High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.
High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.
复制标题
高分辨率螺旋实时心脏电影成像，具有基于深度学习的快速图像重建和量化。
DOI：
10.1002/nbm.5051
复制
发表时间：
2024
影响因子：
2.9
通讯作者：
Salerno,Michael
中科院分区：
文献类型：
--
作者：
Wang,Junyu;Awad,Marina;Zhou,Ruixi;Wang,Zhixing;Wang,Xitong;Feng,Xue;Yang,Yang;Meyer,Craig;Kramer,ChristopherM;Salerno,Michael
本研究的目的是开发和评价基于深度学习的快速螺旋图像重建（SPERE）和基于深度学习（DL）的分割方法，以量化高分辨率螺旋真实的时间电影成像的左心室射血分数（LVEF），包括1.5 T下的2D平衡稳态自由进动成像和1.5和3 T下的梯度回波（GRE）成像。提出并评估了基于3D U-Net的图像重建网络和基于2D U-Net的图像分割网络。低秩加稀疏（L+S）作为图像重建网络的参考，左心室的手动轮廓是分割网络的参考。为了评估图像重建质量，由两名有经验的心脏病专家进行了结构相似性指数、峰值信噪比、归一化均方根误差和盲态分级（5：优; 1：差）。为了评估分割性能，将3 T GRE成像上的LVEF定量与手动轮廓勾画的定量进行了比较。所提出的技术表现出优异的性能。就图像质量而言，L+S和提出的DESIRE技术之间没有差异。对于定量分析，所提出的DL方法在LVEF定量方面与手动分割方法没有差异（p> 0.05）。每个动态序列（40帧）的RESPONSE重建时间约为32 s（包括非均匀快速傅立叶变换[NUFFT]），而使用GPU加速的L+S重建时间约为3 min。DL分段所需时间小于5 s。总之，所提出的基于DL的图像重建和量化技术能够对整个心脏进行1分钟图像重建，并对左心室功能进行自动重建和量化，以实现高分辨率螺旋真实的实时电影成像，具有出色的性能。
The objective of the current study was to develop and evaluate a DEep learning‐based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)‐based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high‐resolution spiral real‐time cine imaging, including 2D balanced steady‐state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U‐Net–based image reconstruction network and 2D U‐Net–based image segmentation network were proposed and evaluated. Low‐rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal‐to‐noise ratio, normalized root‐mean‐square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p> 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL‐based image reconstruction and quantification techniques enabled 1‐min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high‐resolution spiral real‐time cine imaging with excellent performance.