PSIRNet用深度学习实现心脏磁共振快速成像，单次扫描即可获诊断级图像。
PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging
- 基于物理引导的深度学习网络，从两次心跳数据重建图像。
- 相比传统方法缩短8至24倍扫描时间，图像质量获专家认可。
- 适合临床急需快速成像的心脏病患者，尤其对不配合者更友好。
目的：开发并评估一种基于深度学习（DL）的自由呼吸相位敏感反转恢复（PSIR）延迟钆增强（LGE）心脏MRI方法，仅需两次心跳的单次采集即可生成诊断级图像，无需传统的8至24次运动校正信号平均。材料与方法：本回顾性研究使用了2016至2024年间在1.5T和3T扫描仪上多中心采集的55,917名患者的800,653个切片数据。按患者划分，640,000个切片（42,822名患者）用于训练，其余用于验证和测试，且训练与测试数据来自不同机构。训练采用包含8.45亿参数的物理引导深度学习网络（PSIRNet），端到端重建带有表面线圈校正的PSIR图像，输入为单次交错的IR/PD采集数据。重建质量通过结构相似性（SSIM）、峰值信噪比（PSNR）和归一化均方根误差（NRMSE）与运动校正参考图像对比评估。两名心脏病专家独立进行定性评估，采用五级李克特量表评价亮血、暗血及宽频带LGE变体的图像质量。采用精确威尔科克斯符号秩检验，在R 4.5.2中以0.05显著性水平进行配对优劣与等效性分析（等效边界=0.25分）。结果：两位读者均认为单次采集的PSIRNet重建在暗血LGE上显著优于运动校正参考图像（保守P = .002）；在亮血和宽频带场景中，一位读者认为其更优，另一位确认等效（所有P < .001）。推理时间约为每切片100毫秒，远低于运动校正方法超过5秒/切片的耗时。结论：PSIRNet可从单次采集生成诊断级自由呼吸PSIR LGE图像，实现8至24倍的扫描时间缩减。
原文摘要 · Abstract (English)
Purpose: To develop and evaluate a deep learning (DL) method for free-breathing phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) cardiac MRI that produces diagnostic-quality images from a single acquisition over two heartbeats, eliminating the need for 8 to 24 motion-corrected (MOCO) signal averages. Materials and Methods: Raw data comprising 800,653 slices from 55,917 patients, acquired on 1.5T and 3T scanners across multiple sites from 2016 to 2024, were used in this retrospective study. Data were split by patient: 640,000 slices (42,822 patients) for training and the remainder for validation and testing, without overlap. The training and testing data were from different institutions. PSIRNet, a physics-guided DL network with 845 million parameters, was trained end-to-end to reconstruct PSIR images with surface coil correction from a single interleaved IR/PD acquisition over two heartbeats. Reconstruction quality was evaluated using SSIM, PSNR, and NRMSE against MOCO PSIR references. Two expert cardiologists performed an independent qualitative assessment, scoring image quality on a 5-point Likert scale across bright blood, dark blood, and wideband LGE variants. Paired superiority and equivalence (margin = 0.25 Likert points) were tested using exact Wilcoxon signed-rank tests at a significance level of 0.05 using R version 4.5.2. Results: Both readers rated single-average PSIRNet reconstructions superior to MOCO PSIR for dark blood LGE (conservative P = .002); for bright blood and wideband, one reader rated it superior and the other confirmed equivalence (all P < .001). Inference required approximately 100 msec per slice versus more than 5 sec for MOCO PSIR. Conclusion: PSIRNet produces diagnostic-quality free-breathing PSIR LGE images from a single acquisition, enabling 8- to 24-fold reduction in acquisition time.
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