Compensation for respiratory motion–induced signal loss and phase corruption in free‐breathing self‐navigated cine DENSE using deep learning

Author:

Abdi Mohamad12ORCID,Bilchick Kenneth C.2,Epstein Frederick H.13

Affiliation:

1. Department of Biomedical Engineering University of Virginia Charlottesville Virginia USA

2. Department of Cardiovascular Medicine University of Virginia Health System Charlottesville Virginia USA

3. Department of Radiology University of Virginia Health System Charlottesville Virginia USA

Abstract

PurposeTo introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first‐order respiratory motion compensation for self‐navigated free‐breathing cine DENSE of the heart.MethodsThe motion model includes conventional position shifts of magnetization and further describes the phase shift of the stimulated echo due to breathing. These image‐domain effects correspond to linear and constant phase errors, respectively, in k‐space. The model was validated using phantom experiments and Bloch‐equation simulations and was used along with the simulation of respiratory motion to generate synthetic images with phase‐shift artifacts to train a U‐Net, DENSE‐RESP‐NET, to perform motion correction. DENSE‐RESP‐NET‐corrected self‐navigated free‐breathing DENSE was evaluated in human subjects through comparisons with signal averaging, uncorrected self‐navigated free‐breathing DENSE, and breath‐hold DENSE.ResultsPhantom experiments and Bloch‐equation simulations showed that breathing‐induced constant phase errors in segmented DENSE leads to signal loss in magnitude images and phase corruption in phase images of the stimulated echo, and that these artifacts can be corrected using the known respiratory motion and the model. For self‐navigated free‐breathing DENSE where the respiratory motion is not known, DENSE‐RESP‐NET corrected the signal loss and phase‐corruption artifacts and provided reliable strain measurements for systolic and diastolic parameters.ConclusionDENSE‐RESP‐NET is an effective method to correct for breathing‐associated constant phase errors. DENSE‐RESP‐NET used in concert with self‐navigation methods provides reliable free‐breathing DENSE myocardial strain measurement.

Funder

National Institutes of Health

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3