Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results. Interpretation of the mpMRI in the clinic is done largely using the Prostate Imaging - Reporting and Data System (PI-RADS)2 - a score-based system that relies on qualitative interpretation of the image with the goal of identifying clinically significant PCa. Numerous research studies have demonstrated the promise of quantitative mpMRI that aims to improve upon subjective qualitative assessment. Imaging-based measurements derived either from operator-specified regions, or relying on automatic localization of the disease have been studied for a wide range of PCa applications from automated characterization3, to non-invasive monitoring of progression4 and evaluation of treatment response6. Further characterization of this early evidence and the implementation of quantitative imaging procedures more broadly requires improved understanding of the technical robustness and reliability of mpMRI as an imaging biomarker of PCa. One such aspect of technical assessment of a prospective biomarker involves evaluation of its repeatability, i.e., characterization of the consistency of the measurement under similar conditions in absence of underlying changes in the tissue biology. One of the approaches to evaluating repeatability involves imaging of subject multiple times over a short interval of time using identical imaging equipment. This type of study enables evaluation of acquisition-related sources of variability in absence of biological changes in the tissue, and is known as test-retest.Multiparametric Magnetic Resonance Imaging (mpMRI) is an effective tool in detecting and characterizing prostate cancer (PCa)8, little is known about its repeatability. Litjens et al. investigated repeatability in a study that involved repeated imaging of subjects within a longer interval of 6-12 months (n = 10)9. Gibbs et al. studied the repeatability of apparent diffusion coefficient in healthy subjects (n = 8)10. There are also studies investigating short-term repeatability of mpMRI 12. In an attempt to bridge this important gap in knowledge, we recently conducted analysis of PCa mpMRI in a test-retest study that investigated repeatability of measuring volume and mean Apparent Diffusion Coefficient (ADC) in small volume suspected disease, and anatomical regions of interest defined manually13. The study involved 15 subjects, and was the first to investigate short term repeatability of 3 T mpMRI obtained with the use of endorectal coil .Although mpMRI is being widely investigated as an imaging biomarker of PCa14, but subject-level information on the lesion volume is not available for any of the published studies. Applications of radiomics15 — quantitative characterization of radiographic phenotype using a variety of automatically derived metrics, such as texture and shape descriptors — is of increasing interest as applied to PCa16. No studies have investigated so far the repeatability of radiomic features, or the effects of various processing parameters on their stability.None of the prostate mpMRI repeatability studies published to date is accompanied by the imaging datasets used to arrive at the study findings. This prevents the opportunities for reusing the dataset to better understand the findings, or to apply the data to new studies. As an example, it is logical to expect, and there is evidence in other test-retest studies, that repeatability of volume measurement is dependent on the lesion size13. The imaging data we collected is accompanied by the image annotations and derived measurements used to quantify repeatability of volume and mean ADC in the earlier published manuscript. Some of the uses of the dataset we envision include further investigation of additional aspects of repeatability , development of new algorithms , and quantification of technical capabilities of the automatic processing tools .In the present data descriptor we share the original imaging dataset that was analyzed in our earlier study17–19 (https://www.dicomstandard.org/). This continues the precedent we established earlier to utilize standard representation for harmonizing data as applied to quantitative imaging biomarker development20.The resulting dataset  is shared as a collection of objects that are encoded using standardized Digital Imaging and Communications in Medicine (DICOM) (ISO 12052:2017) representation21 was obtained from the Institutional Review Board (IRB) of the Brigham and Women’s Hospital. The data is stored in the QIN-PROSTATE-Repeatability collection of the TCIA (see Data Citation 1).The Institutional Review Board of the Brigham and Women’s Hospital approved the Health Insurance Portability and Accountability Privacy Rule compliant study in which the image dataset was collected. Informed consent was obtained from all participants. Permission to share the de-identified imaging data, annotations and derived measurements in a restricted-access collection of The Cancer Imaging Archive (TCIA)13. Here we provide an abbreviated summary of the data acquisition details presented earlier, and discuss the dataset itself in the subsequent sections.The reader is referred to the manuscript summarizing the study and the analysis of the data for the study detailsMagnetic resonance imaging exams of the abdomen were collected for 15 men at the Brigham and Women’s Hospital (BWH). All of the subjects scanned were treatment-naive at the time of imaging. The cohort included both patients with the histologically confirmed PCa, and patients with no definite diagnosis that had a reason to be evaluated for the presence of PCa. The time interval between the baseline and repeat imaging exam was within 2 weeks. Both of the mpMRI exams were done using the standard clinical protocol implemented at BWH. Patients were scanned on 3 Tesla field strength scanners, either GE Signa HDxt or GE Discovery MR750w. In all cases, the baseline and repeat study for a given patient was done using the same scanner, with the use of a commercially available endorectal coil within an air-filled balloon . All of the acquisition parameters are available within the DICOM MR images included in the collection.22 (https://slicer.org) and involved planimetric labeling of the voxels within the regions of interest (ROIs) corresponding to the entire prostate gland and peripheral zone of the prostate, as well as tumor and normal areas of the peripheral zone (suspected tumor was identified in 11 of the 15 subjects). Each of the structures was annotated separately for each of the subjects using contouring tools in the 3D Slicer Editor module in both baseline and repeat studies for the T2-weighted image series, Apparent Diffusion Coefficient (ADC) maps derived from the Diffusion-Weighted acquisition that utilized b-values of 0 and 1400 mm/s2, and the subtracted images derived from the Dynamic Contrast-Enhanced (DCE) acquisition. The individual studies were randomized such that the reader was blinded to the identification of the subject for a given study, or whether that study was a baseline or a repeat exam. While annotating a given study, the reader reviewed all of the aforementioned sequences side-by-side, localizing the suspected tumor region by evaluating characteristics based on consensus of its appearance in individual sequences. This approach followed the PI-RADS v2 guidelines2, although the encoding of PI-RADS reports is not part of the present dataset. Once localized, the region was contoured in each of the individual image series based solely on its appearance in that image. Regions were annotated in the individual sequences, since it is known that separate MRI sequences interrogate different attributes of the disease, and the goal of the original data analysis study13 was to evaluate repeatability of volume quantification in each of the sequences separately.Acquired mpMRI data was annotated by a radiologist (further referred to as “the reader”) with over 10 years of experience in abdominal MRI interpretation (F.F.). Annotation was performed using the 3D Slicer research platform13.Given the definition of the ROIs, the volumes of each of the annotated regions for each combination of subject, time point and MRI sequence was measured by calculating the total number of pixels within a given region, and multiplying it by the volume of a single voxel. The latter was calculated by multiplying the in-plane pixel spacing and the distance between the consecutive image slices, as derived from the DICOM MRI data in the corresponding sequences. Mean ADC was automatically calculated as the arithmetical average voxel intensity over the segmented regions for the ADC images. These measurements were used to assess the repeatability of volume and mean ADC measurements in the study presented earlier21 (Data Citation 1) and consists of the de-identified MR images and the data derived from the images. The latter group contains the ROIs, and the corresponding per-structure measurements derived from them. All of the components of the dataset are stored using the DICOM17 format. DICOM is the international standard adopted universally by the manufacturers of medical imaging equipment for storing their acquired images. In addition to modality-specific images, DICOM defines multiple types of image-related objects20, including the types of data in the present data descriptor.The full dataset is archived with TCIADICOM objects are organized into a hierarchy of key-value items. The specifics of hierarchy, attributes to be included, constraints on their values, and other details are formalized by the DICOM Information Object Definitions (IODs), further referred to as “object types”. Those definitions can be applied to encode appropriate types of data into instances of DICOM object types that can be stored as DICOM files.In the present dataset, we utilize three types of standard DICOM objects: the DICOM Magnetic Resonance Image (MR) produced by the imaging equipment, the DICOM Segmentation Image (SEG) for the labeling of voxels into the