发表机构
The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发名为CineDiff的基于块的扩散重建框架，用于高度加速的二维实时心脏电影CMR，在多场景评估中优于对比方法，且对分布外数据具鲁棒性。
AI 中文摘要
目的：开发并评估一种基于扩散的重建框架，用于高度加速的二维实时（RT）心脏电影心血管磁共振成像（CMR）。方法：我们训练了一个无条件的基于块的扩散模型，并将其整合到名为CineDiff的重建框架中，该框架采用扩散后验采样实现数据一致性。在四种场景下对CineDiff进行评估：（i）来自健康受试者的30例回顾性欠采样屏气心脏电影数据，涵盖1.5T和3T场强，以及多种加速率；（ii）来自需临床CMR的患者的15例前瞻性欠采样自由呼吸RT心脏电影数据，涵盖1.5T和3T场强；（iii）10例前瞻性欠采样中场（0.55T）自由呼吸扫描，包括5例健康受试者和5例猪模型数据。对于回顾性欠采样，采用峰值信噪比（PSNR）、结构相似性指数测度（SSIM）、学习感知图像块相似性（LPIPS）以及深度图像结构与纹理相似性（DISTS）评估重建质量；对于前瞻性欠采样，采用盲法专家评分（5分李克特量表）评估图像质量。结果：在回顾性欠采样屏气心脏电影数据中，CineDiff在所有评估的加速率下均比对比方法获得更高的PSNR和SSIM，以及更低的LPIPS和DISTS；在前瞻性欠采样自由呼吸RT心脏电影数据中，CineDiff获得更高的专家图像质量评分。定性来看，与传统压缩感知和名为CineVN的变分网络方法相比，CineDiff减少了块状伪影并保留了更精细的解剖细节。结论：CineDiff实现了高度加速的二维RT心脏电影CMR的高质量重建，该方法对分布外数据（包括中场和猪模型扫描）也表现出鲁棒性。
英文摘要
Purpose: To develop and evaluate a diffusion-based reconstruction framework for highly accelerated 2D real-time (RT) cine cardiovascular magnetic resonance imaging (CMR). Methods: We trained an unconditional patch-based diffusion model and incorporated it into a reconstruction framework, termed CineDiff, using diffusion posterior sampling for data consistency. CineDiff was evaluated in four settings: (i) 30 retrospectively undersampled breath-held cine at 1.5T and 3T from healthy participants across multiple acceleration rates, (ii) 15 prospectively undersampled free-breathing RT cine at 1.5T and 3T from patients indicated for clinical CMR, and (iii) 10 prospectively undersampled mid-field (0.55T) free-breathing scans, including five from healthy subjects and five from porcine models. For retrospective undersampling, reconstruction quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and deep image structure and texture similarity (DISTS). For prospective undersampling, image quality was evaluated by blinded expert scoring on a 5-point Likert scale. Results: In retrospectively undersampled breath-held cine data, CineDiff achieved higher PSNR and SSIM and lower LPIPS and DISTS than the comparison methods across all evaluated acceleration rates. In prospectively undersampled free-breathing RT cine data, CineDiff received higher expert image-quality scores. Qualitatively, CineDiff reduced block-like artifacts and preserved finer anatomical detail compared with traditional compressed sensing and a variational network method, termed CineVN. Conclusion: CineDiff enabled high-quality reconstruction of highly accelerated 2D RT cine CMR. The method also demonstrated robustness to out-of-distribution data, including mid-field and porcine acquisitions.
