Magnetization reversal in a GaMnAs trilayer system consisting of two GaMnAs layers separated by a Be-doped GaAs spacer was investigated by magnetotransport measurements. The rotation of magnetization in the two GaMnAs layers is observed as two abrupt independent transitions in planar Hall resistance (PHR). Interestingly, one GaMnAs layer manifests a positive change in PHR, while the other layer shows a negative change for the same rotation of magnetization. Such opposite behavior of the two layers indicates that anisotropic magnetoresistance (AMR) has opposite signs in the two GaMnAs layers. Owing to this opposite behavior of AMR, we are able to identify the sequence of magnetic alignments in the two GaMnAs layers during magnetization reversal. The PHR signal can then be decomposed into two independent contributions, which reveal that the magnetic anisotropy of the GaMnAs layer with negative AMR is predominantly cubic, while it is predominantly uniaxial in the layer with positive AMR. This investigation suggests the ability of engineering the sign of AMR in GaMnAs multilayers, thus making it possible to obtain structures with multi-valued PHR, that can be used as multinary magnetic memory devices. Owing to the tunability of magnetic properties of this material by carrier density, it has received a great deal of attention in the field of spintronics. Dependences of Curie temperature, coercive field, and magnetic anisotropy on carrier concentration has already been thoroughly investigated in single-layer GaMnAs films9. Magnetic devices, however, typically involve combinations of two or more ferromagnetic layers, in which magnetic alignments switch their relative orientations, resulting in giant magnetoresistance (GMR) or tunneling magnetoresistance (TMR) effects12. In this context the process of magnetization reversal in GaMnAs-based multilayer systems needs to be thoroughly understood, in order to utilize such structures in spintronic device applications.The alloy GaMnAs is a well known ferromagnetic semiconductor in which ferromagnetism is mediated by charge carriers16. All those investigations, however, were carried out on GaMnAs multilayers in which the anisotropic magnetoresistance (AMR) of individual GaMnAs layers have the same sign. In that case the planar Hall resistance (PHR) always changes in the same direction (positive or negative) in both GaMnAs layers as their magnetizations make transitions between adjacent quadrants, even when the coercive fields of the two layers are different. Understanding of the behavior of PHR during magnetization reversal in such GaMnAs multilayers is thus rather straightforward. However, the PHR behavior is expected to be significantly more complicated in GaMnAs multilayers in which individual GaMnAs layers have AMR with different signs, since changes in PHR depend on the sign of AMR as the magnetization rotates between adjacent quadrants. Even though most GaMnAs layers investigated so far show negative values of AMR17, observation of positive AMR has been reported in special cases when the flow of current in the GaMnAs film is oriented along carefully chosen crystallographic directions20, and in a Li-doped GaMnAs layer21. However, a GaMnAs trilayer structure with two opposite signs of AMR has never been fabricated, and transport properties during the magnetization reversal have never been investigated.Several investigations addressing magnetization reversal in GaMnAs-based multilayers have already been carried out, reporting spin-valve behavior and/or interlayer exchange coupling in these systemsIn the present study we observe both signs of AMR  in trilayers consisting of two GaMnAs layers separated by a heavily Be-doped GaAs spacer. Surprisingly, in such structures the AMR in the two GaMnAs layers exhibits opposite sign, revealed by opposite changes of the value of PHR as magnetization rotates in the same sense during both field and angular scans. A detailed investigation of magnetization reversal in this trilayer structure was performed by using a specific initialization of magnetization prior to measurement, described in the next section. By careful experiments and their analysis, we were then able to identify the magnetization alignments in the two magnetic layers, and to separate the contribution to PHR from each layer, thus allowing us to investigate the magnetic properties of the individual GaMnAs layers displaying the two signs of AMR.−x1MnxAs film with x = 0.06 was first grown directly on the GaAs buffer, followed by deposition of a 4 nm GaAs spacer layer. During spacer deposition, Be was additionally supplied for p-type doping of the spacer. Two different temperatures, 1120° and 1140°, were used for the Be effusion cell in order to vary the doping level of the GaAs layer in the two trilayer structures, which we will refer to as samples T1 and T2, respectively. Finally, the top Ga−x1MnxAs film with the same of x as the bottom layer was deposited to a thickness of 8 nm. In addition, a single layer of GaMnAs, which we will refer to as sample S1, was grown with the same value of x as that used for trilayer growth, to be used as a reference sample. A schematic of the complete trilayer structure is shown in Fig. Two GaMnAs/GaAs:Be/GaMnAs trilayer structures used in this investigation were grown by molecular beam epitaxy (MBE) on (001) GaAs substrates. Prior to growth of the GaMnAs layer, a 100 nm GaAs buffer was deposited on the substrate at 600 °C. For deposition of GaMnAs layers, the growth temperature was lowered to 250 °C. A 15 nm bottom Ga2 Hall bar device was patterned on the specimen by photolithography and dry etching, with its long dimension along the [110] direction. A schematic view of the Hall device is shown in Fig. For transport measurements, a 1000 × 10 μmRPHR of the device shown in Fig. 23RPHR is the value of the planar Hall resistance, M is the magnetization, J is the current density, J and M, and ΔR is the anisotropic magnetoresistance (AMR) parameter defined as Planar Hall effect (PHE) measurements are convenient for investigating magnetization reversal in ferromagnetic films, because they are very sensitive to the relative orientation of magnetization with respect to the current. This can be done by measuring transverse resistance R is typically negative17. In that case the value of RPHR is negative when the magnetization is either in the 1st or the 3rd quadrants , but during magnetization reversal experiences transitions to positive values as the magnetization rotates to its interim easy axes in the 2nd and the 4th quadrants  for the same sense of magnetization rotation at the coercive fields of the two layers14. Samples T1 and T2 indeed do show additional transition steps during magnetization reversal, but the observed behavior of PHR is quite different from that described above for a trilayer consisting of two GaMnAs layers with the same sign of ΔR. Specifically, the data plotted in Fig. opposite signs as the magnetization rotates in the same sense in the two layers, a process that can only be understood if ΔR in the two GaMnAs layers has opposite signs.The PHR results obtained for samples T1 and T2 are plotted in Fig. 25, from the behavior just described we conclude that the GaMnAs layer with negative ∆R has its magnetic easy axis near 60°, deviating about 15° from the [010] direction. This deviation provides a measure of the relative strengths of uniaxial anisotropy field Hu along 26.For a more detailed discussion of magnetization reversal in trilayers containing GaMnAs layers with opposite signs of ∆R, we will use the results obtained on sample T2. Figure nd panel in the right column of Fig. The negative jump of PHR, on the other hand, becomes continuously weaker as the field direction increases to 85°, at which angle it disappears  corresponds to rotation of magnetization in the GaMnAs layer with negative ΔR, as inferred from Eq.  coercive fields for the GaMnAs layer with the positive (negative) sign of ΔR observed in field scan measurements shown in Figs For PHR measurements shown in the figure, the magnetization of the trilayer  was initialized by applying a field of 2000 Oe in the 4st to the 2nd quadrant in both GaMnAs layers (see thick arrows at bottom of figure). The change in PHR for this simultaneous transition in the two GaMnAs layers is smaller than that caused by the transition in the single GaMnAs layer with negative ΔR, since it is a superposition of opposite contributions to PHR from GaMnAs layers with opposite signs of ΔR as the magnetization rotates from the 1st to the 2nd quadrant. Thus PHR reaches only an intermediate value (marked RPI), which is a stable state with parallel orientations of magnetization in the trilayer, as indicated by thick arrows at bottom of the figure. When the field direction is further