Physics-informed self-supervised generation of digital brain MRI phantoms from weighted images using differentiable MRI simulation
Abstract
Purpose: To develop a physics-informed, self-supervised framework for generating digital brain MRI phantoms directly from conventional weighted MR images without requiring ground-truth parametric maps or anatomical segmentation. Methods: The framework predicts T1, T2, and proton density (PD) maps from T1-, T2-, and PD-weighted images and reconstructs the input images through an MRI signal model. Three generative architectures (variational autoencoder (VAE), generative adversarial network (GAN), and flow-based model) were compared. Models were pretrained on 3,739 synthetic brain slices generated using digital phantoms and an analytical MRI signal model, followed by fine-tuning on 90 real brain slices from three healthy volunteers. The best-performing architecture was subsequently fine-tuned using the differentiable MR-Zero numerical MRI simulator and evaluated on 30 held-out real slices. Results: The flow-based model demonstrated the highest overall performance and preserved fine anatomical details better than the VAE and GAN. After analytical-model fine-tuning, it achieved MS-SSIM values of 0.955-0.985 and PSNR values of 26.68-32.63 dB across T1-, T2-, and PD-weighted images. Fine-tuning with MR-Zero increased T1-weighted reconstruction quality from 0.955 to 0.977 (MS-SSIM) and from 26.68 to 30.60 dB (PSNR), and provided high robustness of metrics across different MR image weightings. The resulting digital phantoms also enabled simulation of images using previously unseen acquisition protocols. Conclusion: The proposed framework enables physics-informed generation of reusable digital brain MRI phantoms from weighted images using limited real-world data. Combining synthetic pretraining with differentiable numerical MRI simulation provides a practical approach for physically grounded MRI data augmentation without requiring reference parametric maps.
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BibTeX
@misc{belousova2026physicsinformed,
title = {{Physics-informed self-supervised generation of digital brain MRI phantoms from weighted images using differentiable MRI simulation}},
author = {Kseniya Belousova and Anna Konanykhina and Zilya Murzina and Walid Al-Haidri and Ekaterina Brui},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.35273},
eprint = {2609.35273},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35273},
}
RIS
TY - UNPB
TI - Physics-informed self-supervised generation of digital brain MRI phantoms from weighted images using differentiable MRI simulation
AU - Kseniya Belousova
AU - Anna Konanykhina
AU - Zilya Murzina
AU - Walid Al-Haidri
AU - Ekaterina Brui
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.35273
UR - https://arxiv.org/abs/2609.35273
AB - Purpose: To develop a physics-informed, self-supervised framework for generating digital brain MRI phantoms directly from conventional weighted MR images without requiring ground-truth parametric maps or anatomical segmentation. Methods: The framework predicts T1, T2, and proton density (PD) maps from T1-, T2-, and PD-weighted images and reconstructs the input images through an MRI signal model. Three generative architectures (variational autoencoder (VAE), generative adversarial network (GAN), and flow-based model) were compared. Models were pretrained on 3,739 synthetic brain slices generated using digital phantoms and an analytical MRI signal model, followed by fine-tuning on 90 real brain slices from three healthy volunteers. The best-performing architecture was subsequently fine-tuned using the differentiable MR-Zero numerical MRI simulator and evaluated on 30 held-out real slices. Results: The flow-based model demonstrated the highest overall performance and preserved fine anatomical details better than the VAE and GAN. After analytical-model fine-tuning, it achieved MS-SSIM values of 0.955-0.985 and PSNR values of 26.68-32.63 dB across T1-, T2-, and PD-weighted images. Fine-tuning with MR-Zero increased T1-weighted reconstruction quality from 0.955 to 0.977 (MS-SSIM) and from 26.68 to 30.60 dB (PSNR), and provided high robustness of metrics across different MR image weightings. The resulting digital phantoms also enabled simulation of images using previously unseen acquisition protocols. Conclusion: The proposed framework enables physics-informed generation of reusable digital brain MRI phantoms from weighted images using limited real-world data. Combining synthetic pretraining with differentiable numerical MRI simulation provides a practical approach for physically grounded MRI data augmentation without requiring reference parametric maps.
ER -