跨场强MRI生成颅脑CT，提升临床适用性
A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths
- 构建模块化结构耦合模型，适应不同MRI场强与序列
- 多中心数据验证显示性能优于传统方法
- 适合需跨设备协同的医学影像临床场景
从磁共振成像（MRI）准确合成计算机断层扫描（CT）图像在颅脑应用中具有重要临床价值，如衰减校正、放疗计划和术中导航。然而，不同MRI场强和扫描协议带来的异质性限制了现有方法的泛化能力。本研究将颅脑CT合成建模为模块化、结构耦合问题，提出一种深度学习框架，以增强在异构MRI条件下的鲁棒性。该模型可适应场强与成像协议的变化，同时保持解剖一致性。在多中心数据集上的实验表明，相比传统方法，本方法在性能和泛化能力上均有提升。所提方法可在异构MRI设置下实现可靠的CT合成，推动临床转化。
原文摘要 · Abstract (English)
Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image-guided interventions. However, heterogeneity across MRI field strengths and acquisition protocols limits the generalizability of existing methods. In this study, we formulate cranial CT synthesis as a modular, structurally coupled problem and propose a deep learning framework to improve robustness across heterogeneous MRI conditions. The model is designed to adapt to variations in field strength and imaging protocols while preserving anatomical consistency. Experiments on multi-site datasets demonstrate improved performance and generalization compared with conventional approaches. The proposed method enables reliable CT synthesis across heterogeneous MRI settings, supporting broader clinical translation.
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