rotated CCW, past 180°, the GaMnAs layer with the negative sign of ΔR rotates first from the 2nd quadrant to the 3rd (open arrows), resulting in perpendicular alignment of magnetizations in the two magnetic layers. In this configuration the PHR of the trilayer reaches a minimum value, marked by RNM. The magnetization of the GaMnAs layer with the positive ΔR undergoes the same rotation at a slightly higher angle, resulting in an intermediate PHR value marked RNI. Finally, as the field orientation passes 270°, the magnetizations of both layers again rotate together (from the 3rd to the 4th quadrant), returning to the initial configuration, as shown by the arrows .As the field direction passes 90° during CCW rotation, the PHR jumps abruptly to a positive value. This is a result of magnetization rotation from the 1R  are about twice as large as those caused by the layer with positive ΔR  for the same rotation of magnetization over the [110] direction. Since the bottom GaMnAs layer is about twice as thick as the top layer, it is expected to cause a larger contribution to PHR for the same rotation of magnetization. This allows us to identify the bottom GaMnAs layer of the trilayer structure as having negative ΔR, and the top layer as having ΔR with a positive sign.Note further that, in the behavior just described, changes in PHR caused by the GaMnAs layer with negative ΔPM in Fig. NM. The value of RPM and RNM occur at field orientations when the magnetizations in the two GaMnAs layers are in perpendicular alignment. As indicated by the thick arrows in the figure, this alignment results in the pronounced PHR minima observed in the CCW rotation (black symbols), and in the pronounced maxima in the CW case. Importantly, such multiple PHR values realized by stable magnetic configurations of the two GaMnAs layers with opposite sign of ΔR provide an opportunity for applications as multi-valued storage devices28.When the field is rotated CW, the rotations of magnetization occur in reverse sequence, as shown by thick arrows in the top two rows of Fig. 30. We note, however, that the work of Bason et al.29 presents a concept of magnetic memory based on the PHE in a single magnetic layer with two in-plane magnetic easy axes; and the work by Sulaev et al.30 reports a continuous change of AMR in BiSbTeSe2 topological insulator by an applied gate electric field. In contrast, the multiple PHR states in our structure were realized based on various magnetic alignments in magnetic layers of the trilayer, clearly differing from the processes involves in those earlier investigations.The concept for PHE-based memory applications can be also found in earlier literatureR (see transitions marked by arrows) decreases very rapidly with increasing temperature in both types of measurement, and nearly disappears at 30 K, which is close to the Curie temperature of one of the GaMnAs layers in sample T2  is sufficiently strong for overcoming the weak energy barriers in the GaMnAs layer with negative ΔR  experiences a 360° rotation (see blue open arrows), while the layer with positive ΔR  experiences only 90° rotations between adjacent quadrants (see red solid arrows). Such partial rotation of magnetization in the GaMnAs layer with positive ΔR is due to the large energy barriers along the [110] and Since the observed PHR of the trilayer is a combination of contributions from the two constituent GaMnAs layers, we will consider these two layers as parallel conduction channelsst quadrant , on the other hand, experiences a different sequence of orientations when the measurement is initialized in quadrant 1 from that observed after initialization in quadrant 3. Since, as can be seen in Fig. R to PHR can then be eliminated by adding the data in Fig. R, which we plot in Fig. 38. Note, however, that the hysteresis widths around the R is primarily cubic, but with a finite admixture of uniaxial anisotropy that makes the energy barriers along [110] and Owing to these specifics of magnetization rotation during reversal in the trilayer, we can now separate the contributions of the two GaMnAs layers to total PHR. For example, if we compare the PHR results in Fig. ant Fig.  with thoant Fig. , we noteR to PHR is now established, it can be subtracted from the total trilayer PHR to obtain the contribution of the layer with positive ΔR. The angular dependence of PHR of the latter layer obtained by this procedure is plotted in Fig. R is limited to a coherent back-and-forth motion between the 1st and 2nd quadrants, as shown by red arrows in Fig. R is almost entirely uniaxial  has opposite signs in the two layers. Detailed investigation of the field and angular dependences of PHR obtained on the trilayer revealed that the sign reversal of AMR occurs in the top GaMnAs layer of the structure, which was deposited on a Be-doped GaAs spacer. The directions of magnetization in the two GaMnAs layers were carefully identified by analyzing the PHR data obtained during magnetization reversal. This identification of magnetic alignments has in turn allowed us to decompose the PHR signals of measured on the trilayer into separate contributions from the two GaMnAs layers. Interestingly, the GaMnAs layer with negative ΔSupplementary Information


A differential equation model is formulated that describes the dynamics of glucose concentration in blood circulation. The model accounts for the intake of food, expenditure of calories and the control of glucose levels by insulin and glucagon. These and other hormones affect the blood glucose level in various ways. In this study only main effects are taken into consideration. Moreover, by making a quasi-steady state approximation the model is reduced to a single nonlinear differential equation of which parameters are fit to data from healthy subjects. Feedback provided by insulin plays a key role in the control of the blood glucose level. Reduced β-cell function and insulin resistance may hamper this process. With the present model it is shown how by closed-loop control these defects, in an organic way, can be compensated with continuous infusion of exogenous insulin. This investigation deals with the modelling of a control mechanism by which, during and after a meal, the blood glucose level is maintained within safe bounds. For that purpose liver and skeletal muscle tissue are used as buffers. The method that is presented focuses on the control mechanism that allows such a configuration in which the hormone insulin plays a key role. The review article by Prentki et al.  gives a The model we introduce consists of three differential equations. Input variables are the glucose component of a meal and the energy expenditure in cells from different body tissues. During digestion of a meal glucose passes through the wall of the gastrointestinal tract and enters the blood circulation. Due to the large intake of glucose during a meal it is expected that the inflow into the bloodstream has a temporal pattern similar to the blood glucose level itself. Therefore, the time dependency of the inflow is assumed to have a particular type of distribution resembling graphs from the literature . The GamNormally, the amount of glucose in the blood circulation is tightly kept within a fixed interval. For a healthy person with a regular eating pattern blood glucose levels vary between ~4 (fasting) and up to 10 mmol/L . During a meal a strong glucose pulse enters the blood circulation. At its peak the flow can be at a rate of more than 250 mmol/h within a short time interval of less than an hour. The hormone insulin, produced in the β-cells of the pancreas, controls glucose homeostasis. It facilitates the uptake of glucose into tissues and stimulates the oxidation of glucose above fats. Moreover, it takes a role in the storage of glucose in liver and skeletal muscle tissue in the form of glycogen. The reverse process of raising blood glucose levels is mainly steered by a decrease in insulin and an increase in the counter regulatory hormone glucagon.x being the concentration of glucose in blood, y the concentration of insulin and z the concentration of glucagon. The interactions of these variables are assumed to be of the type of low order chemical reactions. Instead of describing the dynamics on the basis of what is known about the precise processes underlying the way in which the glucose flow within the body is steered, we concentrate on the direct interactions between the three system variables leading to a description of the system as we observe it. It is anticipated that this description covers the main processes in the control of the blood glucose level. Most importantly the model is required to give a sufficiently accurate quantitative description of the blood glucose profile during the intake of a carbohydrate rich meal. Various simplifying