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Medical Physics
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SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy

Generating synthetic CT for radiotherapy
Authors: Thummerer, Adrian; van der Bijl, Erik; Galapon, Arthur Jr; Verhoeff, Joost JC; Langendijk, Johannes A; Both, Stefan; Cornelis; +2 Authors

SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy

Abstract

AbstractPurposeMedical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone‐beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning.Acquisition and Validation MethodsThe dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data‐providing centers. Details are available in a comma separated value files provided with the datasets.Data Format and Usage NotesThe data is available on Zenodo (https://doi.org/10.5281/zenodo.7260704, https://doi.org/10.5281/zenodo.7868168) under the SynthRAD2023 collection. The images for each subject are available in nifti format.Potential ApplicationsThis dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi‐center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.

Country
Netherlands
Keywords

Adult, FOS: Computer and information sciences, Adolescent, Computer Vision and Pattern Recognition (cs.CV), Biophysics, Computer Science - Computer Vision and Pattern Recognition, Radboud University Medical Center, FOS: Physical sciences, Radboudumc 9: Rare cancers Radiation Oncology, Pelvis, Young Adult, Journal Article, Image Processing, Computer-Assisted, magnetic resonance imaging, Humans, Child, Aged, Aged, 80 and over, Radiotherapy Planning, Computer-Assisted, deep learning, computed tomography, Radiotherapy Dosage, Middle Aged, Cone-Beam Computed Tomography, artificial intelligence, Physics - Medical Physics, Magnetic Resonance Imaging, Radiology Nuclear Medicine and imaging, synthetic CT, Child, Preschool, Medical Physics (physics.med-ph), Tomography, X-Ray Computed, Radiotherapy, Image-Guided

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
72
Top 1%
Top 10%
Top 1%
Green
hybrid