regions of interest (ROIs), and DICOM Structured Reports (SRs) that are encoded per the Measurement Report Template  1500 for the measurements derived from the segmented ROIs of the MR images.dcmqi library23 (https://github.com/QIICR/dcmqi).DICOM objects that are encoded following IODs are called instances, and stored in files or sent over the network. One or more instances can be included in a single DICOM series, which in turn are organized into DICOM studies. Each of the DICOM instances, series and studies are assigned a unique identifier (UID) that are encoded as one of the dataset attributes. A DICOM study usually corresponds to a single imaging encounter of the patient. Individual series of a study correspond to different types of data acquired during that single encounter. In the present dataset, individual studies for a given subject  correspond to the test-retest imaging studies done on different days . Series correspond to each of the different types of MR acquisitions of image data, and annotations of those acquisitions with the ROIs and measurements. Image series were generated by the imaging equipment, while the derived object series were created by the conversion tools provided in the CameCase notation). Such composite context attributes include, for example, PatientID, PatientSex, PatientAge, PatientWeight, StudyDate and StudyTime, and the StudyInstanceUID (UID of the study).Each of the DICOM instances contains the instance-specific attributes, as well as the composite context attributes shared by all of the instances in a given series, study and for the same patient , and linking lab tests and pathology results with imaging evidence27. DICOM defines syntax rules for encoding of data and metadata, and includes controlled vocabularies that leverage widely used (extensible) external lexicons such as SNOMED CT28 (https://www.snomed.org/snomed-ct) and RadLex29. Both the well-defined syntax rules and controlled vocabularies are crucial to the successful extraction of knowledge from the data30. Every DICOM object has a unique identifier, and can include references to related (meta)data. DICOM Structured Reporting (SR)32 is the part of the standard intended to communicate imaging-derived findings, organized as hierarchical structures that link containers, codes describing concept names and values, references to related evidence, etc. DICOM SR “templates” define a pattern of content items and their relationships, constraining the general infrastructure and controlled terminologies for specific use cases. The DICOM Standards Committee has a process for introducing amendments via community participation. The DICOM standard is free to download and use without royalty or license fees. Based on these observations, we believe DICOM satisfies the Findable Accessible Interoperable Reusable (FAIR) guiding principles for scientific data management33.DICOM defines data models that link real-world objects  with information objects defined by the standard . Importantly, imaging-related evidence objects defined by the standard include such items as containers for image-derived measurements34, as originally produced by the MR scanner, and then de-identified. DICOM MR images can be recognized by the value of the SOPClassUID attribute, which uniquely identifies them as stored instances of the DICOM Magnetic Resonance Image IOD, as well as the Modality attribute, which has a value “MR”. The traditional DICOM MR IOD encodes individual slices of a 3D volume as separate instances (files), which can be spatially-related for reconstruction into a volume using the ImagePositionPatient and ImageOrientationPatient attributes. The DICOM Enhanced MR Image IOD, which can encode all the slices of a volume in a single multi-frame object, was not used in this data set since that was not the form in which the data was acquired. Nor was it converted to the similar Legacy Converted Enhanced MR Image IOD35.The MR image data in this collection is encoded as instances of the DICOM Magnetic Resonance Image IOD36, following the standard operating procedures of TCIA37. In the process of de-identification, the original content of some of the attributes was completely removed (such as PatientBirthDate), while others were modified to preserve privacy of the subjects: PatientName and PatientID were modified to follow the “PCAMPMRI-nnnnn” pattern (for PCa mpMRI subject nnnnn), and the original dates were shifted by the same offset for all of the datasets.The MRI data was de-identified using a modified version of the Basic Attribute Confidentiality Profile defined by the DICOM in PS3.15 Appendix E.2SeriesDescription attribute, which can have one of the following values corresponding to the acquisitions described above: “T2 Weighted Axial” , “Apparent Diffusion Coefficient” . Technically, “Apparent Diffusion Coefficient” and “DCE Subtraction” images are derived, since they were obtained by post-processing of the imaging data obtained by the imaging equipment. However, they were derived by the software of the MRI scanner, and saved as MR images, per the normal imaging protocol followed at our institution.The MR image data includes three types of images for each of the 30 studies. The type of image is indicated in the MagneticFieldStrength, RepetitionTime, FlipAngle, EchoTime, and InversionTime. The Manufacturer and ManufacturerModelName attributes can be used to distinguish the datasets obtained using the GE Signa HDx (subjects 1 through 7) and GE Discovery MR750w (subjects 8 through 15) scanners.The technical parameters of the MR acquisition protocol are stored in the MR-specific DICOM standard attributes, and include 38. Within the dataset, segmentations are recognized by their SOPClassUID, and have a value of “SEG” value for the Modality attribute. DICOM Segmentations store all slices of a segmentation within a single multi-frame object.Multiple ROIs were traced in each of the MR series, one for each structure, resulting in a labeling (segmentation) of image voxels belonging to the ROI. These segmentations were stored as DICOM Segmentation image objectsSeriesDescription of the segmentation object to include the SeriesDescription of the segmented image, followed by “Segmentations”. Formally, the ReferencedSeriesSequence attribute of each segmentation object contains a list of instance UIDs (ReferencedSOPInstanceUID) of the source MR images. Also, correspondence between the individual frames of the segmentation and the individual source MR image slices (identified by their SOPInstanceUID) is encoded in the SourceImageSequence of each of the segmentation frames in PerFrameFunctionalGroupsSequence.DICOM Segmentation image objects allow correspondence between the segmentations and individual MR images to be established in different ways. Informally, for the convenience of human users, we chose the A single DICOM Segmentation instance may contain one or more segments. We encoded the segments of all of the ROIs corresponding to a single type of MR image (series) in a study as a single segmentation instance, because all of those structures were traced by the same reader during the same contouring session. Thus for each of the imaging studies, there is a single segmentation instance for each of the analyzed MR series. It contains multiple segments, one for each ROI.SegmentSequence of the Segmentation IOD. To enable semantic interoperability of the data, DICOM uses codes from controlled terminologies to describe the anatomy and properties of segments. Such codes are defined as triplets consisting of the code value, the identifier of the coding scheme, and a human-readable meaning of the code. As an example, given the triplet , “T-D000A” is the code value, “SRT” indicates that this code belongs to the SNOMED CT terminology28, and “Anatomical Structure” is the human-readable code meaning. For historical reasons, DICOM currently uses the SNOMED ID rather than the Concept ID .Each segment encodes both the label assignment for the image voxels, and the structured metadata describing the content of the segment. Each segment’s metadata is described in an item of the SegmentedPropertyCategoryCodeSequence and SegmentedPropertyTypeCodeSequence. If the ROI defines a non-anatomical region, such as the extent of a lesion, the anatomic location of the segmented region is separately specified using the coded AnatomicRegionSequence attribute to define the location of the lesion, otherwise the anatomic location is used as the SegmentedPropertyTypeCodeSequence. The values we used for the individual segmented structures are summarized in For each of the MR series, the following ROIs were segmented: the whole prostate gland, the whole peripheral zone of the prostate gland, and the suspected lesion (if and when a lesion was identified by the expert reader in a given study). In addition, ADC maps were annotated with the ROI of the normal peripheral zone tissue. The semantics of the segment are recorded as a combination of two coded attributes: SegmentAlgorithmType attribute that in our case is always assigned value of “MANUAL” to indicate that a human performed the segmentation, and RecommendedDisplayCIELabValue with a CIELab color value for suggested display color for each segment.Additional standard segmentation metadata recorded includes PixelData attribute of the segmentation object as binary bit planes. We could have, but did not, reduce the size of each plane to a bounding box surrounding the object. We also could have, but did not, detect and exclude empty frames for the segment (corresponding to MR image planes outside of the ROI). The standard allows for either pattern. In our data, if the MR image series being annotated contains n instances (slices), each segment will also have n frames, each of which will have the same number of rows and columns as the image. Each of those frames is encoded using one bit per voxel, with values of one corresponding to the labeled voxels. Encoded frames for all of the segments are then concatenated to form the PixelData