assumptions are made: liver and muscle tissue may store an unlimited amount of glucose, while through the liver an unlimited amount may be delivered. Furthermore, conversion of proteins into glucose and of protein and glucose into fats are secondary processes that are not taken in consideration; they do not play a decisive role in the handling of a large intake of glucose during and shortly after a meal . For a meal with given value wm)( this line is determined by two points: (wm), where xb is the baseline value. Such a relation only holds for meals with a comparable composition of carbohydrates.The dependence of the peak value xmax upon the parameter ω plays a main role in the control of the blood glucose level, see the sensitivity analysis in xmax as a function of the amount of exogenous insulin. In case of diabetes (ω = 2) a peak value of 12.8 will occur for the meal that was presented (■). This can be brought down to 9.7 by doubling ω (□). In the next section it is worked out how this technically can be achieved.The dependence of u(t) introduced in  that keeps the blood glucose concentration at a safe level [b given by (1 < λ), then ,x0)with ρ ≥x and its time derivative Additional functions can be incorporated in the insulin infusion device, such as range control by cutting off dosage below a certain blood glucose level and a temporal increase of ρ at extremely high blood glucose values. For β > 0 fine tuning can also be done by letting the amplification factor μ depend upon By increasing the amplification factor for w the value of ρ, that brings the peak value xmax below 10 mmol/L, can be found by using the linear dependence of the peak value upon the size w of the meal /ρ. This yields with  can be made. A first exploration of such a follow-up project has been made. It shows that a sufficiently large number of data points are needed given the number of parameters that have to be estimated.Furthermore, it is noted that the differential Eq  for insuThe study of closed-loop blood glucose control requires expertise of two almost distinct fields of science: the physiology of varying glucose levels and the construction of control algorithms. The latter is mostly based on a minimization of some cost function using mathematical techniques , 6. In oS1 Appendix(DOCX)Click here for additional data file.


For the pelvis case, the average distance between the actual setup error and estimated error was 2.0±0.5 mm for the doctors using the side‐by‐side method, 2.5±0.4 mm for technicians using the registration method, and 2.0±1.0 mm for the automatic algorithm. The ability of humans to estimate offset values improved statistically using our software for the chest phantom that we tested. Setup error estimation was further improved using our automatic error estimation algorithm. Estimations were not statistically different for the pelvis case. Consistency improved using the software for both the chest and pelvis phantoms. We also tested the automatic algorithm with a database of over 5,000 clinical cases from our hospital. The algorithm performed well for head and breast but performed poorly for pelvis cases, probably due to lack of contrast in the megavoltage portal image. The software incorporates an original algorithm to fuse portal and DRR images, which we describe in detail. The offset optimization algorithm used in the automatic mode of operation is also unique, and may be useful if the contrast of the portal images can be improved.The purpose of this study was to evaluate a custom portal image — digitally reconstructed radiograph (DRR) registration software application. The software works by transforming the portal image into the coordinate space of the DRR image using three control points placed on each image by the user, and displaying the fused image. In order to test statistically that the software actually improves setup error estimation, an intra‐ and interobserver phantom study was performed. Portal images of anthropomorphic thoracic and pelvis phantoms with virtually placed irradiation fields at known setup errors were prepared. A group of five doctors was first asked to estimate the setup errors by examining the portal and DRR image side‐by‐side, not using the software. A second group of four technicians then estimated the same set of images using the registration software. These two groups of human subjects were then compared with an auto‐registration feature of the software, which is based on the mutual information between the portal and DRR images. For the thoracic case, the average distance between the actual setup error and the estimated error was PACS numbers: 87.55.Qr, 87.57.nj Traditionally doctors have judged patient setup errors by viewing portal images alongside planning digital reconstructed radiograph (DRR) images, either with paper printouts, films on a light board, or on a computer terminal. Estimation of the error is made by measuring the distance from the isocenter to anatomical structures  visible in both images.Fully and semi‐automatic methods based on electronic portal imaging devices (EPIDs),The standard patient treatment regime employed at our institution involves first obtaining a CT scan of the patient which will be used for treatment planning. A DRR reconstruction of the beam's eye view (BEV) is computed and stored by a commercial treatment planning system (TPS). The treatment port, isocenter, orthogonal axes with scale information (a tick mark each centimeter) and other treatment information are burned into the DRR image.1760×1760 pixels) deep bit (10 bits per pixel) images. The portal images also contain scale information on the orthogonal axes with a small dot each centimeter. The portal and DRR images are compared to determine that the treatment beam accurately targets the planned treatment volume (PTV) while avoiding organs at risk (OARs). If a problem is detected, the presiding physician may request that the couch position be adjusted. Portal images are then retaken and rechecked against the DRR.Portal images are usually obtained at our hospital before the first treatment fraction using a megavoltage X‐ray source from a linear accelerator and a computed radiograph (CR) system . Images are captured onto photosensitive plates, which are scanned to produce high resolution . The software works for any anatomical region or gantry angle. The software can be operated manually, or with an automatic registration mode based on the mutual information between the images.The purpose of this study is to verify that our software actually improves setup error detection compared with the traditional side‐by‐side method. There is little statistical evidence in the literature of the superiority of image registration to side‐by‐side human estimation of setup error from two‐dimensional portal images. We evaluated the efficacy of the software as an aid for the clinical staff to improve setup accuracy using a prospective phantom study and its statistical analysis. We compared the ability of humans to correctly determine a known setup error with and without the software. An automatic mode of operation of the software was also tested with a database of clinical cases, collected over several years, for which the setup up error was determined by the consensus of a trained software operator , a radiation technician and the presiding oncologist.After a CT scan of the patient is obtained, a DRR of the BEV is computed and stored with a TPS. The treatment port, isocenter, orthogonal axes with scale information (a tick mark each centimeter) and other treatment information are burned into the image. The DRR image is saved in a database accessible to our software. Portal images are usually obtained before the first treatment fraction using a double exposure  with a megavoltage X‐ray source from a linear accelerator, captured with a CR system. The portal images also contain graphical scale information on the orthogonal axes with a small dot or tick mark every centimeter from the isocenter.In our proposed method, while the patient is lying on the treatment couch, the planning DRR and portal images are loaded into the software. The isocenter and two points on orthogonal axes, usually 10 cm from the isocenter, are designated by clicking on the images with the mouse. The locations of the points are determined by the operator using the axis and scale tick marks on each image. The software uses the three control points to determine scaling and rotation in order to transform the portal image into the coordinate space of the DRR image. Because the imaging plate may not be exactly orthogonal to the beam axis, especially when an oblique gantry angle is used, the portal image may be warped. The software can correct for these out‐of‐plane rotations.After the images have been successfully fused, the operator