe the algorithm and how it has been implemented and the assumptions that have been made. We will then present a comparison of our fast Monte Carlo simulations to: a) measured data for the irradiation of a custom water phantom and an anthropomorphic RANDO phantom, and b) an equivalent simulation performed in GEANT4. Finally, we will discuss the potential of this algorithm and the work that still needs to be completed for the algorithm to be employed in the clinic.In this work we have created a simple Monte Carlo algorithm for the purpose of TBI planning on our II. 60Co TBI treatments should allow for a significant reduction in physics processes and resolution of data tables in the code, while still achieving results more informative than hand calculation of point doses.The overall goal of constructing the Monte Carlo code is to provide a lightweight framework that takes advantage of the specific approximations that can be made in dose calculation due to the clinical situation in question. The pseudo‐monoenergetic source and less restrictive requirements of calculation accuracy present in A. 60Co source to the floor is 195.4 cm, and the average distance to patient midplane is ∼175 cm. The patient is set up such that the midpoint of the body aligns with the central axis of the treatment unit. The top of the table on which the patient lies is 6.0 cm from the floor, and the table itself is oriented 90° from the potential axis of rotation of the gantry . A diagram of the treatment setup is presented in All patients treated on the TBI unit are set up on a custom designed table that sits on the floor of the treatment room. The distance from the  60Co decays with 0.9988 probability to a particular excited metastable state of  60Ni through beta decay. This excited  60Ni nucleus further decays to its ground state by emitting two gamma rays, each with the same relative abundance differing by only 1 in 1000. The energies of the two gamma rays are 1.17 MeV and 1.33 MeV, respectively. Due to the equal emission of the two gammas, the effective gamma emission of  60Co is often approximated by employing an average gamma energy of 1.25 MeV. For the purposes of simulation, the small gap in energy between the two gammas makes it computationally convenient to use this single energy approximation and average energy of 1.25 MeV. 60Co unit. The physics processes that can occur during simulation are location‐dependent within the simulation space. In the flattening filter contributions of Rayleigh scattering, photoelectric effect and Compton scattering are included. However, in the patient Rayleigh scattering is excluded due to very low interaction probability in tissue at the initial photon energy. The contribution of pair production is ignored everywhere due to low photon energy.The custom Monte Carlo code was written in C to simulate energy deposition by 1.25 MeV photons over a broad field defined by our modified collimator on the B. 60Co source is 2.0 cm. It is located in a lead‐lined chamber thick enough to ignore contributions from head leakage due to gammas not emitted in line with primary collimation. There is a lead flattening filter composed of six round plates with a thickness of 0.3175 cm each. The diameters of the round plates are 7.62, 12.7, 17.8, 22.9, 27.9, and 33.0 cm, respectively. The distance from the source to the bottom of the filter is 25.4 cm. The length and width of the filter is 33.0 cm and 14.0 cm, respectively.The radius of the C.The simulation begins by randomly selecting an initial momentum for the photon within the limits of collimation. The photon is transported through the lead filter under the approximations that: i) only zero and single scatter photons contribute dose to the patient, and ii) no electrons generated in the filter contribute dose. The second approximation is made due to a thin sheet of acrylic present during treatment that reduces electron contamination in the treatment beam. The effect of this piece of acrylic is assumed to be limited to the electron contamination and overall beam intensity, and is therefore not included explicitly in the simulation.t is the thickness of the filter. The physics processes included in the flattening filter transport are the photoelectric effect, Compton scattering, and Rayleigh scattering. Each component is sampled based on its weight in the total attenuation coefficient.The probability of collision of an individual gamma with the flattening filter is sampled according to the transmission through the filter:hν is the energy of the incoming photon, hv′ is the energy of the outgoing photon, θ is the angle of deflection, and r0 is the classical electron radius given by:For photoelectric process in the flattening filter, the photon is absorbed and the emitted electron is dismissed. For Compton scattering, the recoil electron is dismissed, and the photon scattering angle is sampled according to the Compton differential cross sectionû is the incoming directional unit vector, and Ω is the rotation matrix given by:The azimuthal angle Ψ is sampled uniformly from 0° and 360°. The unit vector of the direction of the scattered photon is given by:The energy of the scattered photon is determined by:F(q) is the Hubbell's form factor and q is the momentum transfer given by:For Rayleigh scattering, there is no recoil electron and the photon loses no energy, but a change in the direction of the photon occurs. The scattering angle is sampled according to the Rayleigh differential cross section:The azimuthal angle is sampled uniformly from 0° and 