无需分箱的扩散神经表示，实现超加速动态腹部MRI重建
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
- 用扩散模型优化隐式神经表示，直接重建非笛卡尔欠采样数据
- 在RV1加速下仍保持高图像保真度和运动轨迹一致性
- 适合需要瞬时解剖结构的超加速动态MRI研究
超加速动态体部磁共振成像（4DMRI）对运动分辨应用至关重要。现有方法在平均呼吸相位上产生可接受的伪影，会模糊并失真瞬时动态信息。恢复此类信息需新范式重建极低采样率的非笛卡尔k空间数据。本文提出B-FIRE，一种无需分箱的扩散隐式神经表示框架，可反映瞬时3D腹部解剖结构。B-FIRE采用基于扩散优化的CNN-INR编码器-解码器骨干网络，结合图像域保真度与频率感知约束的综合损失函数。训练使用分箱运动图像对，推理则针对无分箱欠采样数据。在T1加权StarVIBE肝脏MRI队列上测试，加速比从每帧8条线（RV8）至RV1。与直接NuFFT、GRASP-CS及未展开的CNN方法对比，评估了重建保真度、运动轨迹一致性与推理延迟。
原文摘要 · Abstract (English)
Accelerated dynamic volumetric magnetic resonance imaging (4DMRI) is essential for applications relying on motion resolution. Existing 4DMRI produces acceptable artifacts of averaged breathing phases, which can blur and misrepresent instantaneous dynamic information. Recovery of such information requires a new paradigm to reconstruct extremely undersampled non-Cartesian k-space data. We propose B-FIRE, a binning-free diffusion implicit neural representation framework for hyper-accelerated MR reconstruction capable of reflecting instantaneous 3D abdominal anatomy. B-FIRE employs a CNN-INR encoder-decoder backbone optimized using diffusion with a comprehensive loss that enforces image-domain fidelity and frequency-aware constraints. Motion binned image pairs were used as training references, while inference was performed on binning-free undersampled data. Experiments were conducted on a T1-weighted StarVIBE liver MRI cohort, with accelerations ranging from 8 spokes per frame (RV8) to RV1. B-FIRE was compared against direct NuFFT, GRASP-CS, and an unrolled CNN method. Reconstruction fidelity, motion trajectory consistency, and inference latency were evaluated.
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