uses bony landmarks or other visible anatomical features in order to determine setup error. The portal image can be shifted horizontally or vertically and rotated clockwise or counterclockwise relative to the stationary DRR image. An example screenshot is shown in The operator can apply complementary colors, such as red and cyan, to make the fused image more distinguishable. If the images align exactly, each resulting pixel will be a level of grey. Misalignments are visible as color shadows. The operator can specify which pair of complementary colors to apply, depending on which colors are easier to see. Brightness and contrast can also be adjusted on both images to make bony features more distinguishable. The user can also rapidly flip between the DRR, portal and fused images. After the images are successfully registered, the treatment field shift (in mm) and rotation (in degrees), corresponding to the BEV, is displayed. The required couch movement to correct the offset, based on the gantry angle, can also be computed and displayed.b is the zoom ratio, Rot(θ) is the rotation about the z‐axis, Rot(ϕ) is the rotation about the x'‐axis, and St(Θ) is the stretch ratio produced by the rotation about the x‐axis. The rotation and stretch functions are defined as:Distortions of portal images may include rotations about the beam axis (z‐axis) and rotations about an axis perpendicular to the beam axis (x‐ or y‐axis). It is difficult to independently correct rotations about the x and y axes. We therefore devised a scheme to treat rotations about the x and y axes as equivalent rotations about an arbitrary axis perpendicular to the z‐axis, denoted as the x′‐axis. The x′‐axis lies in the x, y plane and points in the direction of the tilt. It is possible to compose rotations about the x′‐axis from equivalent rotations about the x and y axes, combined with a rotation about the z‐axis. Coordinates on the imaging plate  correspond to the original coordinates  as follows:Note that k, l, m and n are in the original beam axis' coordinate system and can be computed from two pairs of points.The proposed image‐registration method uses imaging plates to obtain portal images for irradiation field verification. For non‐zero gantry angles, the imaging plate is held freely on a stand without fixation to a certain coordinate system. In order to correct perspective distortion due to the imaging plate not being orthogonal to the beam axis, three control points are manually placed using graphical axis scale tick marks on each image. The specified points should cover the area of the image containing bony structures used for alignment. We refer to the area roughly surrounding the control points as the “working area”.To test the correction function, an electronic tiltmeter was used to measure a 10° tilt of the imaging plate about the horizontal axis. The plate was exposed to X‐rays using the linac axis grid. The resulting image was fused with a dummy DRR image. A test point was placed at a position 5 cm along the x‐ and y‐axis within the working area. The distance from the test point on the DRR image and the transformed portal image point was measured. The experiment was repeated with the test point outside the working area. The experimental setup is illustrated in pi is the probability of a gray‐level pixel value i estimated from the histogram of the image.p(ij) is the probability of gray‐level pixel value i from one image (A) and the gray‐level pixel value j from the second image (B) at the same position. Mutual information is then defined as:H(A) is the entropy of the first image , H(B) is the entropy of the second image , and H is the entropy of their joined histogram.We developed a method to automatically determine the x‐y shift and in‐plane rotation based on the mutual information (MI) of the pixel intensity values of the overlapping portions of the transformed portal and DRR images. The entropy of a single image can be computed from the equation:Optimizing the MI involves finding the x‐y shift position that yields the highest mutual information coefficient, automatically shifting the portal image left, right, up and down a determined step size, and choosing the position with the highest MI coefficient. If the current x‐y shift value yields the highest MI coefficient, the optimization loop is abandoned. In order to reduce the likelihood of stopping at a local maximum, we employ a multiresolution method. The initial step size is relatively large, usually 4 mm. The best MI coefficient is located with the initial step size. The step size is then reduced by half and the process is repeated. The final step size is usually 1 mm, but can be set lower for submillimeter accuracy.Adding rotation checks can increase optimization time exponentially if every possible rotation is checked at each shift location. However, if we assume that the setup rotation is small, a good compromise approach is to search rotation and shift iteratively; that is, search for the best x‐y shift and then search for the best rotation. This process is repeated until the best possible rotation is found at the best possible x‐y shift.10×10 cm square field.CT scans of anthropomorphic thorax and pelvis phantoms were taken. The images were read into a commercial treatment planning software system . DRR images  were generated using an antero‐posterior (AP) port with a In order to be able to present a portal image with a known but arbitrary x‐y error offset, we first obtained a single portal image of the phantom without field or scale ticks. Our test software burns in the treatment field and tick marks based on determined horizontal and vertical offset error values . The porFor the ten thorax and ten pelvis cases generated by the computer with the above method, we presented the series of cases to each examiner using three methods. First, five licensed radiation oncologists determined setup error with the conventional side‐by‐side method, without the aid of the registration software. We refer to this as the “side‐by‐side method”. These doctors included experienced physicians, as well as doctors in training. Four radiation technicians were then asked to determine the same series of setup errors using the registration software, which we refer to as the “registration method”. The automatic determination function of the software was also used to determine the setup error of the same case list (“automatic method”). Doctors and technicians were allowed to adjust image brightness and contrast. No time constraint was in place during the testing.(x−x0)2+(y−y0)2 where  is the estimated offset and  is the actual offset. The average and standard deviation of the distance was computed for each examiner.The distance from actual and estimated offset was computed with The average consistency of an examiner was defined as the average geometric distance between two estimations of a single case. The mean of the methods was compared with the paired t‐test. The difference of the average consistency among the three methods was also computed in order to determine if consistency improved using the software. All statistical analysis was performed with JMP version 8 .In order to test the performance of the automatic registration mode of the software on actual clinical data, we assembled a database of over 5,000 patient setup cases performed between April 2007 and December 2009 at our hospital. At the time of treatment, the portal image for each case was registered with the corresponding DRR image using the manual mode of the software, and the setup error for each case was determined by a consensus of three people: a software operator , a radiation technician, and the attending oncologist. Although it is difficult to establish a “gold standard” for clinical data, for this study we assume the human‐determined offset is correct, or at least very near the actual offset error.After assembling the database, a large batch script was executed to open each case, fuse the portal and DRR images, and automatically determine the error offset using the MI optimization algorithm described above. The automatically‐determined error offset was then compared with the human‐determined error offset. The geometric distance between the two values was computed and a statistical analysis was performed.The results of the thoracic and pelvis are shown in 4.3±3.0 mm for the radiation oncologists without the registration software, 2.1±2.4 mm for technicians with the registration software, and 0.8±0.4 mm for the automatic algorithm. For the pelvis, the average distance between the actual setup error and estimated error was 2.0±0.5 mm for the radiation oncologists without the registration software, 2.5±0.4 mm for technicians with the registration software, and 2.0±1.0 mm for the automatic algorithm.For the thorax, the average