360°. The new directional unit vector is determined by rotation matrix as described in All photons through the filter will be projected onto the entry plane of patient. If the incident point is outside the entry window of the patient, the photon is dismissed. This approximation ignores interactions that could take place in air between the patient and the flattening filter.D.2.54×2.54×5 mm in X, Y, and Z directions, respectively, where Z is the CT slice thickness.If a given photon is transported through the flattening filter and remains within the patient window, transport within the patient begins. In order for the Monte Carlo dose algorithm to be clinically useful, it must be able to simulate the transport of photons and electrons based on CT image sets collected during patient treatment simulation. To this end, a separate procedure was created in MATLAB  to read CT image sets in DICOM format and create binary data files containing the voxelized information in a format easily inputted into the Monte Carlo algorithm. The translated data file is then read into an array containing an element for each voxel in the original CT dataset. For a given simulation, the patient is represented as lying in a digitized box with the same geometry as the CT image. Each element of the digitized box preserves the Hounsfield's unit (HU) and spatial location of the original CT voxel. To preserve the geometry of treatment, the top of the table on which the patient lies in the CT image set is set 6.0 cm higher than the floor in the simulation to correspond to the table top in the treatment room. For the results presented here, the voxel dimensions were In order to determine the transport of a given particle, the density and material type must be known. While this is difficult to obtain directly from HU values in a CT image set, a set of empirical assumptions can be used as a good approximation. As in standard treatment planning systems, the density of a given voxel can be related to the HU values given in the CT image set.Once a density is assigned, the type of material can be selected based on that density.The values of mass attenuation coefficients for different materials are taken from Appendix D2 of the work by Attix.h is the step size of particle transportation (chosen as 1 mm for this work), and λj is the mean free path of the photon in the jth voxel. Each photon is assigned a surviving probability by choosing a random number between 0 and 1 using a Mersenne twister pseudo‐random number generator.The distance of travel before interaction is determined by accumulating transmission probabilities along the path in tissue:If the collision is determined to be a photoelectric process, the photon is completely absorbed and the emitted electron is assigned the same energy as the photon. Due to the low probability of a photoelectric process occurring at the initial 1.25 MeV energy, the majority of photoelectric interactions will take place with scattered photons at much lower energies. As such, these electrons will be ejected with low energies and short ranges. Due to the coarse dose matrix used in our simulation, the energy of these electrons is low enough to confine the electron travel to a single voxel. As an overall approximation in the code, we therefore ignore the angular distribution of the ejected electrons. As a further approximation, the energy loss due to binding energy is ignored.For Compton scattering, the scattering angle and energy are calculated based on The azimuthal angle of recoil electron is opposite to that of the photon:The direction of recoil electron is determined by rotation matrix in h is the step size (chosen to be 1 mm for this work). The tables of mass collision stopping powers are taken from Appendix E of the work by Attix.Due to the low energy of the majority of recoil electrons, transport is carried out using the continuously slowing down approximation (CSDA) without angular scatter. As such, the electron path is followed in a straight line with continuous energy deposition until all kinetic energy is deposited. Using the CSDA approximation without considering angular scatter obviates the computational cost of sampling a multiple‐scattering distribution and significantly speeds up the simulation. The energy deposition from recoil electrons is calculated based on the electron mass collision stopping powers:ΔE is the total energy deposited in that voxel, ρ is the density of that voxel, and V is the volume of each voxel. The dose calculated in this way is representative of the dose deposited in the voxel material, but not representative of the way dose is measured and defined in clinic . To convert to dose to water, stopping‐power ratios between water and the voxel material are used as in Bragg‐Gray cavity theory.The energy cutoff for photon and electron transport is set to 10 keV. When the kinetic energy of a photon or an electron is less than the energy cutoff, the particles are dismissed and their entire energy is deposited in the local voxel. The final dose distribution in voxel  is determined by:E.The method used for sampling is essentially a numerical inversion of the discretized cross sections. At a resolution of 1°, each differential cross section is evaluated at a particular angle, summed with all values of the differential cross section between that angle and 0°, and tabled. All tabled values are then normalized to the total cross section. At a sampling resolution of 1000 total bins between , each angle is then tabled with a frequency in 1000 equal to its probability of occurring as determined through the cross‐sectional tabling. This is repeated for every photon energy, from 0 keV to 1250 keV, at a resolution of 10 keV. In this way, a number sampled from a uniform distribution multiplied by 1000 and rounded to the nearest integer represents an index in the table of outgoing angles. No comparison is needed for each sampled event, only a selection of a pretabled value.F.