论文标题
高分辨率3D LGE CMR的自我监督物理学引导的深度学习重建
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR
论文作者
论文摘要
晚期增强（LGE）心脏MRI（CMR）是诊断心肌疤痕的临床标准。与2D成像相比，3D各向同性LGE CMR提供了改进的覆盖范围和分辨率。但是，由于扫描时间长和对比度冲洗，需要图像加速度。物理学引导的深度学习（PG-DL）方法最近作为改进的加速MRI策略而出现。 PG-DL方法的培训通常以监督方式进行，需要全面采样的数据作为参考，这在3D LGE CMR中具有挑战性。最近，提出了一种自我监督的学习方法，以实现没有完全采样的数据的培训PG-DL技术。在这项工作中，我们将这种自我监督的学习方法扩展到3D成像，同时应对与3D卷的小型培训数据库大小相关的挑战。结果和一项关于前瞻性加速3D LGE的读者研究表明，以6倍加速度的拟议方法优于3倍加速度临床使用的压缩传感方法。
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compared to 2D imaging. However, image acceleration is required due to long scan times and contrast washout. Physics-guided deep learning (PG-DL) approaches have recently emerged as an improved accelerated MRI strategy. Training of PG-DL methods is typically performed in supervised manner requiring fully-sampled data as reference, which is challenging in 3D LGE CMR. Recently, a self-supervised learning approach was proposed to enable training PG-DL techniques without fully-sampled data. In this work, we extend this self-supervised learning approach to 3D imaging, while tackling challenges related to small training database sizes of 3D volumes. Results and a reader study on prospectively accelerated 3D LGE show that the proposed approach at 6-fold acceleration outperforms the clinically utilized compressed sensing approach at 3-fold acceleration.