distance between the actual setup error and the estimated error was Each case was presented to the examiner twice. These two values are plotted vertically for each examiner. Consistency was measured as the geometric distance between the two estimated offsets of the same portal image. This corresponds to the height of the vertical bar in 2.4±2.0 mm for thorax and 1.4±1.2 mm for pelvis. The average consistency for the registration method was 1.7±1.6 mm for thorax and 0.9±0.5 mm for pelvis. The results are summarized in In order to determine statistically whether setup error estimation improved using the software, we computed the average consistency among the three methods. The average consistency for the side‐by‐side method was In order to test statistically that setup error estimation improved using the software, we computed the paired two samples for means. The null hypothesis is that population mean of the differences between the paired values is zero. The results are summarized in Results of the database study are summarized in ≤ 2 mm (Good), ≤ 5 mm (Fair), ≤ 10 mm (Poor) and > 10 mm (Terrible). In order to demonstrate the usefulness of the automatic mode of operation for two sample anatomical cases, we arbitrarily divided the distance between the human and computer estimated offsets into four bins: Results of the tilt experiment are presented in Our results show that the registration method was at least not worse than the side‐by‐side method, and the automatic method was statistically better than both the side‐by‐side method and registration method for the thorax phantom case studied. This result suggests that our software can be a reasonable complementary method in the clinical practice. Based on this study, the software has been installed in our hospital information system and has been used in clinical practice since April 2007. Moreover, the time required for the estimation of the setup error has not been extended by the usage of this program, once the operator obtained sufficient experience with the software.Because image registration is performed while the patient is waiting in the treatment position, the system must be fast — less than about one minute after the portal image is obtained.DRRs by any commercial planning software are to be used, as long as scale information is burned into the bitmap. The DRR image can even be a screen‐captured bitmap, or a digital scan of a paper, or film print.Any common file format  can be used for either the portal or DRR image.The system must work with any beam view of any anatomical region encountered in clinical practice.The software must run on a single standard PC running the Windows operating system.A number of criteria influenced our software design decisions:We have found that there was a large inconsistency in the side‐by‐side method among doctors. This is probably due to a difference in training or experience. The large variation in the accuracy of the final decision based on the portal film may influence the clinical outcome. The improvement in the consistency with automatic registration (which was found in this study for thorax) may improve the local control rate and complication rate in this context.If Dr. 5, in particular, is omitted, the side‐by‐side method compares well with the registration methods. Although Dr. 5 was a licensed radiation oncologist with experience estimating setup errors, he or she may have needed more practice with our experimental setup. Although we explained that the movement of the radiation field (rather than the treatment couch) should be specified, it is easy to mistake left‐right or up‐down shift. We feel that these kinds of human errors are inevitable when relying solely on human judgment with the side‐by‐side method.From the database study, we can state that the automatic mode of operation performs well for head, neck and breast cases, but performs poorly for pelvic cases. Due to the thickness of the human anatomy in the pelvic and abdomen regions, the contrast of the resulting megavoltage portal image is very low. This makes automatic registration based on mutual information extremely difficult. In order to improve the performance of the automatic registration, the contrast of the portal image needs to be improved. Possible methods of improving portal image contrast include using kilovoltage X‐ray, or using advanced digital image processing.Although the automatic registration algorithm performed reasonably well on the pelvis phantom, the brightness and contrast (window level) of the portal image from the phantom image was adjusted by hand to maximize the contrast of the pelvic bone. In the database study, however, it was not feasible to adjust all cases by hand. The window level was computed automatically from the raw scanned data based on the histogram of pixel values. If the window level algorithm can be improved, the image contrast may be increased. Sophisticated image filters may also improve the signal‐to‐noise ratio and make bony features more recognizable.While some verification systems are applicable to only a particular application  may not align exactly. If the entire image is to be used for alignment, an alternative method would be to use a calibration function such as McalList from the Matrox Imaging Library.The ability to estimate offset values improved using our software for the chest phantom that we tested. Setup error estimation was further improved using our automatic error estimation algorithm. Estimations were not statistically different for the pelvis case. Comparing the automatic setup function with a database of clinical cases estimated by human operators revealed that the automatic function works relatively well for head, chest and breast cases, but performs poorly for pelvis and other cases. Automatic registration should improve by increasing the contrast of the portal image. Although setup error can be manually judged accurately and quickly with the software as an aid to doctors and technicians, work remains to make the software more fully automatic.


The Australian Longitudinal Study on Male Health (Ten to Men) used a complex sampling scheme to identify potential participants for the baseline survey. This raises important questions about when and how to adjust for the sampling design when analyzing data from the baseline survey.We describe the sampling scheme used in Ten to Men focusing on four important elements: stratification, multi-stage sampling, clustering and sample weights. We discuss how these elements fit together when using baseline data to estimate a population parameter  or to estimate the association between an exposure and an outcome . We illustrate this with examples using a continuous outcome (weight in kilograms) and a binary outcome (smoking status).Estimates of a population mean or disease prevalence using Ten to Men baseline data are influenced by the extent to which the sampling design is addressed in an analysis. Estimates of mean weight and smoking prevalence are larger in unweighted analyses than weighted analyses  and the standard error of the mean is 1.03 times larger in an analysis that acknowledges the hierarchical (clustered) structure of the data compared with one that does not. For smoking prevalence, the corresponding standard error is 1.07 times larger. Measures of association  are generally similar in unweighted or weighted analyses and whether or not adjustment is made for clustering.The extent to which the Ten to Men sampling design is accounted for in any analysis of the baseline data will depend on the research question. When the goals of the analysis are to estimate the prevalence of a disease or risk factor in the population or the magnitude of a population-level exposure-outcome association, our advice is to adopt an analysis that respects the sampling design. Korn and Graubard [Like many large-scale health surveys, the Australian Longitudinal Study on Male Health (Ten to Men) used a complex sampling scheme. This choice was made because sampling the target population using a simple random sample was not feasible. Sampling theory therefore plays an important role in our study design because it provides a framework for efficiency gains . In Ten Graubard  give an Our aims here are to (1) describe each of these competing elements as they relate to Ten to Men; (2) detail and discuss the calculation of inverse-probability-of-selection sampling weights; and 3) provide recommendations for analyses that acknowledge these aspects of the design. We use a continuous variable (weight in kilograms) and a binary variable (current smoking status: smoker or non-smoker) as illustrations throughout. Our attention is restricted to the baseline  wave of data collection. Our analyses are conducted in Stata [ provide When stratification is used in a survey