Extending Cardiac Functional Assessment with Respiratory-Resolved 3D Cine MRI

Abstract

This study aimed to develop a cardiorespiratory-resolved 3D magnetic resonance imaging (5D MRI: x-y-z-cardiac-respiratory) approach based on 3D motion tracking for investigating the influence of respiration on cardiac ventricular function. A highly-accelerated 2.5-minute sparse MR protocol was developed for a continuous acquisition of cardiac images through multiple cardiac and respiratory cycles. The heart displacement along respiration was extracted using a 3D image deformation algorithm, and this information was used to cluster the acquired data into multiple respiratory phases. The proposed approach was tested in 15 healthy volunteers (7 females). Cardiac function parameters, including the end-systolic volume (ESV), end-diastolic volume (EDV), stroke volume (SV), and ejection fraction (EF), were measured for the left and right ventricle in both end-expiration and end-inspiration. Although with the proposed 5D cardiac MRI, there were no significant differences ( p > 0.05, t-test) between end-expiration and end-inspiration measurements of the cardiac function in volunteers, incremental respiratory motion parameters that were derived from 3D motion tracking, such as the depth, expiration and inspiration distribution, correlated ( p < 0.05, correlation coefficient, Mann-Whitney) with those volume-based parameters of cardiac function and varied between genders. The obtained initial results suggested that this new approach allows evaluation of cardiac function during specific respiratory phases. Thus, it can enable investigation of effects related to respiratory variability and better assessment of cardiac function for studying respiratory and/or cardiac dysfunction.

The auto segmentation for cardiac structures using a dual‐input deep learning network based on vision saliency and transformer

“…This is not only because the volume change can be measured by whole heart segmentation to calculate ESV, EF, and other indicators. 24,25 More importantly, the accurate segmentation of the whole heart based on artificial intelligence (AI) on a phase of the R-R interval plays a vital role and has broad prospects for promoting the development of cardiac functional imaging (such as CT and MR) to track movement, so as to ensure more accurate application in the diagnosis of cardiovascular diseases. [26][27][28] Because of ECG-gated and very short acquisition time,CCTA image can well capture the static images of a certain phase in the cardiac cycle and effectively suppress the artifacts of heart beat.…”

“…Accurate segmentation of cardiac structures plays a more and more important role in cardiac functional assessment. This is not only because the volume change can be measured by whole heart segmentation to calculate ESV, EF, and other indicators 24,25 . More importantly, the accurate segmentation of the whole heart based on artificial intelligence (AI) on a phase of the R‐R interval plays a vital role and has broad prospects for promoting the development of cardiac functional imaging (such as CT and MR) to track movement, so as to ensure more accurate application in the diagnosis of cardiovascular diseases 26–28 .…”

Automatic image-based respiratory signal extraction in real-time CMR

“…However, for the LV volume, we did not observe a significant difference between the PE and PI heartbeats. This could be because the small sample size is inadequate to distinguish variations in LV function, which is less susceptible to respiratory-induced motion (28). We also compared the two methods using data from two patients, one with a periodic respiratory pattern and the other with an irregular respiratory pattern.…”

“…These findings are consistent with the literature (6). However, for the left ventricle, the differences between BH, PE, and PI are not significant, which is likely due to the sample size being inadequate to distinguish smaller variations in left ventricle (LV) function (6,28).…”

Semiautomatic quantification of abdominal wall muscles deformations based on dynamic MRI image registration

“…Real-time dynamic MRI is a promising modality in the field of medical imaging given its capacity to capture organs motion and deformation. It is currently used for the diagnosis of cardiac dysfunction and perfusion 1,2 and pelvis organ prolapse. 3 This diagnostic approach is still often limited to a qualitative analysis of images by experts whereas quantitative information would enable refining it.…”