无需训练数据，用低秩动态模型实现3D心脏MRI超快重建
A multi-dynamic low-rank deep image prior (ML-DIP) for 3D real-time cardiovascular MRI
- 分离建模图像内容与运动场，联合优化重建动态序列
- 加速倍数超1000，2分钟扫描即达PSNR>29dB、SSIM>0.90
- 适合高心率、呼吸运动及早搏患者，保留心跳细节
目的：开发一种从高度欠采样数据中重建3D实时心脏磁共振（CMR）的框架，无需全采样训练数据。方法：提出多动态低秩深度图像先验（ML-DIP），通过独立神经网络分别建模空间图像内容与形变场，并在每例扫描中联合训练，直接从欠采样k-space数据重建动态序列。在（i）含早搏的3D心脏数字幻影、（ii）10名健康受试者（含2人静息与运动状态）、（iii）12例早搏病患上评估。幻影使用峰值信噪比（PSNR）和结构相似性指数（SSIM）评估；体内性能对比2D实时电影的心功能定量及图像质量（相较2D实时电影与基于分箱的5D-Cine）。结果：幻影中，扫描时间仅2分钟时，ML-DIP实现PSNR > 29 dB、SSIM > 0.90，准确恢复心脏运动、呼吸运动与早搏事件。健康受试者中，功能参数与2D电影相当，图像质量优于5D-Cine，即使在运动高心率与大运动下亦表现优异。早搏患者中，ML-DIP保持逐搏变异并重建不规则搏动，而5D-Cine因分箱产生运动伪影与信息丢失。结论：ML-DIP通过从欠采样数据中学习低秩空间与运动表征，实现加速倍数超1000的高质量3D实时CMR，且不依赖外部全采样训练集。
原文摘要 · Abstract (English)
Purpose: To develop a reconstruction framework for 3D real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training datasets. Methods: We developed a multi-dynamic low-rank deep image prior (ML-DIP) framework that models spatial image content and deformation fields using separate neural networks. These sub-networks are jointly trained per scan to reconstruct the dynamic image series directly from undersampled k-space data. ML-DIP was evaluated on (i) a 3D cine digital phantom with simulated premature ventricular contractions (PVCs), (ii) ten healthy subjects (including two scanned during both rest and exercise), and (iii) 12 patients with a history of PVCs. Phantom results were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). In vivo performance was evaluated by comparing left-ventricular function quantification (against 2D real-time cine) and image quality (against 2D real-time cine and binning-based 5D-Cine). Results: In the phantom study, ML-DIP achieved PSNR > 29 dB and SSIM > 0.90 for scan times as short as two minutes, while recovering cardiac motion, respiratory motion, and PVC events. In healthy subjects, ML-DIP yielded functional measurements comparable to 2D cine and higher image quality than 5D-Cine, including during exercise with high heart rates and bulk motion. In PVC patients, ML-DIP preserved beat-to-beat variability and reconstructed irregular beats, whereas 5D-Cine showed motion artifacts and information loss due to binning. Conclusion: ML-DIP enables high-quality 3D real-time CMR with acceleration factors exceeding 1,000 by learning low-rank spatial and motion representations from undersampled data, without relying on external fully sampled training datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。