design, it refers to the population being partitioned into groups prior to selection of the sample . SamplesThe Australian Statistical Geographic Standard  (ASGS) uSitting alongside the ASGS classification of remoteness is the ABS division of the population into “statistical areas”. The smallest units are Mesh Blocks , which aggregate into Statistical Area 1s , then SA2s , SA3s, SA4s and finally SA5s which are the six Australian States and two Territories.Ten to Men employed a multi-stage design. For the major cities stratum, SA1s were sampled first  and all households were sampled within SA1s. For the inner and outer regional strata, SA2s were randomly sampled first  and then a fixed number of SA1s were randomly sampled within SA2s; at the final stage, households were sampled within SA1s. This additional step in the hierarchy of sampling SA2s for the inner and outer regional strata was introduced to reduce the distance fieldworkers had to travel.Australian Longitudinal Study on Male Health – Methods in this collection for a definition of the eligibility criteria). Thus, within a stratum, Ten to Men can be described as a cluster sample of eligible households, with SA1s defining the cluster. Households were therefore an additional level in the completely-nested hierarchy implied by the multi-stage sampling design.For all three strata, all eligible males within an eligible household were invited to participate in the study  are more likely to be invited to participate since sampling was proportional to size where size refers to the number of boys. Although individuals in the inner and outer regional strata did, in theory, have equal probabilities of selection , this was violated in practice due to variation in the participation fractions between households, SA1s and SA2s. This variability was an issue for the major cities stratum as well.Sampling weights can be used to address bias in estimation due to unequal sampling fractions and to account for non-response when estimating a population parameter. These sampling weights are calculated as the inverse of the individual probability of participation. For inner and outer regional participants the weights are the inverse of the product of (1) the probability of an SA2 being selected: (2) the probability of an SA1 within SA2 being selected; and (3) the probability of an individual within an SA1 both agreeing to participate and providing usable data. For major city participants, the weights are the inverse of the product of (1) the probability of an SA1 being selected and (2) the probability of an individual with an SA1 agreeing to participate and provide usable data. Where a stratum is under-represented in the sample compared to the population then the sampling weights will up-weight data from individuals in that stratum in the analysis. Details on the calculations of the baseline sampling weights for Ten to Men are given in Estimating means, prevalences or totals from a complex survey as though they were generated from a simple random sample has the potential to generate biased estimates and for the stated precision of these estimates to differ from the variability that we would observe in them under repeated sampling. The multi-stage sampling and selection of household clusters must therefore be acknowledged and accommodated when estimating population parameters. This can be done by either specifying a full multi-level model  or by using a set of “survey” commands, both of which are available in most standard statistical software packages (including Stata). The multi-level model approach allows us to account for all levels of the hierarchy  but does not allow the effect of the sample weights to be incorporated into the analysis at the level of the individual participant . This means the estimates generated from this procedure may be biased.The survey commands (at least those implemented by Stata and other major programs) only allow proper accounting for clustering at the top level of the multi-stage sampling hierarchy. This distinction is relevant in Ten to Men because for major cities, SA1s sit at the top of the hierarchy, whereas for the inner and outer regional strata, the larger SA2s were the first unit to be sampled. However, these commands do allow sample weights to be specified in the analysis. Consequently there is not a single procedure implemented in the commonly used software platforms that can account for the multi-stage design of the survey  and produce unbiased estimates of population parameters when using data from all three strata (which the weights are intended to address).We now demonstrate four approaches that reflect different ways of dealing with these issues when estimating a population parameter. Table As expected, the estimates of the population mean weight and the confidence intervals differ depending on the extent to which the sampling design characteristics are accommodated by the estimation procedure. When the population mean is estimated under the assumption of simple random sampling (row A), the mean weight is 83.9 kg (95 % CI 83.6 to 84.2 kg). Repeating this analysis but incorporating adjustment for the sample weights (row B) gives a mean weight of 81.4 (95 % CI 80.9 to 81.9). Using a multi-level modelling strategy that adjusts for the correlated observations within households, SA2s and SA1s (but does not adjust for stratification or sample weights) gives a similar mean to the unweighted analysis of 84.0 kg (95 % CI 83.5 to 84.5 kg) (row C). This mean appears to be biased probably because it does not account for the sample weights. Population estimates that account for the top level of the sampling hierarchy as well as weighting (and either with or without adjustment for stratification) are all identical to the level of precision reported (rows D to G). For example, when the SA1 is used as the PSU and the estimates are adjusted for stratification, the mean is 81.4 kg (95 % CI 80.8 to 81.9). This estimate is the same for all other combinations of PSU (SA1 vs SA2) and adjustment for stratification (no adjustment vs adjustment).The results for analysing a binary variable, current smoking status, paint a similar picture. The estimate of the population prevalence is highest when no adjustment is made for the sampling scheme. It is lower when adjustments are made for this, with no appreciable difference between using the SA1 or the SA2 as the PSU or making adjustment for stratification.Estimates of the association between variables  may also be affected by how the sampling scheme is treated in the analysis , 8. MostWe explore these issues with data examining the association between self-rated health and weight using linear regression. The measure of interest is the difference in mean weight (in kilograms) between two groups: those reporting excellent or very good health and those reporting good, fair or poor health. We compare results under a variety of conditions: where there is adjustment for the multi-stage design , adjustment for stratification , and use of sample weights (yes or no). We also examine the association between self-rated health and smoking status using logistic regression, where the effect size of interest is an odds ratio. We again omit the results from analyses that use a multi-level logistic model for the same reasons discussed in the previous section.In an analysis that makes no adjustment for the multi-stage design or for stratification or weighting Table , row A, Repeating the analysis to account for all stages of sampling using a multilevel model (rows E and F) gives a mean difference of −4.9 kg (95 % CI −5.5 to −4.2), with further adjustment for stratification giving a difference of −4.8 kg (95 % CI −5.5 to −4.2). As with estimating population prevalences using multi-level models, it is not possible to easily account for the sample weighting in this context.The final four rows in Table Thus, the estimate of the mean difference ranges from −4.3 kg to −5.1 kg. Taken as a whole, these results suggest that it is the adjustment for the sample weights that has the biggest impact on the results, with the adjustments for the sampling hierarchy and stratification having relatively minor influences on the estimate of the effect size. Nonetheless, all analyses would lead to the conclusion that the mean weight differs between the two groups, with those who have excellent or very good health being 4 to 5 kg lighter than those who have good, fair or poor health. This suggests that the way in which the sample design is accounted for makes some difference to the estimate of this measure of association on this occasion. This is supported by the second analysis in Table Analyses of baseline data from Ten to Men will require explicit adjustment (through the use of sampling weights or procedures for clustering) for the