∼2 billion. The 3‐hour time limit was determined through an analysis of our current clinical workflow and represents a realistic time frame in which simulation could be useful in our TBI planning process. However, noise in the simulation can be greatly reduced by increasing the number of events through increasing calculation times, and the overall allowed calculation time could be adjusted on a case‐by‐case basis.The simulation should provide useful dose distribution information on a clinically realistic timescale. For our current implementation, we limit the time for simulation to 3 hours using a Mac Pro workstation  with a Quad‐Core Intel Xenon processor  running at a clock speed of 3.2 GHz. The four core processor has 256 KB of L2 Cache per core, and a total of 8 MB of L3 Cache. The physical memory available for computation is 24 GB. In this time period at the algorithm's current state of optimization, the number of initiated gamma events is G.31.8 cm×48.4 cm×30 cm. A photo of the phantom is shown in Initial validation of the simulation was performed by percent depth dose (PDD) and depth‐dose profile measurements in a water phantom. The dimensions of the water phantom are H.(>10%), the gamma analysis is evaluated at a distance to agreement of both 3 mm and 5 mm and separately at dose deviations of 3% and 5% of maximum dose.Further validation of the simulation was undertaken by comparing simulation results to measured profiles in an anthropomorphic RANDO phantom . The RANDO phantom is comprised of 2.5 cm axial sections and is constructed of both biological and nonbiological tissue equivalent materials. This phantom was used to provide a detailed mapping of a dose distribution as similar as possible to that expected in an actual patient. For the RANDO phantom, EBT2 film was used to measure dose between the axial slices in the phantom. As shown in I. 60Co source. The physics processes included in the simulation included only G4PhotoElectricEffect and G4ComptonScattering for gammas, and G4eMultipleScattering, G4eIonisation, and G4eBremsstrahlung for electrons. The simulation was run for the same number of events as the in‐house simulation — 2 billion —and dose maps were exported to MATLAB for analysis and conversion to DICOM‐RT format readable by the RIT113V5.2 Film Analysis software.The GEANT4 simulation was performed using computer, image set, and geometry as the fast Monte Carlo simulation, including the flattening filter. The simulation was built upon the DICOM example provided with the GEANT4 framework.ɛ=1/(δ2T), where T is the total simulation time and δ2 is the variance, was estimated by recording the dose in an identical 2 cm×2 cm×2 cm box sitting approximately 5 cm deep in the abdomen region of the phantom. To estimate variance in a reasonable amount of time, ten simulations of only 20 million incident photons were run for each code, and the variance in the dose collection region was recorded  to determine a rough estimate of the efficiencies of both codes.The efficiency of both simulations, defined as III.Dmax to a depth of 20 cm, almost entirely due to noise. When the aluminum bars with thickness of 9.5 mm are introduced into the simulation, the dose measured at the plane between two aluminum bars at a depth of 3.0 cm in the water had a mean relative difference of 4.9% on cross‐profile comparison, again thought to be mostly due to noise. This profile through the aluminum is shown in The PDD profiles calculated using the Monte Carlo simulations in the water phantom compared favorably to those measured in the phantom. As shown in >99%. For the RANDO phantom, measured films were scanned directly into RIT113 film analysis software. The dose distributions for the four simulated planes are displayed in <2%. The in‐house and GEANT4 simulations agree slightly better with each other than either does with film measurements.The gamma comparison of the simulation results to those obtained using GEANT4 are presented in ∼30 shorter than that of the GEANT4 code for the same number of initial events. However, the variance of the in‐house code as estimated with the regional dose measurements was a factor of ~ three times larger. Thus the efficiency of the in‐house code was only ~ ten times that of the GEANT4 code despite its much shorter computational time.The simulation time for the in‐house code was a factor of IV.We believe that the comparisons between measured and simulated dose distributions illustrate that the fast Monte Carlo algorithm developed here is capable of calculating dose in heterogeneous tissue. While the simulation clearly requires further refinement, the approximations in the evaluation of the physical interactions that have been used to increase the speed of calculation appear to provide an acceptable physics framework for calculating dose for this particular TBI application.Much work still needs to be done before any clinical implementation could be considered. Electron transport as implemented in this code leaves out many corrections applied in other codes for energy‐straggling, path length deviation, lateral scatter, and interface effects.