in a given Segmentation image instance. We did not use the Deflated Transfer Syntax to compress the resulting DICOM objects but simply encoded them in their uncompressed form. Encoding of the segmentations in the uncompressed form improves reusability of the resulting objects, since the specific compression scheme may not be supported by the recipient. Furthermore, the size of the segmentation objects in the uncompressed form is smaller than that of the segmented image series, limiting the practical need for compression.The segments are stored in the ImagePositionPatient between the MR images and the corresponding segmentations. This discrepancy is due to fact that our DICOM conversion process utilized representation of the image segmentation stored using volumetric research format (NRRD) as input. That volumetric representation defines image geometry using origin, spacing, and directions, and assumes regular sampling of the volume. Representation of the source MR image being segmented encodes image origin for each of the image slices individually. From our experience, it is not uncommon to observe discrepancies on the order of 0.001 between the encoded ImagePositionPatient and the one calculated assuming regular sampling. We deliberately decided to accept this discrepancy, since the tool used for annotating the data utilized volumetric representation of the data .We note that there are small numeric discrepancies  in the 24, in a manner similar to its previous application for encoding PET-derived measurements20. No non-standard attributes or content items were used.The ROI-based measurements are stored as DICOM Structured Reporting (SR) objects. DICOM SR organizes content hierarchically as a tree, each node of which is a content item. The constraints and required components of the hierarchy may be defined by a specific template. We followed the DICOM Measurement Report Template (TID 1500)39. Mean ADC quantity is , post-coordinated with the derivation , and units of .A single SR object was created for each annotated MR image series. Within one SR object, individual measurements are grouped by ROI within the Imaging Measurements container, into Measurements Groups, as defined by DICOM SR TID 1411 Volumetric ROI Measurements, a subordinate template included by TID 1500. Individual measurements within a group are accompanied by the coded concepts defining the quantity measured, and the units of measurement, see In addition to storing the individual measurements, each measurement group contains metadata described by the following coded concepts, as defined by the template  or part of the structure  or a lesion was measured. The values we used are summarized in “Source series for segmentation”: the SeriesInstanceUID of the DICOM MR image series that was segmented.“Referenced Segment”: the SOPInstanceUID of the DICOM segmentation object and the segment number defining the ROI used to perform the measurement.In addition to measurements derived from images, the SR document contains several other components specified in the template definition. Image Library container (defined by DICOM SR TID 1600) contains key content items describing the image from which the measurements were derived, and allows the SR to be understood in context without the need to dereference other objects in the dataset. Image Library content items include the imaging modality, geometric information for the individual image slices, and a coded information about the target region .As with other DICOM objects, the composite context of the DICOM SR objects is encoded at the attribute level (outside the SR content tree).13. Technical validation of the correctness of the conversion into the DICOM representation was confirmed as follows. First, we randomly selected a subset of encoded segmentations, and visually confirmed the consistency of the segmented ROIs as loaded from the DICOM representation and the non-DICOM format used for the data capture originally. For this purpose we used the 3D Slicer QuantitativeReporting module23 (see 41 (https://dicom4qi.readthedocs.io). Finally, we validated the compliance of the encoding of the objects with the DICOM standard by using the dciodvfy tool included in the dicom3tools software (http://www.dclunie.com/dicom3tools.html). SR objects were further validated using the com.pixelmed.validate.DicomSRValidator tool of the Pixelmed toolkit (http://pixelmed.com/#PixelMedJavaDICOMToolkit) to verify their compliance with the TID 1500 template structure.The dataset presented here was previously analyzed to evaluate repeatability of quantitative MR parameters The dataset is available in the limited-access QIN-PROSTATE-Repeatability collection of The Cancer Imaging Archive (TCIA) (Data Citation 1). Limited access was requested by the Institutional Review Board (IRB) of the Brigham and Women’s Hospital, requiring that interested parties apply for access and agree to the terms of use before accessing the data. The main reason for restricted access was in order to maintain the list of individuals accessing the dataset per the requirements of the BWH IRB.22 (http://slicer.org) with the QuantitativeReporting extension installed23. 42, MeVisLab (https://www.mevislab.de/), ePAD43, and several others discussed on our public DICOM4QI iteroperability connectathon web pages41 (https://dicom4qi.readthedocs.io). Conversion of the DICOM SEG format into any of the commonly used volumetric research formats supported by the Insight Toolkit44, such as NIfTI (http://nifti.nimh.nih.gov/) or NRRD (http://teem.sourceforge.net/nrrd/) can be done using converters from the freely available dcmqi software23. Individual metadata attributes can be extracted into tab-delimited tables linked by SOPInstanceUID using the freely available software (https://github.com/QIICR/dcm2tables). Individual DICOM files can be explored at the attribute level using dcmdump tool included in the open source DCMTK software45. DICOM SR documents can be examined at the level of the SR content tree using dsrdump tool of DCMTK. Both dcmdump and dsrdump tools are wrapped in the interactive dicom-dump package of the freely available Atom editor (https://atom.io/packages/dicom-dump).The dataset can be explored using a number of publicly available tools. Individual image series, segmented regions and the measurements corresponding to those regions can be visualized using 3D Slicer softwaread hoc, proprietary or other non-standard formats for communicating data derived from images. However, such approaches inevitably rely on domain- or site-specific conventions, formats and software (if available), which makes harmonization of different types of data and datasets obtained from different sources difficult if not impossible. Furthermore, such approaches do not meet the Findable, Accessible, Interoperable, and Reusable (FAIR)33 principles of data management. Examples of the non-FAIR aspects of research formats include: non-standard approaches to sharing data typically do not utilize unique identifiers for the derived datasets; they have limited capabilities and conventions for associating and encoding metadata with the data it describes; and they do not reuse existing structured terminologies to describe the metadata or its values, limiting semantic interoperability of the data. The need of wider utilization of the existing standards for data harmonization in medical research, including algorithm comparison challenges46, is widely recognized48. While understanding and adopting the DICOM format can be challenging in practice, it establishes a common framework for data management in the imaging domain. Although the use of DICOM for communicating the derived data objects shared in this data descriptor may seem excessive and overly burdensome, we argue that it maximizes interoperability of the data for machine readability, harmonization with other datasets, and the (unforeseen) future uses. There are no viable existing alternative approaches to encoding the types of data we share that are known to us that satisfy the FAIR guidelines, though there are some academic efforts that lay claim to the word “standard”. We stress that the supporting tools we have described, and others, are publicly available, which enable conversion of the shared DICOM datasets into alternative research representations to accommodate the needs of data consumers without native support of the relevant DICOM object types in their workflows. Of course, converting DICOM to research formats will often be lossy with respect to metadata if the research format provides no support to represent it.We note that contrary to the standardized approach we have taken here, it is common in the imaging research community to use dcmqi. One of the challenges in implementing the conversion process is the selection of the standard codes to describe the metadata concepts required by the standard representation. Ideally, those should be selected from existing ontologies to enable semantic interoperability of the data. The complexity of identifying such codes varies based on the specific use case. As an example, identifying codes describing human anatomy could be accomplished with the SNOMED-CT or FMA ontologies. However, identifying ontologies and terms for describing concepts that are exploratory in nature  can indeed be challenging. We note however, that DICOM permits the use of “private” coding schemes populated by the entity creating the document, which is straightforward, but limits the semantic interoperability of the data. The details on the selection of the terms is provided in the dcmqi documentation. The exact amount of effort required will obviously vary from dataset to dataset, but we would argue that the process can be expected to become easier over time as more datasets are published in the standard form. In addition we note that the process of identifying the proper metadata coding should be considered a necessary aspect of data publication to achieve FAIR representation, and should therefore be not