sampling design in order to generate unbiased estimates with reliable measures of their precision that reflect their variability under repeated sampling. The application of adjustments will depend largely on the particular research question and the proposed statistical analysis. While we have illustrated these concepts in the context of the Ten to Men study, the issues are relevant to all clustered survey designs.For estimates of a population prevalence and totals, the sampling design (including sample weights) should be adjusted for, since the estimators will (most likely) be biased and its precision understated if unadjusted, because the sampling variability will depend on the sampling fractions and hierarchical structure of the data. The issues are defining the PSU (SA1 or SA2) and whether or not to adjust for stratification. Regarding the PSU, our results show little difference in practice between using the SA1 or the SA2 as the PSU. Our recommendation is therefore to treat SA1s as the PSU. Similarly, while adjustment for stratification made no appreciable difference in this instance, we also recommend adjusting for stratification. In support of this, Some researchers may find it helpful to conduct sensitivity analyses, where they compare unadjusted and adjusted estimates of prevalence and associations to determine which of the results are sensitive to the extent that the sampling scheme is accommodated in the analysis. We support this, with the proviso that a statistical analysis plan be prepared prior to commencing analysis (see Thomas and Peterson  or Rubin


Two quality control devices, a light‐field device and a radiation‐field device, have been specially designed to assist the clinical implementation of conformal dynamic arc treatment (CDAT) and intensity‐modulated radiation therapy (IMRT). With these devices, the light field as well as the radiation field projected from the individual beams at any treatment position  can be evaluated. The devices are attached at the front end of the couch and placed at the isocenter of the linear accelerator treatment system (LINAC). The devices are designed to be able to rotate parallel to the gantry head so that the light field and the radiation field projected from a direct beam can be assessed. The aim of this study was to evaluate the geometric precision of the beam placement and the dosimetric accuracy performed in CDAT and IMRT with the aid of these devices. The devices are placed separately from the LINAC during use and provide an independent check on the quality performance of the LINAC in three dimensions. The condition of gantry sagging and any mechanical displacement resulting in field shift can be observed and traced during gantry rotation. Mistakes that occur during the isocenter calibration can lead to significant displacement in the field projection, which would not be revealed with the conventional quality control setting . This was demonstrated with the aid of the two quality control devices in the study. The influence of gravitational acceleration in the multi‐leaf collimator (MLC) leaf positioning error, which would consequently lead to inaccurate dose delivery, was investigated. The results of our study show that the existence of a gravitational influence is statistically significant, although the magnitude of the dose inaccuracy is small.PACS numbers: 87.53.Kn, 87.56.Fc, 87.66.Cd This was performed initially with a few conformal static beams through the use of a number of custom‐molded blocks made of lead alloy. With the development of the dynamic multi‐leaf collimator (DMLC), the field shape can be dynamically conformed to the target during gantry rotation. Thus, further improvement in dose conformity can be achieved through a series of conformal dynamic arcs. Lately, the field‐shaping technique has been extended into the concept of intensity‐modulated radiation, known as intensity‐modulated radiation therapy (IMRT).Over the past decade, the field‐shaping technique has been widely implemented to upgrade conventional radiotherapy to a three‐dimensional conformal radiation treatment. The benefits of dose conformity to the target volume while sparing dose to normal tissues and improving target dose uniformity have been discussed in several publications.–7 ThThe intensity‐modulated dose distribution is acquired by dividing the treatment fields into a number of segments. Each segment is automatically shaped by the multi‐leaf collimator (MLC) and irradiated as a function of the fraction of the total number of monitor units (MUs). The dose rate and the leaf movement for each segment are calculated by the MLC control system. The accuracy of the MLC leaf position at the isocenter is defined by a tolerance factor that normally ranges from 0.05 cm to 0.5 cm. To achieve a tighter tolerance, in the case of IMRT, if the leaf cannot reach the position in the required time, the treatment time will be increased by the electron gun delay, which results in a reduced dose rate. However, dose variations due to beam instability may be induced by the increase in beam hold‐off incidenceAlthough CDAT and IMRT are characterized by their superior conformal dose distribution compared with conventional modalities, their routine clinical implementation is partially held back by the complexity of their beam verification. Therefore, to ensure that the treatment is delivered accurately, it is essential that an efficient and effective quality assurance program can be applied on a routine basis.(–) Prior to treatment, a routine quality assurance control program is followed to ensure that the mechanical integrity of the treatment unit is maintained to an established standard. In general, before treatment, the shape of an individual treatment field is checked against the light field or radiation field. For the case of IMRT, it is common to perform a radiation fluence test for individual treatment fields. This test is generally performed at the 0° gantry angle due to the setup expediency. The result of such a test, however, does not necessarily provide the same outcome as if performed at the treatment position. The mechanical displacement of the LINAC system during gantry rotation and the effects of gravitational acceleration on leaf position accuracy are always of concernA review of the literature indicates that dose verification is performed during the commissioning of the system to ensure dose accuracy–16 PThe light‐field device is made of Perspex and consists of two concentric dials—a scale dial and an indicator dial—a paper holder, and a supporting metal rod Fig. . The two128 mm×113 mm×4 mm. A light‐field printout generated by the planning system is held between the Perspex sheets during the light‐field test. To avoid the production of light diffraction during the light‐field test, a square opening with dimensions 78 mm×78 mm was made in the middle of the upper sheet. The entire system is supported by a metal rod, which is inserted through the central axis of the scale dial and acts as a supporting pole for rotation.The paper holder consists of two pieces of Perspex with dimensions The radiation‐field device Fig.  is very Before using, the device must be calibrated at the horizontal level to define the offset position (0°). Calibration is performed with the device attached to the treatment couch. Instead of securing the device directly onto the treatment couch, a stereotactic couch mount is applied to ease the setup procedure because the couch mount provides for level adjustment. The offset position is found by rotating the paper holder/Perspex block together with the indicator dial to a horizontal level. The horizontal level is confirmed by placing water on the surface of the paper holder/Perspex block along the transverse axis of the treatment couch. When the horizontal level is achieved, the pointer marked on the indicator dial indicates the offset position. The offset position is then denoted with the scale dial at the position marked with 0 division. The horizontal level along the longitudinal axis of the treatment couch is also adjusted with the couch mount. After calibration, the position of the scale dial must be kept constant throughout the whole test. The center of the paper holder/Perspex block is then brought to the position of the isocenter of the treatment system by aligning it with the wall lasers and the ceiling lasers. Before the light‐field or radiation‐field test, the horizontal level of the holder must be checked after the printout/film has been inserted to ensure that no tilting results from the thickness of the printout/film. Direct beam projection is obtained by rotating the paper holder/Perspex block and placing it at the same angle as the gantry. Since the indicator dial is rotated with the paper holder/Perspex block, the angle of rotation