DOI: 10.5281/zenodo.14918089
title: SynthRAD2025 Grand Challenge dataset: generating synthetic CT for radiotherapy

description: Dataset Description

Dataset Structure

A detailed description available in "SynthRAD2025_dataset_description.pdf". A paper describing the dataset has been submitted to Medical Physics and is available as pre-print at: https://arxiv.org/abs/2502.17609. The dataset is divided into two tasks:



Task 1 (MRI-to-CT conversion) is provided in Task1.zip.

Task 2 (CBCT-to-CT conversion) is provided in Task2.zip.


After extraction, the dataset is organized as follows:

Within each task, cases are categorized into three anatomical regions:



Head-and-neck (HN)

Thorax (TH)

Abdomen (AB)


Each anatomical region contains individual patient folders, named using a unique seven-letter alphanumeric code:[Task Number][Anatomy][Center][PatientID]Example: 1HNA001

Each patient folder in the training dataset contains (for other sets see Table below):



ct.mha: preprocessed CT image

mr.mha or cbct.mha (depending on the task): preprocessed MR or CBCT image

mask.mha: Binary mask of the patient outline (dilated)


An overview folder within each anatomical region contains:



[task]_[anatomy]_parameters.xlsx: Imaging protocol details for each patient.

[task][anatomy][center][PatientID]_overview.png: A visualization of axial, coronal, and sagittal slices of CBCT/MR, CT, mask, and difference images.


Dataset Overview

The SynthRAD2025 dataset is part of the second edition of the SynthRAD deep learning challenge (https://synthrad2025.grand-challenge.org/), which benchmarks synthetic CT generation for MRI- and CBCT-based radiotherapy workflows.



Task 1: MRI-to-CT conversion for MR-only and MR-guided photon/proton radiotherapy, consisting of 890 MRI-CT pairs.

Task 2: CBCT-to-CT conversion for daily adaptive radiotherapy workflows, consisting of 1,472 CBCT-CT pairs.


Imaging data was collected from five European university medical centers:



Netherlands: UMC Groningen, UMC Utrecht, Radboud UMC

Germany: LMU Klinikum Munich, UK Cologne


All centers have independently approved the study in accordance with their institutional review boards or medical ethics committee regulations.

Inclusion criteria:



Patients treated with external beam radiotherapy (photon or proton therapy) at one of the data-providing centers.

Imaging data available from one of the three anatomical regions.

No restrictions on age, sex, tumor characteristics, or staging.


License

The dataset is provided under two different licenses:



Data from centers A, B, C, and E is provided under a CC-BY-NC 4.0 International License (creativecommons.org/licenses /by-nc/4.0/).

Data from center D is provided with a limited license which permits it's use only for the duration of the challenge and remains valid only while the challenge is active (Limited Use License Center D). By downloading Center D's data, participants agree to these terms. Once the challenge ends, access to the data ends, the download link will be deactivated, and all downloaded data must be deleted. After requesting participation in the challenge on the SynthRAD2025 website, participants can access the download link for center D at https://synthrad2025.grand-challenge.org/data/.


Data Release Schedule






Subset



Files



Release Date



Link





Training 



Input, CT, Mask



01-03-2025



https://doi.org/10.5281/zenodo.14918213





Training Center D



Input, CT, Mask



01-03-2025



Check the download link at:https://synthrad2025.grand-challenge.org/data/Limited use License:License





Validation Input



Input, Mask



01-06-2025



https://doi.org/10.5281/zenodo.14918504





Validation Input Center D 



Input, Mask



01-06-2025



Check the download link at:https://synthrad2025.grand-challenge.org/data/Limited use License:License





Validation Ground Truth



CT, Deformed CT



01-03-2030



https://doi.org/10.5281/zenodo.14918605





Test



Input, CT, Deformed CT, Mask



01-03-2030



https://doi.org/10.5281/zenodo.14918722





 

Dataset Composition

The number of cases collected at each center for training, validation, and test sets.

Training Set




Task
Center
HN
TH
AB
Total




1
A
91
91
65
247


 
B
0
91
91
182


 
C
65
0
19
84


 
D
65
0
0
65


 
E
0
0
0
0


 
Total
221
182
175
578


2
A
65
65
64
195


 
B
65
65
65
195


 
C
65
63
62
190


 
D
65
63
53
181


 
E
65
65
65
195


 
Total
325
321
309
955




Validation Set




Task
Center
HN
TH
AB
Total


1
A
14
14
10
38


 
B
0
14
14
28


 
C
10
0
3
13


 
D
10
0
0
10


 
E
0
0
0
0


 
Total
34
28
27
89


2
A
10
10
10
30


 
B
10
10
10
30


 
C
10
10
10
30


 
D
10
10
8
28


 
E
10
10
10
30


 
 Total
50
50
48
148




Testing Set




Task
Center
HN
TH
AB
Total


1
A
35
35
25
95


 
B
0
35
35
70


 
C
25
0
8
33


 
D
25
0
0
25


 
E
0
0
0
0


 
Total
85
70
68
223


2
A
25
25
25
75


 
B
25
25
25
75


 
C
25
25
25
75


 
D
25
24
20
69


 
E
25
25
25
75


 
 Total
125
124
120
369




Pre-processingThe following pre-processing steps were applied:



DICOM-to-MHA conversion

Rigid registration between CT and MR/CBCT

Defacing

Resampling to 1×1×3 mm resolution

Cropping to remove background and reduce file size

Deformable image registration (validation & test sets only)


Preprocessing scripts are available at: https://github.com/SynthRAD2025/preprocessing.

Challenge DesignThe overall challenge design can be found at: https://doi.org/10.5281/zenodo.14051074.

FundingThe challenge is supported by a grant from Stiftungen zu Gunsten der Medizinischen Fakultät der Ludwig-Maximilians-Universität München, awarded to Adrian Thummerer to cover computational costs.