a burden but a requirement.We believe the effort of creating and consuming standardized datasets, such as the one developed in the present data descriptor is considerably lower than it used to be with the availability of the open source tools discussed in the paper. The foremost requirement for creating a standardized DICOM representation of the analysis results is the availability of the source DICOM images being analyzed. Given such representation is available, the conversion process should be straightforward and can be done in batch mode using Investigative Radiology13 and does not describe the analysis framework used therein. The prior publication utilizes a commonly used framework for evaluating repeatability of measurement using Repeatability Coefficient and Intraclass Coefficient. Similar approaches have been used in other studies investigating repeatability of radiomics features , and can be applied to the presented dataset. We have also published initial analysis of radiomics features based on the presented dataset as a preprint, see Schwier et al.50 The framework for the evaluation of repeatability is thus well-established. We are not claiming that the dataset will allow comprehensive evaluation of the performance of an imaging biomarker. High repeatability does not imply good performance evaluating the disease or clinical utility. Rather, repeatability evaluation can serve as a component in the overall evaluation of a biomarker. The presented dataset alone cannot be used to make statistically-sound claims about performance of analysis approaches. Our intent is to provide a dataset that could supplement a larger dataset and support evaluation of an analysis method in a test-retest setting. While the dataset is small, this is due to the inherent difficulty in conducting test-retest studies . Other studies that investigated mpMRI repeatability in the prostate reported similar-sized cohorts: 8 in Gibbs et al.51, 10 in Litjens et al.9, 14 in Sadinski et al.11 Notably, neither of the aforementioned studies is accompanied by a publicly available dataset. In absence of evaluations that utilized larger cohorts, those small sample studies can still be valuable. As an example, claims stated in the Quantitative Imaging Biomarker Alliance (QIBA) Diffusion Weighted Imaging profile52 are based on the analysis of those small cohorts. As another example, the seminal study by Aerts et al.53 utilized the lung CT RIDER test-retest dataset consisting of 31 subjects as part of a supervised feature selection methodology. Examining proceedings of MICCAI 2018, the premier conference concerned with innovation in medical imaging computing, there are a number of studies that utilize cohorts of less than 20 subjects for evaluating models trained on larger populations. These are just some of the examples illustrating the value of datasets that are relatively small in size.The manuscript is a data descriptor that accompanies our earlier publication in 15, including in prostate cancer4, has recently been explored by a number of investigators. Stability of radiomic features is often used for unsupervised pre-selection of radiomic features in developing signatures of the disease53. Although there are publicly available test-retest datasets for such diseases as lung cancer14, we lack data on repeatability of radiomic features in prostate mpMRI . Computer-aided approaches for automated detection and characterization of PCa have been proposed54, but again, consistency of such automated processing results in a test-retest setting has not been evaluated. Furthermore, the dataset can be utilized to enable investigation and testing of new image computing tools that can support tasks related to image quantitation. As an example, it could be used to evaluate consistency of automated segmentation of the structures of interest , to evaluate the effect of automated image registration techniques on improving stability of the extracted quantitative measures, or to investigate how different approaches to normalization of the MR signal58 can affect the consistency of the radiomic features. Importantly, due to the nature of the study, although annotations were performed by an expert, we do not know the exact boundaries of the manually traced structures. As such, the dataset cannot be used to evaluate accuracy of PCa detection or precision of boundary localization, though it can be used to investigate consistency of segmentation across multiple readers in a secondary analysis. We also envision that it might be valuable to perform a study annotating the dataset with the both baseline and repeat images being available to the reader, which we expect should improve the consistency of localization of the boundaries of the annotated regions, potentially leading to the improved repeatability of the measurements.We envision a number of applications where the presented PCa dataset could be valuable. Characterization of disease using radiomic phenotypingWe note the dataset does not include the PI-RADS v2 interpretations of the findings, since the standardized machine-readable definition of PI-RADS v2 reports in DICOM is currently not available. We plan to augment the dataset with the structured reports encoding PI-RADS findings when such definition becomes available, but it is out of scope for the present data descriptor.How to cite this article: Fedorov, A. et al. An annotated test-retest collection of prostate multiparametric MRI. Sci. Data. 5:180281 doi: 10.1038/sdata.2018.281 (2018).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population . In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). The eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. This study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA's message and then enhance the related impact. Antismoking public service announcements (PSAs) have been proved to have success in reducing smoking habits in citizens  and thisA recent study investigated the relationship between fast-paced, evocative messages and message processing in the context of antidrug PSAs . It was In this scenario, efficient procedures for evaluating the effectiveness of antismoking messages could be a useful tool for designing public health campaigns. Encouragingly, research has shown that antismoking advertising can be successful in both adult  and younIn fact, it is important to understand the factors that could highlight Effective PSAs and, vice versa, to avoid features that could promote ineffectiveness in those PSAs, to develop efficient and cost-effective antismoking campaigns. Neuroscience techniques, rooted in consumer neuroscience studies, appeared a valid approach for achieving this goal. The antismoking PSA assessment can be performed through the study of the physiological and cerebral reactions to the exposure to the different kind of PSAs. Through such data collection techniques, several aspects related to the impact of commercial advertisings can be investigated with respect to target population's gender , 16, culBased on these considerations, we employed different indexes related to the cerebral and emotional reactions of the participants to the stimuli provided, which will be explained in the following paragraphs.On the basis of previous researches, which focused on antismoking campaigns evaluation  and theiConsidering that the characteristics of a health campaign  can influence its effectiveness , 36, we The aim of this study was to investigate the cerebral and emotional reaction to the exposure to selected antismoking PSAs in an adult sample. In particular, we hypothesized the following:The following paragraphs will explain the techniques and methodologies which allowed us to acquire the neurophysiological signal to develop the indexes for this article , during the observation of antismoking images divided into Effective and Ineffective. Successively, the statistical results obtained for these indexes will be shown and discussed, and this will allow to confirm or not the initial hypothesis.In this study, we employed different indexes related to the cerebral and emotional reactions of the subjects to the stimuli provided: the Effort, the Emotional, and the Visual Attention Indexes. In the following, we would like to describe the roots of such indexes in the already existing literature of the psychophysiological measurement of cognitive and emotional states, and how such indexes could be linked to the impact of the images related to Effective and Ineffective antismoking PSAs.It is known that the prefrontal cortex (PFC) plays a pivotal role in a cortical circuit involved in emotional and cognitive processes , 37. TheIt has been demonstrated that emotional involvement has long been acknowledged as an essential ingredient in the recipe for persuasion; in fact, the study of persuasion has often examined the various influencing roles of emotion, suggesting how emotion plays an especially significant role in healthy campaigns –56.x-axis, while the GSR is plotted on the y-axis, reflecting information concerning the stimuli' valence (positive or negative) and arousal (low or high activation)  Hz), in order to reject the continuous component as well as high-frequency interferences, such as muscular artefacts. To detect and remove components due to eye movement, blinks, the independent component analysis (ICA) procedure, in particular the SOBI algorithm , has beey domain . Thus, ty domain  some decTo evaluate the mental effort/processing, GFP from such frontal electrodes in theta band has been used. Thus, the obtained values have been standardized on the basis of the baseline (the IAPS images) EEG activity acquired at the beginning and at the end of the experiment. An increase in the frontal theta  would imply an increase in the task difficulty .x-axis reported the HR values, reflecting the valence dimension of a stimulus, while the y-axis reported the SCL values, mirroring the arousal dimension of a stimulus [Galvanic skin responses (GSR) and heart rate (HR) have been acquired