SynthRAD2023 Grand Challenge dataset: generating synthetic CT for
radiotherapy
- URL: http://arxiv.org/abs/2303.16320v1
- Date: Tue, 28 Mar 2023 21:38:25 GMT
- Title: SynthRAD2023 Grand Challenge dataset: generating synthetic CT for
radiotherapy
- Authors: Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon, Joost JC
Verhoeff, Johannes A Langendijk, Stefan Both, Cornelis (Nico) AT van den
Berg, Matteo Maspero
- Abstract summary: This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images.
The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Purpose: Medical 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 CBCT and MRI images to
facilitate the development and evaluation of sCT generation for radiotherapy
planning.
Acquisition and validation methods: The 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 CSV files
provided with the datasets.
Data format and usage notes: The data is available on Zenodo
(https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The
images for each subject are available in nifti format.
Potential applications: This 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.
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