Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain. The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM image information objects were selected from datasets published in TCIA. Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams. The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation, and de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification.

MG Anonymized Medical Imaging Dataset (DICOM) Overview This dataset contains a collection of anonymized MG medical imaging studies in DICOM format. DICOM (Digital Imaging and Communications in Medicine) is the international standard for transmitting, storing, and displaying medical images and associated metadata. This dataset is intended for educational, research, and development purposes in the fields of medical AI

• Intended Use: This dataset is provided for non-commercial research. By using this dataset, you agree to use it ethically and responsibly, in accordance with Kaggle's rules and for the advancement of healthcare research.

Dataset Details - Format: All files are in standard DICOM (.dcm) format.

This dataset is not as conveniently organized on the filesystem, rather, this dataset looks like something that you are likely to get as a raw dump from a clinical data archive. It contains 2 imaging modalties (CT and MR) for different instances.

The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying various TCIA breast cancer collections. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data was imported into I2B2 and cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 to CSV and thence converted to DICOM SR according to the the DICOM Breast Imaging Report template [1] , which supports description of patient characteristics, histopathology, receptor status and clinical findings including measurements. The purpose was not to advocate DICOM SR as an appropriate format for interchange or storage of such information for query purposes, but rather to demonstrate that use of standard concepts harmonized across multiple collections could be transformed into an existing standard report representation. The DICOM SR can be stored and used together with the images in repositories such as TCIA and in image viewers that support rendering of DICOM SR content. During the project, various deficiencies in the DICOM Breast Imaging Report template were identified with respect to describing breast MR studies, laterality of findings versus procedures, more recently developed receptor types, and patient characteristics and status. These were addressed via DICOM CP 1838, finalized in Jan 2019, and this subset reflects those changes. DICOM Breast Imaging Report Templates available from: http://dicom.nema.org/medical/dicom/current/output/chtml/part16/sect_BreastImagingReportTemplates.html

Brain MRI Dataset, Normal Brain Dataset, Anomaly Classification & Detection

The dataset consists of .dcm files containing MRI scans of the brain of the person with a normal brain. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 7 studies, made from the different angles which provide a comprehensive understanding of a normal brain structure and useful in training brain anomaly classification algorithms.

  MRI study angles… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/dicom-brain-dataset.

Discover Our Extensive Dataset: Thoracic CT Scans – Comprehensive Lung Analysis

We are thrilled to introduce our expansive dataset, featuring over 1 million high-quality DICOM files from thoracic CT scans. This dataset is meticulously designed to support researchers and developers in advancing the understanding of lung diseases and enhancing diagnostic accuracy using AI and ML technologies.

Access the Dataset

This is a limited preview of our thoracic CT scan dataset. For full access to our comprehensive collection, please visit HumanAIzeDATA to discuss your specific needs and pricing options. Feel free to reach out directly at ✉ contact@human-ai-ze.com.

Dataset Content

• Over 1 million high-quality DICOM files, providing a detailed look at various thoracic conditions and pathologies.

Technical Specifications

• Details about the CT equipment used, including scanner models, settings, and configurations, ensuring high standards of image clarity and detail.

About HumanAIzeDATA

At HumanAIzeDATA, we specialize in high-quality datasets for AI/ML projects in the medical field. Our datasets are designed to meet the exacting standards of clinical research and application development.

Why This Dataset?

• Focused on Thoracic Health: Tailored for projects targeting pulmonary diseases and chest-related medical studies.
• Ready for AI: Ideal for developing algorithms for automated diagnosis, segmentation, and monitoring of thoracic diseases.
• Supported by Experts: Curated with the input from medical professionals to ensure clinical relevance and accuracy.

How You Can Use This Dataset

• Developing predictive models for early detection of lung conditions.
• Enhancing existing medical imaging software with robust features for detailed thoracic analysis.
• Academic research in pulmonary medicine, radiology, and machine learning applications in healthcare.

Remember, accessing our datasets involves agreeing to our terms of use, ensuring that the data is used ethically and responsibly.

We are excited to see how our thoracic CT scan dataset can contribute to groundbreaking projects and advancements in medical technology. For any inquiries or to get started, please don’t hesitate to contact us!

This dataset contains DICOM versions of the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.

NRRD versions can be found in http://dx.doi.org/10.5281/zenodo.20766 (doi:10.5281/zenodo.20766).

The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

Note : The TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules) of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.

This dataset contains hundreds of ultrasound (USG) cases collected from hundreds of unique patients, representing a wide spectrum of real-world clinical scenarios. Each case is stored in DICOM format, preserving full-resolution imaging data along with essential metadata for medical research and diagnostic model development.

🏥 Data Type: Ultrasound images across various anatomical regions and clinical indications.

📁 Format: Standard DICOM files with embedded metadata.

👥 Patients: Diverse set of patients, covering multiple age groups and both genders.

📊 Use Cases: Suitable for medical imaging research, AI diagnostic model training, anomaly detection, and clinical decision support development. Link to download : https://huggingface.co/datasets/AIxBlock/USG-medical-dataset-dicom-format

This dataset is intended to support the medical AI research community in advancing ultrasound-based diagnostics and building high-quality, real-world AI models.