with a sampling rate of 128 Hz through a NeXus-10  system. For these recordings, the electrodes were placed to the palmar side of the middle phalanges of the second and third fingers, on the nondominant hand of the participant, according to published procedures . Employistimulus . By matcstimulus . The EI Eye-tracking data have been collected in order to establish where the gaze is directed when looking at the selected stimuli. A remote eye-tracker has been used (Eye Tribe) with the sampling frequency set at 30 Hz. From the raw data collected, all the artifactual or nonphysiological points of gaze were automatically removed. Then, an identification of the fixations on the images has been performed with an I-DT (Identification by Dispersion Threshold) algorithm  that useFor each AOI, we obtained the Visual Attention (VA) Index that takes into account the percentage of total fixation duration (%TFD) for the selected AOI and the percentage of the area itself (%Area), calculated as in :(2)VA=%T%TFD can give us information about how much attention is given to a particular area of the image , but set-test has been used. The statistical analysis was performed with the Holm protection against the alpha inflation error possibly occurring for the execution of multiple comparison [Data obtained for the Neuroelectrical Indexes and for the VA were analysed with ANOVA test. The between variable were “Smoking Habit,” with two levels, Heavy Smokers (HS) and Nonsmokers (NS), and “Gender” with two levels, Female (F) and Male (M); the within variable was the “PSA kind,” with two levels, Effective and Ineffective. Duncan post hoc has been employed on the statistically significant results from ANOVA. Concerning the results for the EI, mparison .F (1.27) = 7.836 p < 0.05); the post hoc analysis highlighted an increase reported by HS group for Effective images when compared to HS for Ineffective ones (p < 0.05) and to NS for Effective ones (p < 0.05). Concerning the Effort Index, ANOVA showed a statistically significant effect for the interaction between the variables Smoking Habit and PSA kind (p=0.002) and between Effective and Ineffective (p < 0.05), which highlighted an increase of this index for HS participants and Effective PSA, as shown in Moreover, the statistical results showed a difference for the Effort Index value between HS and NS group (p < 0.014), as shown in The EI analysis showed a statistically significant effect for the interaction between Gender  and the PSA images (Effective and Ineffective): in particular, concerning Effective images, the EI in females showed the lower value compared to males , with higher levels for the Ineffective PSAs as shown in The VA analysis showed a statistically significant difference between the PSA kind , with higher levels in the female population; Concerning the “Gender” variable, the VA analysis showed a statistical difference in the perception between Female and Male subjects  and the different variables of sample (Gender and Smoking Habit).The aim of this paper was to evaluate the perception of adult sample of Effective PSAs compared to the Ineffective ones, on the basis of previous studies, which demonstrated the neurophysiological pattern existence in a young population sample during the observation of antismoking PSAs , evaluatResults related to the use of the VA Index suggest a tendency for the population to be more focused on the informative elements of the PSAs for the Ineffective ones. A possible explanation of such results is pointing at a poor delivery of the message, since the more an area of interest (AOI) is attended, the more the processing is made in order to grasp the information contained in it . As a maThe proposed study highlights also a different perception between the male and the female group in the population analysed: the female group attends more the informative parts of the selected PSAs, regardless of its kind. The presented stimuli put the sample population in front of a real and common problem: nicotine addiction and tobacco consumption, with related health and social problems. There is plenty of evidence in literature that suggest as males tend to avoid or deny health-related problems  or to hap=0.03).Concerning the Effort Index, results showed the highest value in the high smokers (HS) group during the observation of PSA Effective images. Such result could be explained by the peculiar content and style of the images potentially requiring a higher frontal theta for gaining the antismoking information. Petty and colleagues' study  demonstrp < 0.05). Specifically, the Effective PSAs selected for this paper, characterized by fear arousing appeal communication style, elicited higher cognitive processing of antismoking messages than the Ineffective ones, instead characterized by ironic and informative communication styles. These results are correlated with a previous study, which tested how the use of threatening and scary components in antitobacco messages increased mainly cognitive and emotional processes [Concerning the two PSA kinds (Effective and Ineffective), results showed a statistically significant difference between Effective and Ineffective (rocesses .p=0.014), according to literature, which highlighted a gender difference on the appreciation of TV advertisements [Concerning the EI, results showed a similar trend on the perception between PSA kind and Gender, where women showed a negative emotional involvement when exposed to Effective PSAs, while men showed a slightly positive emotional involvement. Statistical results highlighted a significant difference for Effective antismoking images between females and males , the occurrence of a peculiar neural activation in the prefrontal cortex in response to antismoking advertising. There have been many studies which have demonstrated that the average neural activity in the medial prefrontal cortex (MPFC) during messaging has been associated with future healthier behaviour change in individuals . Throug


Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model. Incremental sheet forming (ISF) is a flexible manufacturing technology that does not require special dies and uses a single tool to produce a variety of regular and multifaceted shapes. Further, it is economical when employed in the manufacturing of complex parts using simple tools as compared with other conventional sheet metal forming technologies , which requires a partial die as a support for the sheet during the process , and sinISF is used in many applications, such as the manufacturing of automotive parts  and the Therefore, it is essential to model and quantify the relationship between the forming force and the input process parameter affecting its value. Further, empirical models developed using traditional methods may not describe the nonlinear complex relationship between the input and output variables. Fuzzy logic (FL), an artificial neural network (ANN), and a genetic algorithm are unconventional methods used to develop models for a nonlinear complex system. An adaptive neuro-fuzzy inference system (ANFIS) can be used in numerous fields such as manufacturing technologies, machining, and economic systems . ANFIS iAn investigation into the forming forces in SPIF is particularly important for selecting the appropriate hardware and optimizing the process parameters to assure the precision of a process. The efficient prediction of the forming forces is desirable in order to monitor the forming process, prevent failures, and implement on-line process control. The characterization of the forming forces is essential in order to estimate the needed power of the machine. The expected forming force has consequences regarding the design of the tooling and fixtures, as well as on the selected machine. There has recently been an increasing interest in the development of models that can help investigate the effects of input variables on the performance outputs using artificial intelligence methods as an alternative to traditional approaches –31]. Th. Th31]. The rest of this paper is organized as follows: The experiments are presented in Section 2. The ANFIS, ANN, and regression models are presented in Sections 3 and 4. The results and discussions are detailed in Section 5. Finally, the conclusions are presented in Section 6.A vertical CNC milling machine, a specially designed fixture, forming tools, and a piezoelectric dynamometer were used to conduct the experiments. The sheet material selected for this study was a commercial aluminum alloy, AA1050-H14, which is a popular grade of aluminum for general sheet metal work owing to its excellent corrosion resistance, high ductility, and highly reflective finish. Further, the material composition, extracted using a SPECTRO machine, is presented in Measuring the forming force during this process is extremely important to prevent failure, determine the optimal process, and implement on-line control. Forming force tests were conducted using a KISTLER 2825A1 with eight freely selectable measuring signal-component force dynamometer controllers, which helped measure the force components in three directions . In addition, the measuring system included charge amplifiers (a complementary KISTLER 5019B three-channel charge amplifier) and data acquisition cards to record the measured forces on a PC. The sampling rate of the force measurement was 50 Hz. ANFIS is an effective approach to building models of complex nonlinear systems. Here, a hybrid learning process is used to structure an input–output mapping based on human knowledge and training data pairs. The ANFIS is applied in the framework of adaptive networks. It consists of five network layers. Each layer is described by several node functions. The information is moved unidirectionally. A diagram of the ANFIS structure with three inputs and two membership functions for each input and one output is shown in ANFIS consist of five layers to achieve the following fuzzy inference :Layer 1: Fuzzy layeri(x) is an appropriate parameterized