发表机构
School of Physics and Engineering, ITMO University（ITMO大学物理与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种物理信息自监督框架，从加权MRI图像生成数字脑部体模，无需参数图或分割，结合合成预训练与可微分模拟，基于流模型达到最佳性能。
AI 中文摘要
目的：开发一种物理信息、自监督的框架，直接从常规加权MR图像生成数字脑部MRI体模，无需真实参数图或解剖分割。方法：该框架从T1、T2和质子密度（PD）加权图像预测T1、T2和PD图，并通过MRI信号模型重建输入图像。比较了三种生成架构：变分自编码器（VAE）、生成对抗网络（GAN）和基于流的模型。模型在3,739张使用数字体模和解析MRI信号模型生成的合成脑切片上预训练，随后在来自三位健康志愿者的90张真实脑切片上微调。性能最佳的架构随后使用可微分MR-Zero数值MRI模拟器进行微调，并在30张留出的真实切片上评估。结果：基于流的模型展现出最高的整体性能，并比VAE和GAN更好地保留了精细解剖细节。在解析模型微调后，其在T1、T2和PD加权图像上达到MS-SSIM值0.955-0.985和PSNR值26.68-32.63 dB。使用MR-Zero微调将T1加权重建质量从0.955提升至0.977（MS-SSIM），PSNR从26.68提升至30.60 dB，并在不同MR图像加权下提供了高鲁棒性的指标。生成的数字体模还能模拟使用先前未见过的采集协议的图像。结论：所提出的框架能够利用有限真实数据，从加权图像实现物理信息的可复用数字脑部MRI体模生成。将合成预训练与可微分数值MRI模拟相结合，为无需参考参数图的物理基础MRI数据增强提供了一种实用方法。
英文摘要
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.