∼30 times faster than an equivalent voxel‐based calculation GEANT4, while the efficiency is estimated to improve by only a factor of ∼10. Despite seemingly similar levels of noise throughout the simulations at the number of events used, the computational time decrease is only a limited success since GEANT4 is not optimized for this type of computational situation, and there are many other Monte Carlo codes much faster for voxelized dose calculations.2.54 mm×2.54 mm×5 mm; however, this resolution may not be necessary for our particular application. Larger voxel dimensions of 5.08 mm×5.08 mm×5 mm could provide a suitable resolution for the determination of whole body dose.The magnitude of the fluctuations in the calculated doses is directly linked to the limited number of events. This can be improved by simply increasing the number of simulated gamma rays, which is an available option only when the calculation speed has been optimized. The current calculation speeds are A common step to reduce simulation time is to separate the simulation of the treatment head from that of the patient dose. A version of that approach could be used in the future. However, the reason for including it in the manner here is to provide a methodology for including patient‐specific lung blocking in the calculation. In our treatment setup lung blocks, if needed, sit only a few tens of centimeters above the patient and are thus not part of the treatment head. While further improvements of the code can include the use of a presimulated phase space for the treatment head, a simplified physical model for photon interaction with the lung blocks will still be required, and this can be built upon that presented here for the flattening filter.While the physical processes used in the code are already sparse, further reductions in those processes should also increase calculation speed. Ignoring Rayleigh scatter within the flattening filter (and any lung blocks) may prove to provide acceptable reproductions of the physical dose distributions. Further simplifying the electron transport for low‐energy photoelectron interactions  should decrease the computational cost by reducing sampling and increasing the ratio of sampled events that contribute most to the dose.V. 60Co teletherapy unit. The algorithm is based on voxel density information from CT images of patients. Within the context of the required dose accuracy for TBI, the simulations accurately predict the PDD along the central axis and the depth‐dose profile in a water phantom. In an anthropomorphic RANDO phantom, the gamma pass rates for limits of 5 mm and 5% of maximum dose were >99% for all planes measured. At present, the fast Monte Carlo code developed here is ~ ten times more efficient than an equivalent GEANT4 simulation, and accurate to within a mean relative difference of <2% for all profiles taken across axial dose distributions produced by both codes. This has been accomplished by limiting physics processes, spatial resolution, and the resolution of lookup tables within the code to provide only the accuracy needed for TBI implementation. Further additions of variance reduction techniques to the already purpose‐built lightweight code will potentially allow this code to provide clinically useful information in planning TBI at our center.In this work, a custom Monte Carlo code was developed in C to simulate dose distribution by 1.25 MeV photons in heterogeneous tissue for total body irradiation using


In image‐guided radiotherapy (IGRT) of prostate cancer, delineation of the clinical target volume (CTV) often relies on magnetic resonance (MR) because of its good soft‐tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR‐CT registration of the prostate has previously been developed using a voxel property‐based registration as an alternative to a manual landmark‐based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni‐Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80 mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration.PACS number(s): 87.57.nj, 87.61.‐c, 87.57.Q‐, 87.56.J‐ Planning image‐guided radiotherapy (IGRT) of prostate cancer often benefits from several imaging technologies. Computed tomography (CT) is the standard imaging modality for planning of IGRT of prostate cancer. However, studies have shown that a delineation of the clinical target volume (CTV) using CT leads to an overestimation of the CTV. CTVs up to 40% larger have been found on CT when compared to using magnetic resonance (MR) for CTV delineation.An important challenge in the registration process relates to the movement of the prostate between scans, which can occur relatively to the pelvic bones and neighboring organs in the pelvic. This may result in an inaccurate alignment of the prostate in MR and CT if the registration is based on an alignment of the pelvic bones.To be able to reduce margins added to the CTV, a local registration of the prostate is needed to ensure an accurate alignment of the prostate in MR and CT. A local registration can be obtained by fiducial markers, which are implanted into the prostate gland and can be regarded as common visualized structures in MR and CT. Previous studies have performed local rigid MR‐CT registration of the prostate based on registrations using implanted fiducial markers. Landmark‐based registration,The use of fiducial markers in a rigid registration of MR and CT, as in the previously listed studies, relies on an assumption that the prostate does not deform but only moves relatively to the pelvic bones. This assumption has been investigated and discussed in a