membership function, and ai, bi, and ci form a parameter set that changes the forms of the functional movement screen  with a value between 1 and 0.In this layer, the membership value is calculated using the following equation:Layer 2: Multiplies the incoming signals and sends the product out.Each node output represents the firing strength of a rule.Layer 3: Normalizes the firing strengthsi denotes the output of layer i.In this layer, the normalized firing strength is computed using the following equation:Layer 4: Defuzzificationi, qi, ri, and si make up the consequent parameter set of the node, which are identified during the training process.In this layer, each node i is an adaptive node with a node function.Layer 5: Total output layerIn this layer, all incoming signals are added (summation output). The circle node function is fixed whereas the indicated square function is adaptive. This can be calculated as follows:The ANN computational model involves three layers, output, hidden, and input layers. Each layer contains neurons and each neuron is related to all the neurons in the next layer. None of the processes are executed in the input layer, and the input for the neuron is obtained from the actual setting. The input vector is the weight of a neuron multiplied by the strength; the result obtained helps create the product. The output from the last neuron can be interconnected to the input of the next neurons or can be directly interconnected with the environment. The output comprises an activation function and a summation function. The activation function takes the weight of a neuron as an input and produces its activation as an output. The calculation of the net input from the processing neurons is the summation function. Using the ANN, the nonlinear relationships between the output and input owing to the contained activation function of the nonlinear and linear algebraic equations can be stored. After the weight is altered by the activation function, the neurons that have moved to other neurons make up the next layer. The output of the activation function accepts the results, and then presents them to either the external network or to the neurons in the next layer. The network output is compared with the target having the applied input, and the difference between them is then considered an error. Moreover, algorithms of different networks are applied to decrease the error .An analysis of variance (ANOVA) was used to estimate the effects of all factors and their interaction on Fz. As a standard practice in ANOVA, terms with a p-value < α = 0.05 are considered significant. The ANOVA results, presented in The ANFIS model was developed as a function of SPIF for the forming force using training and testing data. The ANFIS tool that already exists in MATLAB was applied, which tests the relationship of the process parameters used to execute the perfect training and maximizes the prediction model accuracy for the selected responses (forming force). To obtain the results, the ANFIS algorithm was designed using the initial parameters. The training process was applied using 100 epochs for the forming force on three axes . A training curve was obtained after the training process was complete, as shown in An analysis of the curves shows that, after 35 epochs, the errors become steady, as shown in During the training process, FIPs were repeated multiple times until the errors were minimized. Different ANFIS parameters were used as the training parameters to validate the accuracy of the prediction model. The results of the developed ANN model are used to predict the forming force based on the input process parameters in single-point incremental sheet metal forming. The numbers of training and testing samples are 28 and 6, respectively.Several training experiments were carried out to identify the optimal network structure and best training parameters of the neural networks, producing minimum errors during the training phase. Similarly, several training experiments with different numbers of hidden neurons, learning rates (0.60), and momentum values (0.80) were checked, as shown in A regression analysis helps in the development of a mathematical equation to characterize the relationship between two or more input variables and the response outputs. In this study, mathematical models are also developed using a regression analysis to fit the measured data for the three selected responses. Using Minitab software, regressions models for the forming force were developed, and a full quadratic model was initially selected for all responses. Later, the insignificant terms were removed based on their p-values and accuracy. The following equations can be used to predict the forming force as a function of significant factors:To assess the ability of the developed ANFIS model relative to that of a neural network and regression analysis, an ANN model and a regression algorithm were developed using the same input variables. The results obtained using the ANFIS prediction are very close to the measured values. Moreover, the absolute mean percentage errors were calculated for each of the developed models. Tables Based on the performances of the ANFIS and ANN models in terms of the average absolute percentage error for the training and testing data, it was observed that the ANFIS model outperforms the ANN and regression models, while retaining their full potential.This paper proposed ANFIS and ANN models to predict the forming force in the context of sheet metal forming, particularly SPIF. In addition, the influences of the tool diameter, feed rate, sheet thickness, and step size on the main forming force were investigated. Considering the ANOVA for the forming force (Fz), it was concluded that the significant factors are the tool diameter, step size, and sheet thickness. The results of the ANFIS and ANN models were compared with both the experimental data and those predicted using a regression model. The comparison showed that the ANFIS model can accurately predict the forming force for both training and testing data; in addition, the ANFIS model exhibited a better prediction performance for the selected responses. Moreover, the results showed that the ANFIS model can predict the forming force along the three axes for the training data with a MAPE of 7.25%, 6.42%, and 8.98%, respectively, and for the testing data with a MAPE of 5.85%, 9.61%, and 15.44%, respectively. It can therefore be concluded that the developed model using the ANFIS approach can be effectively used to measure the forming force during ISF and provide more reliable results than the ANN and regression models.


Light Touch (LT) has been shown to reduce postural sway in a wide range of populations. While LT is believed to provide additional sensory information for balance modulation, the nature of this information and its specific effect on balance are yet unclear. In order to better understand LT and to potentially harness its advantages for a practical balance aid, we investigated the effect of LT as provided by a haptic robot. Postural sway during standing balance was reduced when the LT force (~ 1 N) applied to the high back area was dependent on the trunk velocity. Additional information on trunk position, provided through orthogonal vibrations, further reduced the sway position-metric of balance but did not further improve the velocity-metric of balance. Our results suggest that limited and noisy information on trunk velocity encoded in LT is sufficient to influence standing balance. In Light Touch (LT), small interaction forces on a fingertip have been shown to improve human postural stability during standing –3. TouchDue to the simplicity in providing LT and its effectiveness in improving standing stability, LT is believed to have the potential to become a useful balance aid . The genUnfortunately, traditional and widely used LT experimental methodologies may be inadequate to investigate how LT leads to improved stability, often in terms of reduced postural sway –10. In tIndeed, Riley  argued tTo clarify the cause-effect relationship between LT and balance as well as to maintain the consistency of tasks in LT experiments, externally modulated LT may be utilized. Johannsen  studied More importantly, an experimental advantage is that IPT does not involve a supra-postural task for the perceiver, so any reductions in sway are true benefits. How then does the information contained in externally modulated LT compare to traditional paradigms? It is unclear from  how IPT Our objective in this study is to determine if a specific information about balance, encoded within the light interaction force, reduces postural sway. To study this, it requires the LT to be applied and modulated externally, similar to how the LT was applied externally in Johannsen et al. , but preThe main hypothesis of this work is that LT reduces postural sway by providing additional information about one’s sway itself. That is, the IPT in Johannsen et al.  ..Fig 2). Our haptic robot also does not supply a constant force to the participant due to friction and damping. The effect of damping forces that depend on the velocity appears similar to the effect of time delays in harmonic motion . Also, tTo provide sway position-dependent information with externally applied LT, we note that by just by having our robot to lightly touch the participant, we inevitably provide the sway-velocity information to the participant due to static friction in the robot joints. That is, it is impossible to provide only sway position-dependent interaction forces without also providing sway velocity-dependent interaction forces. To mitigate this issue, we created another condition in which the haptic robot augments the LTR condition with a position-dependent vibration (LTR+V), similar to the vibrotactile feedback , 18. In To test the effect of velocity and position information on balance, the CoP data from the LTR and LTR+V conditions were compared to a baseline condition where no force was applied to the participant (NF). In NF condition, the robot did not touch the participant and no external additional information on body sway was provided to the participant.Ten participants aged between 19 and 27 years , participated voluntarily in the study. The individuals were healthy with no self-reported neuromuscular injuries or balance disorders. Prior to the experiment, all participants gave written consent as approved by the Missouri S&T Institutional Review Board (IRB). The participants were first made aware of the three experimental conditions, NF, LTR, and LTR+V. In all conditions, the participants were given identical instructions to stand as quietly as possible with their eyes closed. Instead of the light clothing in Johannsen et al. [Each participant underwent 12 trials of each condition for a total of 36 trials per participant. These 36 trials were block-randomized into 3 blocks of 12 trials, where each block contained 4 trials of each condition. The first block was intended to make the participants accustomed to the LT by a robotic manipulator, whereas the second and third blocks were intended to capture the effect from conditions with sufficient number of trials. Each trial lasted 20 seconds [The ground reaction forces and moments of each participant were processed using MATLAB  to obtain the CoP data, which was smoothed with a moving average filter using a 100 ms window. Then, the anterior-posterior standard deviation of CoP (SD CoP) was obtained . Also, tp < 0.05.To exclude possible learning effects, data from block 1 were not analyzed. In addition, to account for the high skew of the SD CoP and SD vCoP, statistical analyses were performed on the natural log transforms of these metrics instead (log(SD CoP) and log(SD vCoP)). The preliminary analyses leading to these decisions are presented in the results section. We used SPSS  to perform ANOVA with both participants and conditions as fixed factors. Tukey HSD was used to test the significance of the difference at In order to confirm the expected velocity-dependence of light interaction force from the robot in the LTR condition, a separate, single-trial experiment was conducted. While the haptic robot was set to the LTR condition, an experimenter gently pushed on to the end-effector while moving forward and backward multiple times to mimic the natural sway. The linear regression coefficient was found from the measured force-velocity relationship to verify the velocity-dependence.The means and the standard deviations of SD CoP, as well as SD vCoP, are shown in We first looked for possible learning effects in our three conditions. While there were no significant differences between the blocks for the NF or LTR conditions, block 1 was significantly worse in the LTR+V condition for both SD CoP (p < 0.001) and SD vCoP (p < .0001), potentially indicating that it took participants longer to get acclimated to this condition. For this reason, we excluded all of the data from block 1, and ran all subsequent analyses using blocks 2 and 3.Before the natural log transform, SD CoP showed skew of 1.66 and excess Kurtosis of 3.98, indicating highly asymmetric data distribution. Similarly, SD vCoP was highly asymmetric with skew of 2.26 and excess Kurtosis of 7.61. However, after log transform, both log(SD CoP) and log(SD vCoP) became much more symmetric, with both skew  and excess Kurtosis  being within their acceptable ranges for statistical analysis using ANOVA. The resulting metrics of balance in this study are shown in Sway variability was reduced due to the light interaction force applied by the haptic robot . The log2 = 0.79). The positive velocity of the contact point resulted in a lower magnitude of the contact force, whereas the negative velocity resulted in a larger contact force. The interaction force was also correlated to the position of the contact point but with comparably weaker dependency (R2 = 0.51).Despite inconsistencies in force mapping, the interaction force applied by the haptic robot in the LTR condition was highly velocity-dependent  of the movement, or the sign of the velocity, extremely fast and accurately. By definition, friction always resists movement. Because of this, the friction in the robot joints causes the pushing force to always decrease due to a movement away from the robot (forward sway velocity) and vice versa, providing accurate information about the direction of the movement at that exact time. In fact, if it is the only the sign (+ or -) of the velocity that we need, friction is more accurate and faster than any conceivable electromechanical realizations of a velocity sensor. Velocity derived either as the rate of change of position or the time integration of the acceleration suffers from noise and resolution in the measurement as well as the sampling time of digital circuitry, especially near zero velocity. In this regard, it is ironic that our “imperfect” haptic robot was in fact “perfect” in providing the direction of movement to the participants.In the presented experiment, the instructed goal of our participants was to remain as quiet and stable as possible. That is, the participants were not instructed to pay attention to the interaction force. This was similar to the instruction given to the participants in  touchingA distinguishing characteristic of our experimental design is that it allows for separating the LT provider (robot) and LT receiver (human participants). A notable benefit of using externally modulated light touch is that it makes it easier to characterize what information the participant is receiving and how. Unlike in traditional LT experiments where a participant both applies and receives LT , 2, ExteA fingertip resting on a fixed surface could provide information about the current position of the trunk relative the finger, but it could also provide information about relative velocity or acceleration, and it is not clear which variable(s) are most useful for reducing sway. This observation leads to deeper questions regarding how the abstract concept of balance should be measured and quantified. One intriguing result is that the LTR+V condition was able to provide an improvement in log(SD CoP) but not log(SD vCoP), potentially suggesting separable cognitive modulation of balance that selectively affects these variables   and the CoP of the support forces against the feet . This reNonetheless, each of these measures has practical limitations to their usefulness , 38. CoPWith regards to the position information provided in the LTR+V condition, it should be noted that although the light touch signal contains information about both the magnitude and direction of velocity, the vibrational component is identical for positions in front of and behind the center point, and thus only carries information about the magnitude and not direction. Future work could experiment with employing different directions of vibration  depending on the sign of the displacement. It is speculated that these directions should be orthogonal to the direction of the velocity-dependent LT (fore-aft), because otherwise, the position-information would interfere with the velocity-information encoded in the LT. However, the efficacy of such differentiated vibration would depend on the participant’s ability to distinguish vertical versus mediolateral vibration at the touch location.


Precise characterization of reinforcing bars (rebars) in a concrete structure is of significant importance for construction quality control and post-disaster safety evaluation. This paper integrates ground-penetrating radar (GPR) and electromagnetic induction (EMI) methods for simultaneous estimation of rebar diameter and cover thickness. A prototype of GPR-EMI dual sensor is developed, and a calibration experiment is conducted to collect a standard EMI dataset corresponding to various rebar diameters and cover thicknesses. The handheld testing cart can synchronously collect both GPR and EMI data when moving on the concrete surface, from which a data processing algorithm is proposed to simultaneously estimate the rebar diameter and cover thickness. Firstly, by extracting the apex of the hyperbolic reflection from the rebar in the preprocessed GPR profile, the rebar position is determined and further used to extract the effective EMI curve. Then, the rebar diameter and cover thickness are simultaneously estimated from the minimum mean square error between the measured and calibrated EMI data under the constraint of the GPR-estimated cover thickness. A laboratory experiment is performed using four casted concrete specimens with 11 embedded steel rebars. The results show that the diameters of 10 rebars are correctly estimated out of the 11 rebars, and the maximum estimation error for the cover thickness is 6.7%. A field trial is carried out in a newly-constructed building, and the diameters of four tested rebars are all accurately estimated while the estimation errors of the cover thickness are less than 5%. It is concluded that the developed GPR-EMI dual sensor and the proposed algorithm can estimate the rebar diameter and cover thickness accurately by a single scan. Non-destructive testing (NDT) techniques play a significant role in monitoring and diagnosing construction structures. Accurate characterization of the properties of reinforcing bars (rebars) in concrete structures is critical for the quality control during the construction phase, as well as for health monitoring and post-disaster safety evaluation during the operation phase . The parElectromagnetic induction (EMI) is the principle of most of the commercially available rebar locators and cover meters . An EMI Ground-penetrating radar (GPR) is another important NDT method based on the propagation and scattering of high-frequency electromagnetic (EM) waves. It has been successfully applied to utility detection , pavemenEMI is sensitive to both the depth and size of metallic buried objects. However, it is difficult to simultaneously and accurately obtain the two unknowns in a direct