Synthesizing computed tomography for radiotherapy challenge (SynthRAD2025)
Creators: name: Thummerer, Adrian
name: Galapon, Arhur Jr.
name: Kurz, Christopher
name: van der Bijl, Erik
name: Kamp, Florian
name: Landry, Guillaume
name: Terpstra, Maarten
name: Maspero, Matteo
name: Wahl, Niklas
name: Rogowski, Viktor
Publisher: name: Zenodo
Year: 2024
Subjects: medical imaging
magnetic resonance imaging
computed tomography
cone beam computed tomography
image synthesis
generative model
radiotherapy
MICCAI 2025 challenge

description: Clinical problemMedical imaging has become increasingly important in the diagnosis and treatment of oncological patients, particularly in radiotherapy (RT), which is used in more than half of all cancer patients. In RT, pre-treatment imaging is used to develop a radiation treatment plan, which is subsequently delivered in fractions over up to 30 days. Traditionally, X-ray-based imaging is widely adopted in RT for patient positioning and monitoring before, during, or after the treatment, however, also magnetic resonance imaging (MRI) is playing an increasingly indispensable role in radiotherapy workflows.More specifically, accurately calculating radiation therapy doses for cancer patients requires detailed information about tissue photon attenuation or proton stopping power, traditionally obtained from computed tomography (CT) images. Ideally, this would be done at each treatment fraction. However, CT exposes patients to ionizing radiation, has a limited soft-tissue contrast and is usually not available in treatment rooms.

Opportunities(1) MRI: Magnetic resonance imaging (MRI) offers superb soft-tissue contrast without radiation but lacks the attenuation/stopping power data needed for radiation therapy dose calculation and treatment plan optimization [Schmidt et al., 2015].(2) CBCT: Cone-beam computed tomography (CBCT) is widely used in radiation therapy for in-room patient positioning and monitoring. However, its image quality is limited due to artifacts, hindering widespread adoption of in-room CBCT-based treatment planning [Boda-Heggeman J et al., 2011].

Solution GapConverting both MRI and CBCT to synthetic CT (sCT) images enables [Spadea 2021] (1) MRI-only radiation therapy, which minimizes radiation exposure, eliminates treatment inaccuracies due to CT-to-MRI registration errors and simplifies clinical workflows [Edmund and Nyholm, 2017] and (2) online adaptive MRI- or CBCT-based radiation therapy, where sCTs from either MRI or CBCT images allow for accurate dose calculations and daily treatment plan adaptation (at each fraction) to anatomical changes during therapy.

JustificationPublicly available datasets and challenges are needed to compare and advance sCT generation methods for both MRI and CBCT. This will ultimately lead to safer, more efficient, and potentially personalized radiation therapy for cancer patients.

Objective & TasksThis challenge aims to provide a platform offering public data and evaluation metrics to compare the latest developments in sCT generation methods. A type 2 challenge will be run, where the participants need to submit their algorithm packaged in a docker for testing. Two tasks are defined. Task 1 comprises MRI-to-sCT generationto facilitate MRI-only and MRI-based adaptive radiotherapy. Task 2 will focus on CBCT-to-sCT generation to facilitate CBCT-based adaptive radiotherapy.

Dataset & the challengeA multi-center dataset is presented, balancing training, validation and test cases to evaluate the methods on different MRI and CBCT devices and sequences/acquisition settings for head-and-neck, thorax and abdomen cases. Currently, five centers (UMC Utrecht, UMC Groningen, Radboud Nijmegen, LMU University Hospital Munich and University Hospital Cologne) share data providing 65/10/25 (train/validation/test) MRI or CBCT and CT pairs per tumor entity of patients undergoing radiotherapy at the respective institute.A total of 900-1200 paired MRI-CT and 1500 CBCT-CT sets is provided, along with a body mask that will be considered for evaluation and may be used for inference. The target CTs of the validation and test set will not be shared with the participants to avoid optimistic biases. Input data of the validation and test set will be accessible by the algorithm submitted by the participants, but not directly available to the teams, until the end of the challenge.The challenge runs on https://synthrad2025.grand-challenge.org/, and the pre-processing and evaluation code used to rank the submissions based on image, geometric, and dose evaluations will be shared. Challenge participants may choose to participate either in Task 1 or 2 or both. For each task, algorithms should be provided for all the anatomical sites. The participants may decide whether to provide one or more models per task. The center of origin of the data will not be disclosed at a case level.The evaluation code used to rank the challenge is shared at https://github.com/SynthRAD2025. After completion, the leaderboard will remain open for submission, ensuring that future methods may still be evaluated up to 2030.We envision that this challenge will enable a fair and open evaluation of different approaches to synthetic CT generation from MRI and CBCT.