This database provides a collection of myocardial perfusion scintigraphy images in DICOM format with all metadata and segmentations (masks) in NIfTI format. The images were obtained from patients undergoing scintigraphy examinations to investigate cardiac conditions such as ischemia and myocardial infarction. The dataset encompasses a diversity of clinical cases, including various perfusion patterns and underlying cardiac conditions. All images have been properly anonymized, and the age range of the patients is from 20 to 90 years. This database represents a valuable source of information for researchers and healthcare professionals interested in the analysis and diagnosis of cardiac diseases. Moreover, it serves as a foundation for the development and validation of image processing algorithms and artificial intelligence techniques applied to cardiovascular medicine. Available for free on the PhysioNet platform, its aim is to promote collaboration and advance research in nuclear cardiology and cardiovascular medicine, while ensuring the replicability of studies.

Data associated with manuscript:
The Australasian dingo archetype: De novo chromosome-length genome assembly, DNA methylome, and cranial morphology

Raw Dicom data, Alpine Dingo brain (zip) and domestic dog brain (zip). Brains were scanned using high-resolution magnetic resonance imaging (MRI). A Bruker Biospec 94/20 9.4T high field pre-clinical MRI system located at the Biological Resources imaging Laboratory University of New South Wales (UNSW) was used to acquire MRI data of a fixed dingo and domestic dog brain. The system was equipped with microimaging gradients with a maximum gradient strength of 660mT/m and a 72mm Quadrature volume coil. Images were acquired in transverse and coronal orientation using optimized 2D and 3D Fast Spin Echo (FSE) and Gradient Echo (MGE) methods. Image resolution was 200x200x500 and 300x300 microns isotropic for type 3D and 2D pulse sequences, respectively.

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: CCDI-MCI. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

The Molecular Characterization Initiative (MCI) [2] is a component of the National Cancer Institute’s (NCI) Childhood Cancer Data Initiative (CCDI). It offers state-of-the-art molecular testing at no cost to newly diagnosed children, adolescents, and young adults (AYAs) with central nervous system (CNS) tumors, soft tissue sarcomas (STS), certain rare childhood cancers (RAR), and certain neuroblastomas (NBL) treated at a Children’s Oncology Group (COG)–affiliated hospital. The goal of MCI is to enhance the understanding of genetic factors in pediatric cancers and to provide timely, clinically relevant findings to doctors and families to aid in treatment decisions and determine eligibility for certain planned COG clinical trials.

The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled in EveryChild APEC14B1 protocol.

Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3,4] using custom open source scripts and tools as described in [5]. The resulting converted images were released in IDC in the CCDI-MCI collection with the IDC data release v19.

To learn how to access related clinical and genomic data accompanying this collection please see the CCDI-MCI page and CCDI Hub.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. ccdi_mci-idc_v19-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. ccdi_mci-idc_v19-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. ccdi_mci-idc_v19-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).

[2] https://www.cancer.gov/research/areas/childhood/childhood-cancer-data-initiative/programs/molecular-characterization

[3] National Electrical Manufacturers Association (NEMA). DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD. at <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8>

[4] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018).

[5] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: Initial release. (Zenodo, 2023). doi:10.5281/ZENODO.8240154

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: NLM-Visible-Human-Project. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

The NLM Visible Human Project [2] has created publicly-available complete, anatomically detailed, three-dimensional representations of a human male body and a human female body. Specifically, the VHP provides a public-domain library of cross-sectional cryosection, CT, and MRI images obtained from one male cadaver and one female cadaver. The Visible Man data set was publicly released in 1994 and the Visible Woman in 1995.

The data sets were designed to serve as (1) a reference for the study of human anatomy, (2) public-domain data for testing medical imaging algorithms, and (3) a test bed and model for the construction of network-accessible image libraries. The VHP data sets have been applied to a wide range of educational, diagnostic, treatment planning, virtual reality, artistic, mathematical, and industrial uses. About 4,000 licensees from 66 countries were authorized to access the datasets. As of 2019, a license is no longer required to access the VHP datasets.

Courtesy of the U.S. National Library of Medicine. Release of this collection by IDC does not indicate or imply that NLM has endorsed its products/services/applications. Please see the Visible Human Project information page to learn more about the images and to obtain any supporting metadata for this collection. Note that this collection may not reflect the most current/accurate data available from NLM.