number of studies.Comparison of image registration approaches have previously been performed in a number of studies.Carl et al.The objective of this paper is to compare two approaches previously presented for MR‐CT registration that have used the Ni‐Ti prostate stent as the fiducial marker. The first approach is based on manually defined landmarks on the prostate stent and on the pelvic bones and is now the clinical practice at Aalborg University Hospital.512×512×16 corresponding to a voxel size of 1×1×2.5 mm3  and axial T2−weighted  1.5 Tesla MR scans  obtained with a body coil with an image matrix of 512×512×16 corresponding to a voxel size of 0.55×0.55×3 mm3. Only T2−weighted MR images were acquired. Both axial MR and axial CT scans were acquired supine using knee and feet flexion, and the time between the two scans was between zero and five days. No special measures were taken to reduce motion.Data from 30 patients was used and consisted of spiral CT images  with an image matrix of The patients were all diagnosed with local or locally advanced prostate cancer and referred to the Department of Oncology at Aalborg University Hospital for radiotherapy treatment. The prostate was manually delineated by an experienced clinician slice‐wise on the axial MR and was used as the CTV for the radiotherapy treatment. The insertion of the prostate stent was performed using locally urethral anesthesia and prophylactic antibiotics (Ibrufen and Ciproxin). The catheter was inserted to the correct position and the collar of the stent was then expanded using 50 ml 60°C hot water. The length of the stent was adapted to the individual patient based on an estimation of the length of the urethral part of prostate using a diagnostic MR scan. The outer diameter of the cylinder shape part of the stent was 7 mm and for the fully expanded collar 14 mm. The noninvasive insertion procedure, together with a possibility of easy removal of the prostate stent, is one of the benefits of the prostate stent compared with alternative fiducial markers. Advantages using the prostate stent in the MR‐CT registration include fewer artifacts in CT and the fact that it is clearly visible in MR. Other advantages are that the stent itself is one large 3D object and it does not migrate over time.The manual approach used four or five selected anatomical landmarks to perform a rigid registration of MR and CT. Three landmarks were defined on the prostate stent, one each at the cranial, middle, and caudal end of the stent. Another one or two anatomical landmarks were defined, one anterior and one lateral to the prostate surface but as close to the prostate surface as possible. To weight the landmarks on the stent as much as possible, it was sought only to use one anatomical landmark placed at the symphysis pubis between the two pubic arches. In the cases with large prostate movement, another landmark was placed in one of the pelvic bones. The anatomical landmarks were moved if they had an impact of the alignment of the prostate stent in MR and CT. The anatomical landmarks were defined to constrain rotations about the central axis of the stent. The registration that resulted in a minimized root‐mean‐square distance between landmarks was then determined and the MR image was transformed. Manual registrations were performed between March 2007 and May 2009The automatic approach performed a rigid registration in a two‐step voxel property‐based approach, which used the voxel similarity measure mutual information presented by Collignon et al.Three experiments were performed to compare the registration approaches.The first experiment investigated translational and rotational differences of the CTV after registration with each of the two registration approaches. This was obtained by extracting 1000 random point coordinates automatically inside the CTV volume in MR to investigate the difference in translation and rotation of tumor, which was included in the CTV. The 1000 points were then registered with each of the two registration approaches, and the rotational and translational differences after registration were calculated, as well as the distance between the random points after registration.CTVman is the CTV after the manual registration and CTVauto is the CTV after the automatic registration.The second experiment investigated the registration effect on the CTV by computing the overlap of CTVs produced by each registration. Identical transformations will give an overlap of one and the larger the differences in the transformations, the lower the overlap. The overlap was calculated as the DSC  and sensSensitivity was used as a measure of the number of common voxels in the CTV between the two registration approaches compared to the number of uncommon voxels in CTV between the registration approaches. Furthermore, the prostate stent and the pelvic bones were segmented using fuzzy c‐means to visualize the differences in transformations on the prostate stent with reference to the pelvic bones.The third experiment investigated the potential for the automatic registration approach to be implemented into clinical practice. This was indirectly validated by extending the manual delineation of the CTV after the automatic registration with the error of the automatic registration. The metrics calculated in the volume overlap experiment were calculated in this experiment, as well. The overlap between the volumes were expected to be higher as the CTV transformed with the automatic registration approach was extended, which was expected also to result in a higher