is shown on the scale dial at the position pointed by the indicator dial.10 cm2×10 cm2. The leaf width ranges from 3.0 mm at the center of the field to 5.5 mm at the periphery. The maximum distance over the center line is 5 cm, and the maximum separation between the leaves is 10 cm. The system is capable of both dynamic MLC (DMLC) and segmental MLC (SMLC) delivery techniques; however, only the DMLC delivery technique is applied in this study. During IMRT, the control system drives the leaves at their maximum speed of 1.5 cm/s in a stepping manner while modulating the dose rate to achieve the desired leaf position. Various dose rates are applied in a range of 100 MU/min to 600 MU/min. The default tolerance factors of 0.25 cm and 0.5 cm are specified by the vendor, which controls the spontaneous MLC leaf position in IMRT and CDAT, respectively. According to the vendor's specifications, leaf position accuracy is also governed by the secondary feedback and the stepping motor of the system, which drive the leaf to the position with an accuracy of 0.1 mm. The test results of the light and radiation fields were compared with the dose plan generated by the planning system . The studies performed were as follows:The accuracy of the field projection of a dynamic conformal arc at different gantry angles was studied with the aid of the light‐field device. The field shape projected from the gantry was compared at every 10° of the gantry angle with the light‐field printout generated by the planning system.27 mm×22 mm×38 mm and 50 mm×50 mm×28 mm. To study the gravitational effect, each field was repeated to irradiate in three settings: setting 1: gantry 0° and collimator rotation 0°; setting 2: gantry 90° and collimator rotation 0°; and setting 3: gantry 90° and collimator rotation 90°. When both the gantry and collimator rotations are at 90°, the direction of the MLC leaf motion is parallel to the effect of gravity. In this case, any gravitational acceleration acting upon the MLC during gantry rotation will result in an increase in the leaf position error. In the case of gravitational influence, the difference in dose distribution between settings 3 and 1 are expected to be significantly higher than that between settings 2 and 1.The effect of gravitational acceleration on the leaf position and the dosimetric error during IMRT were evaluated. Two clinical plans with 13 IMRT fields were applied for this study. The maximum dimensions of the target volumes were A film was placed between the two Perspex slabs of the radiation‐field device and was irradiated according to the value of the leaf sequence factor. Radiation, in the amount of 100 MU to 148 MU was given. To avoid dose response saturation, the dose response sensitivity of the Kodak Xomat‐V film was tested in a dose range of 0 MU to 250 MU. The film was placed at the isocenter with a 15 mm buildup of water. In the study of each field, each set of three films was developed at the same time and followed by the dose scan with the film scanner (VIDAR VXR 12 plus) to minimize the variations that arose during film processing and scanning. The dose distributions were studied with a film dosimetry system . Using the dose distributions obtained at setting 1 as a reference, the differences between dose distributions obtained in settings 2 and 3 were compared.The displacement of the beam projection resulting from the inaccurate calibration of the isocenter was studied with the aid of the radiation device. In the study, the isocenter was inaccurately calibrated by a 1.1 mm shift from the actual position. The beam fluence profile of a single IMRT field was studied at three gantry angles: 0°, 50°, and 90°. The amount of field displacement was assessed by image subtraction. The fluence images obtained at the shifted isocenter were subtracted by that obtained at the correct isocenter at gantry zero.The automation precision of the LINAC system applied for the treatment of DCAT and IMRT was evaluated with the aid of the light‐field and radiation‐field devices. The studies were taken on a Varian CLINAC 2100 C/D equipped with an m3™  micro‐multileaf collimator (mMLC). The mMLC consists of 26 pairs of tungsten leaves that allow the treatment field to be shaped up to Fig. The dose response sensitivity of the Kodak Xomat‐V film was studied by plotting the optical density against doses in the range of 0 MU to 250 MU. The result confirms that the dose range (100 MU to 148 MU) applied in our study is well beyond the dose response saturation region (the plateau of the curve).gantry=90°, collimator=0°) and setting 3  were studied using the dose distribution obtained in setting 1  as a reference. By using setting 1 as a reference, the variations in pixel‐to‐pixel response, processing, and scanning conditions were accounted for and eliminated. Doses were normalized to the maximum dose of individual films. The dose histogram was studied, and the number of pixels enclosed by the isodoses from 20% to 90% in 10% steps was calculated. Neither extremely low (<20%) nor extremely high doses (>20%) were used for the study due to the substantial errors induced by the insensitive detection in the low‐dose region during film scanning and the limitation in spatial resolution for small area dose. The differences in pixel number between settings 3 and 1 and between settings 2 and 1 as a function of isodoses from 20% to 90% were calculated for all fields. The mean differences in pixel number and the standard deviations of 13 fields were plotted against the isodoses, as shown in Fig. 2 to 2.9 cm2 and 0.3 cm2 to 1.5 cm2, respectively. The difference in the number of pixels between settings 3 and 1 and between settings 2 and 1 were compared in pairs as a function of isodose from 20% to 90% in steps of 10% for all fields. The result of the paired t‐test suggested a significant difference (p<0.001) in dose coverage between settings 3 and 2. Our results therefore support that the position that gravitational acceleration influences leaf position error, which subsequently leads to inaccurate dosimetry.The differences between the dose distributions obtained in setting 2 . Moreover, due to the weight of the tools themselves, perfect circular rotation along the circumference is impossible because displacement caused by sagging is often experienced when the film holder/portal imager starts moving away from the zero gantry angle.With the devices introduced here, the above‐mentioned problems have been overcome; hence, the mechanical integrity of the LINAC system can be examined in a precise manner. The accuracy of the speed response of the MLC during DCAT and IMRT is also an important factor to ensure accurate dosage. Whether this will be affected by the orientation of the gantry head due to the effect of gravitational acceleration should be considered. Our study proves that there is a certain influence in leaf position accuracy due to gravitational acceleration. By restricting the tolerance level, one can minimize the effect and further improve dosimetry accuracy; however, it should be compromised with the practical capability of the system. Therefore, it is recommended that the optimal tolerance level be carefully investigated for each treatment system in such a way that it is practically tolerable and conceded with the acceptable dose accuracy. Moreover, with the aid of the radiation device, the dose distribution of an individual IMRT field can be obtained at any treatment position. Any inherent mechanical defects can be revealed by comparing the dose distribution with the fluence profile calculated by the planning system.Two quality control devices have been specially designed for light‐field and radiation‐field tests for the treatment of CDAT and IMRT. The devices provide an independent check on the mechanical performance of the LINAC system, which demonstrates geometric accuracy of beam delivery. In addition, dosimetry accuracy can be evaluated. With the devices, mechanical deterioration of the gantry can be traced easily in any gantry position. Moreover, mistakes made during system calibration can lead to significant errors in field placement that generally cannot be observed with the conventional quality control setting (gantry 0°) but can be revealed with the aids of these devices. IMRT and CDAT can be regarded as complex treatments that involve complicated dose calculation and complex processes in dose delivery. The devices suggested here are effective and expedient tools that can be used to ensure that the mechanical integrity of the delivery system is maintained to an established standard.The author thanks Hei Yuen Choi for the construction of the devices, Rachel Wong for the illustration, Kristal Lau for preparation of the manuscript, and Martin Szegedi from BrainLab Ltd. for the technical discussion.