Citation guidelines can be found on the National Library of Medicine Terms and Conditions information page.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. nlm_visible_human_project-idc_v15-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. nlm_visible_human_project-idc_v15-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. nlm_visible_human_project-idc_v15-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180

[2] Spitzer, V., Ackerman, M. J., Scherzinger, A. L. & Whitlock, D. The visible human male: a technical report. J. Am. Med. Inform. Assoc. 3, 118–130 (1996). https://doi.org/10.1136/jamia.1996.96236280

This upload contains the same data as published in our previous zenodo dataset upload. Unlike our previous upload, this version contains data after transferring the DICOMs directly from the Siemens Skyra 3T to our Linux machine (as done in real-time experiments). The purpose of this separate upload is to serve as sample data for our real-time cloud software, for a specific sample project. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

Imaging Data Commons (IDC) is a repository within the Cancer Research Data Commons (CRDC) that manages imaging data and enables its integration with the other components of CRDC. Further details about IDC are available in this publication . IDC hosts a growing number of imaging collections that are contributed by either funded US National Cancer Institute (NCI) data collection activities, or by the individual researchers. Image data hosted by IDC is stored in DICOM format. This public dataset consists of the following components: 1. BigQuery Metadata tables : these include DICOM metadata attributes extracted from the DICOM data into BigQuery tables, which are further enriched by including collection-level metadata that is not available in DICOM. 2. DICOM files : these files are available in Storage buckets. This dataset is rather large (~40TB), and is updated monthly, which makes is challenging to download all of the files. Instead, users should utilize the BigQuery tables to search and identify files of interest, which then can be downloaded selectively from Cloud Storage. Please see the download instructions page in Imaging Data Commons documentation: https://learn.canceridc.dev/data/downloading-data See further details about data organization in IDC documentation .

Explore the booming Medical Image Sharing Solution market, driven by cloud adoption and AI integration. Forecasted to reach USD 12,500 million by 2025 with a 15% CAGR, discover key drivers, restraints, and regional trends.

DICOM files are given for 11 CT scans which were used in a research article. Each scan contains about 1250 slices with 512x512 gray scale images, each in its own directory. The low number slices contain the diapers in the order D5 ... D1, then the vials of powder are contained in the order V11 ... V2. The symbols correspond to the injected masses which are given in the paper and which are repeated here in a file called "mass.txt". There is also a file "README.txt" which describes the directory structure.

This deidentified imaging dataset is comprised of raw k-space data in several sub-dataset groups. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMRD format and the RSNA Clinical Trial Processor, respectively. Manual inspection of each DICOM image was also performed to check for the presence of any unexpected protected health information (PHI), with spot checking of both metadata and image content.

Knee MRI: Data from more than 1,500 fully sampled knee MRIs obtained on 3 and 1.5 Tesla magnets and DICOM images from 10,000 clinical knee MRIs also obtained at 3 or 1.5 Tesla. The raw dataset includes coronal proton density-weighted images with and without fat suppression. The DICOM dataset contains coronal proton density-weighted with and without fat suppression, axial proton density-weighted with fat suppression, sagittal proton density, and sagittal T2-weighted with fat suppression.

Brain MRI: Data from 6,970 fully sampled brain MRIs obtained on 3 and 1.5 Tesla magnets. The raw dataset includes axial T1 weighted, T2 weighted and FLAIR images. Some of the T1 weighted acquisitions included admissions of contrast agent.

Additional information on file structure, data loader, and transforms are available on GitHub.

Prostate MRI: Data obtained on 3 Tesla magnets from 312 male patients referred for clinical prostate MRI exams. The raw dataset includes axial T2-weighted and axial diffusion-weighted images for each of the 312 exams.

This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy). Prostate-MRI-US-Biopsy collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations. This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer. Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (https://www.pixelmed.com/dicomtoolkit.html). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (https://www.slicer.org/) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (https://github.com/QIICR/dcmqi) [5]. Resulting objects were validated using dicom3tools dciodvfy (https://www.dclunie.com/dicom3tools.html). Details describing the conversion process as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion. Specific files included in the record are:

Prostate-MRI-US-Biopsy-DICOM-Annotations.zip: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm referenced_images_sorted-idc_file_manifest.s5cmd: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (https://github.com/peak/s5cmd), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download. References [1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. Cancer Res. 81, 4188–4193 (2021). doi: 10.1158/0008-5472.CAN-21-0950. [2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: 10.1016/j.juro.2012.08.095 [4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 [5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Res. 77, e87–e90 (2017). DOI: 10.1158/0008-5472.CAN-17-0336.

The Magnetic Resonance - Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).

The dataset consists of 2D image slices extracted using the RadiAnt DICOM viewer software. The extracted images are transformed to DICOM image data format with a resolution of 256x256 pixels. There are a total of 179 2D axial image slices referring to 20 patient volumes (90 MR and 89 CT 2D axial image slices). The dataset contains MR and CT brain tumour images with corresponding segmentation masks. The MR images of each patient were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to the CT images. The MR scans have a resolution of 0.7x0.6x5 mm^3, while the CT scans have a resolution of 0.6x0.6x7 mm^3.

More information and the application of the dataset can be found in the following research paper:

Alaa Abu-Srhan; Israa Almallahi; Mohammad Abushariah; Waleed Mahafza; Omar S. Al-Kadi. Paired-Unpaired Unsupervised Attention Guided GAN with Transfer Learning for Bidirectional Brain MR-CT Synthesis. Comput. Biol. Med. 136, 2021. doi: https://doi.org/10.1016/j.compbiomed.2021.104763.