sensitivity.The rotational difference in the z‐axis (superior/inferior direction) can be explained by the rotational constraints in the automatic registration approach. In this approach, no rotation about the z‐axis was allowed to prevent rotation about the central axis of the stent.0.87±0.05 (range 0.77‐0.95) and the mean sensitivity was 0.87±0.05 (range 0.74‐0.95).The CTV overlap after registrations measured as the mean DSC was The mean registration error calculated as the Euclidean distance for the landmark‐based MR‐CT registration used in the present study has previously been calculated and can be regarded as the general registration error. The mean error was 1.25 mm and the maximum error was 1.86 mm between the four or five anatomical landmarks.0.90±0.05 (range 0.77‐0.97). The mean DSC was 0.87±0.05 (range 0.77‐0.93). The sensitivity increased as both the rate of true positive voxels increased and the rate of false negative voxels decreased.The mean sensitivity between the manual delineation after manual registration and the extended manual delineation after automatic registration was Several registration approaches have been published and are used for registration of MR and CT images of the prostate. In this paper, a comparison of two of the most widely used approaches, landmark‐based registration using manually defined landmarks and voxel property‐based registration using mutual information as the similarity measure, is presented. The registrations from the manual landmark‐based registration were obtained from the treatment planning process and were, therefore, not performed by the authors, to eliminate any potential observer bias.The comparison was based on differences in the translations and rotations between the two registration approaches. Also, a manual delineation of the prostate as the CTV for each dataset was used to calculate the anatomical overlap of the prostate in the transformed MR images. Both registration approaches assume no prostate deformation or change in prostate volume caused by differences in rectum or bladder filling between the scans or the use of an endorectal coil. This assumption is supported by studies where the prostate deformation was found to be present, but negligible, compared with the prostate movement relative to the pelvic bones.Otherwise, the registrations would have introduced a risk that the prostate stent in MR was aligned with one of the stent border in CT instead of an alignment of the central axis in both imaging modalities. No problems caused by partial volume effect could be found in either of the registration approaches. Artifacts are a common problem in CT when fiducial markers are used. The prostate stent in the present study is made of nickel and titanium, which both have a low atomic number. Therefore, the artifacts caused by the prostate stent in CT are less of a problem compared to gold fiducial markers.The processing time of the automatic registration was in total between 3 and 4 min for each patient. This was merely machine processing time and included saving all intermediate results and without speed improvements. For the manual registration, the time required for a clinician to perform the registration will vary, but typically takes between 10 and 30 min and is entirely manual labor except for the minimization of the distance between the landmarks.1.1±0.5 mm for CT and 3.0±0.9 mm for MR.No ground‐truth registration exists, which makes visual inspection crucial in order to assess the influence of the difference between the registration approaches. It is possible to perform phantom studies to get closer to the ground truth, as in Korsager et al.The error which is used to discuss the clinical potential is most likely an overestimate of the automatic registration error as it is computed as a root‐mean‐square error and thereby a worst‐case estimate, assuming an isotropic error in all three image directions. Furthermore, it is a systematic error determined from one patient. A more appropriate procedure is to find the mean systematic and mean random error for a number of patients and use that to compute the error, which needs to the accounted for in the margin added to the CTV. As the validations have not been performed using the same procedure and datasets, a comparison of the accuracy cannot be performed.The errors in the registration and the difference between the registration approaches need to be accounted for in radiotherapy planning. This is done by adding a margin to the CTV, which also includes uncertainties related to patient setup errors and prostate position error caused by interfraction movement and by intrafraction movement. However, the benefit of using image registration in prostate cancer radiotherapy is that it enables target delineation in MR, which has been shown to reduce the delineated volume by about 30%–40%T2−weighted MR scans were available as they are now used in the clinic to delineate the prostate as the CTV. Other MR modalities, such as Dynamic Contrast Enhanced MRI and Diffusion‐weighted MRI, would be appropriate to include to define the tumor and thereby increase confidence in prostate tumor definition.In the present study, only The results demonstrate comparable registrations between the manual landmark‐based registration and the automatic voxel property‐based registration. The quality of the automatic MR‐CT registration is observer‐independent, which improves objectivity and reduces uncertainties in the planning.This work is licensed under a Creative Commons Attribution 4.0 International License.