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.

翻译：加速动态容积磁共振成像（4DMRI）对于依赖运动分辨的应用至关重要。现有4DMRI在平均呼吸相位中会产生可接受的伪影，但可能模糊并扭曲瞬时动态信息。恢复此类信息需要一种新范式来重建极度欠采样的非笛卡尔k空间数据。我们提出B-FIRE，一种用于超加速MR重建的无分箱扩散隐式神经表示框架，能够反映瞬时三维腹部解剖结构。B-FIRE采用以扩散优化的CNN-INR编码器-解码器骨架，通过综合损失函数强制执行图像域保真度和频率感知约束。训练参考使用运动分箱图像对，推理则在无分箱欠采样数据上进行。实验基于T1加权StarVIBE肝脏MRI数据集，加速倍数从每次8条辐线（RV8）至RV1。将B-FIRE与直接NuFFT、GRASP-CS及展开CNN方法进行对比，评估了重建保真度、运动轨迹一致性及推理延迟。