TotalSynth generates synthetic CT scans from MRI and CBCT images
TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCT
Computer Vision and Pattern Recognition
Summary
Medical imaging often uses CT scans, but these expose patients to radiation, while MRI and CBCT scans do not provide the same detail. The authors developed TotalSynth, a tool that can create synthetic CT images from MRI and CBCT scans, helping doctors get CT-like images without extra radiation. They tested it on thousands of cases, showing it works well across many body parts. The study also found that adapting the tool for new data sources can improve accuracy.
What this means in practice
- •For radiology departments: Create CT-like images from MRI or CBCT to reduce patient radiation exposure while maintaining diagnostic quality.
- •For radiation therapy planners: Use synthetic CT images generated from MRI or CBCT scans to assist in accurate dose planning without additional CT imaging.
Authors
Valentin Boussot, Cedric Hemon, Anais Barateau, Caroline Lafond, Jean-Claude Nunes, Jean-Louis Dillenseger
Abstract
Purpose: To develop and evaluate TotalSynth, a reusable pretrained framework for whole-body synthetic CT (sCT) generation from MRI and cone-beam CT (CBCT) images. Materials and Methods: In this retrospective technical study, the dataset was assembled between 2020 and 2026 from SynthRAD challenge data, four prostate cohorts, and BIC-MAC. After registration quality control, 1450 of 1800 public challenge pairs were retained; 350 were excluded for insufficient registration quality or major source/CT mismatch. The corpus also included 84 additional prostate MRI/CT and 60 external BIC-MAC MRI/CT cases. Three 5-fold model families were evaluated with image-domain, anatomy-aware, registration-based, and uncertainty metrics. Age and sex were not consistently available across public datasets. Results: The released MRI-to-CT model achieved an overall MAE of 67.49 HU, SSIM of 0.920, and PSNR of 29.28 dB. The CBCT-to-CT model achieved an overall MAE of 53.55 HU, SSIM of 0.939, and PSNR of 32.09 dB. The unified model maintained similar performance on MRI inputs (MAE, 67.68 HU) and CBCT inputs (MAE, 54.22 HU). On external BIC-MAC data, MRI-to-CT MAE was 100.91 HU without fine-tuning and 62.21 HU after fine-tuning. Conclusion: TotalSynth provides reusable MRI- and CBCT-based CT synthesis models with broad anatomical coverage, while external evaluation highlights the need for local validation and optional fine-tuning under domain shift.