manner. GPR has a high sensitivity on the buried depth rather than the size of the objects, whereby a straightforward estimation of the buried depth is readily done by a time-depth conversion . ConsideIn order to carry out a synchronous measurement of both EMI and GPR data, a novel dual-sensor system has been developed . Figure While operating, the device is placed on the concrete surface, and moves across the potential rebars. Radar and EMI sensors implement synchronous scanning, and both sets of data are displayed on the display in a real-time pattern. The calibration of rebar data by EMI sensing is carried out using a customized Cartesian-coordinate scanning frame installed above a sandpit as shown in In our calibration experiments, 11 steel rebars are chosen as the calibration samples referring to the industrial construction standard in China . The rebD = 20 mm) with varying rebar diameters and a fixed rebar diameter (R = 20 mm) with varying cover thicknesses, respectively. It can be seen that all the EMI curves exhibit a pulse response on the buried rebars. The signal strength raises proportionally with the increase of the rebar diameter, while drops exponentially with the increase of the cover thickness, reflecting a high sensitivity of EMI signal to both the rebar diameter and cover thickness. The amplitude of the peak of each EMI curve is picked up, and its variations with the rebar diameters and cover thicknesses are shown in The calibration process records 616 EMI response curves corresponding to the eleven rebar diameters and 56 cover thicknesses. The data processing algorithm for simultaneous estimation of rebar diameter and cover thickness from the GPR and EMI data is depicted in (a)The GPR data is preprocessed to increase the signal to noise ratio. A sequence of standard GPR processing techniques are implemented, including DC removal, zero-time correction, band-pass filtering, amplitude scaling, median filtering, and background removal .(b)The hyperbolae from the buried rebars in the GPR profiles are extracted by an edge detection algorithm after pre-processing, and their apex coordinates are picked up for locating the rebar and roughly estimating the cover thickness, as shown in The detailed information is described as follows:(c)Localization of the buried rebar and extraction of the effective EMI curve. From the horizontal coordinate of the detected hyperbolic apexes, the horizontal location of buried rebar can be accurately determined, which is further used to extract the effective EMI curves from the embedded rebar. The effective EMI curve centers on the rebar location with the span defined as twice of the FWHH, and it contains the majority of the useful information in the EMI response while stays away from the noise level. The EMI amplitude, with the location corresponding to the hyperbolic apex of the GPR profile, is used to judge whether the GPR hyperbola is reflected by a rebar or a plastic pipe. If the EMI amplitude is close to the noise level, the reflective object is judged as non-metallic; otherwise, it is judged as a metallic rebar.(d)t) of the hyperbolic apex, the two-way travel time of EM waves propagating from the air-concrete interface to the embedded rebar is used to roughly estimate the cover thickness by:D is the cover thickness (m), v is the EM wave velocity in the concrete (m/ns), and t is the two-way travel time (ns). The velocity is calculated by:c is the EM wave velocity in free space, i.e., 0.3 m/ns, and εr is the relative permittivity of the concrete. Taking the heterogeneity of the concrete compositions and the intrusive moisture into account, we assign εr ranging from 4 to 10 [Pre-estimation of the cover thickness. Knowing the time coordinate f(x) is the in-situ measured EMI curve, i and j are respectively the serial numbers standing for various rebar diameter and cover thickness in the calibration experiment, x is the horizontal coordinate of the EMI curve, l is the intercept length of the extracted effective EMI curve, which is twice of the FWHH as stated above, and MSE is the mean square error. The search of the local minimum MSE is implemented under the constrained scope of the cover thicknesses that are estimated by GPR data in the previous step. This constraint condition avoids multiple solutions of rebar diameter and cover thickness, and thus can improve the estimation accuracy.Determination of the rebar diameter and cover thickness. Through calculating the mean square errors between the EMI data extracted from the field measurement and those calibrated in the laboratory in advance (as stated in the previous section), the rebar diameter and cover thickness are simultaneously estimated through searching the local minimum mean square error under the constraint of the GPR-estimated cover thickness. The error function is expressed by:The upper and lower two arc trajectories correspond to the two troughs in the GPR reflection waveforms, and the apex of the upper one is used for the rebar localization.To check the proposed method for simultaneous estimation of rebar diameter and cover thickness, we conducted a laboratory measurement with the developed GPR-EMI device using four concrete specimens embedded with rebars.In the laboratory experiment, four concrete specimens were casted and eleven rebars with different diameters were embedded inside. The specimens were poured with the ordinary Portland concrete, and the dimensions are 1000 mm × 250 mm × 150 mm. The rebars, labelled from #1 to #11 with the diameters ranging from 6 to 28 mm, were buried in the concrete specimens with different cover thicknesses, as shown in MSEs between the measured and calibrated EMI curves using Equation (3). MSEs obtained from the rebar labelled #5, whose actual diameter and cover thickness are 14 and 35 mm, respectively. From the hyperbolic reflection of the rebar in the GPR profile in MSE map for accurate estimation of the rebar diameter and cover thickness. If taking no account of the cover thickness limits, the rebar diameter and cover thickness are estimated to be 10 mm and 23 mm by the global minimum MSE, as marked in MSE, as marked in From the hyperbolic reflections of the eleven embedded rebars, the rebar locations are determined by the aforementioned edge detection algorithm, and the corresponding effective EMI curves are extracted. For each measured EMI response, we extract the effective EMI curve and calculate a map of We estimated the diameter and cover thickness of the eleven rebars in the four specimens, and analyzed the errors between the estimated and true values, as shown in To further verify the effectiveness of our device in testing practical concrete structures, we chose two reinforced concrete columns in a newly-completed building to conduct a field trial. The columns have a cross-section of 0.8 m × 0.8 m. A quick-scan was implemented to locate the horizontal and vertical rebars. To mitigate strong interference from the neighboring rebars, we set a horizontal survey line between two horizontal rebars, of which the spacing is 15 cm. In this paper, we propose the integration of EMI and GPR for simultaneous estimation of rebar diameter and cover thickness, which is of significance for quality control and safety evaluation of concrete structures. A prototype of GPR-EMI dual sensor has been developed, and a standard set of EMI data for calibration has been recorded using eleven rebars of different diameters buried at different depths in sand. The developed rebar detection device can synchronously record a GPR profile and an EMI response curve by a single scan in a handheld moving manner. The main contribution of this paper is the development of a data processing method for simultaneous estimation of rebar diameter and cover thickness. From the GPR data, a buried object is located and its cover thickness is roughly estimated from the apex of the hyperbolic reflection. The corresponding EMI data is extracted according to the GPR-determined location, and the detected object can be interpreted as a rebar or a plastic pipe by the EMI amplitude. The GPR-estimated cover thickness range is used as a constraint for further estimation of the rebar diameter and cover thickness by calculating the mean square errors between the measured and calibrated EMI data. A laboratory experiment demonstrates that integrating GPR and EMI data can greatly enhance the estimation accuracy. The field experiments on two concrete columns show that both the rebar diameter and cover thickness can be accurately estimated. We conclude that the developed EMI-GPR dual sensor can have a promising prospect in the practical NDT of concrete structures. The method of integrating GPR and EMI data can also be used for estimation of the diameter and buried depth of other cylindrical conductive objects, such as a metal pipe.One of the limitations of the developed system and algorithm is that it is still difficult to implement an effective measurement and estimation of a rebars in a densely-meshed rebar net, where the GPR and EMI signals from the neighboring rebars severely interfere with each other. An attempt will be made to use advanced signal processing algorithms, and another attempt will aim to improve the system performance and enhance the directivity of the sensors. It is worth noting that the nominal frequency (corresponding to the bandwidth) of the pulse GPR has a significant impact on its resolution. The larger the bandwidth of the transmitting pulse is, the thinner the hyperbolic trajectory is, resulting in a more precise hyperbola extraction with Sobel operator as well as a more accurate cover thickness estimation in the following step. Thus, we are currently attempting to develop a prototype with a higher GPR center frequency of 2.6 GHz. In addition, the data processing is time- and labor-consuming with the increasing demands of field tests. We think artificial intelligent algorithms, such as deep learning, may have the potential in improving the efficiency of data analysis and processing.