@article{QIMS13991,
author = {Fei Han and Stanislas Rapacchi and Peng Hu},
title = {Prospective cardiac motion self-gating},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {7},
number = {2},
year = {2017},
keywords = {},
abstract = {Objective: To develop a prospective cardiac motion self-gating method that provides robust and accurate cardiac triggers in real time.
Methods: The proposed self-gating method consists of an “imaging mode” that acquires the k-space segments and a “self-gating mode” that captures the cardiac motion by repeatedly sampling the k-space centerline. A training based principal component analysis algorithm is utilized to process the self-gating data where the projection onto the first principal component was used as the self-gating signal. Retrospective studies using a sequence with self-gating mode only was performed on 8 healthy subjects to validate the accuracy and reliability of the self-gating triggers. Prospective studies using both ECG-gated and self-gated cardiac CINE sequences were conducted on 6 healthy subjects to compare the image quality.
Results: Using the ECG as the reference, the proposed method was able to detect self-gating triggers within ±10ms accuracy on all 8 subjects in the retrospective study. The prospectively self-gated CINE sequence successfully detected 100% of the cardiac triggers and provided excellent CINE image quality without using ECG signals.
Conclusions: The proposed cardiac self-gating method is a robust and accurate alternative to conventional ECG-based gating method for a number of cardiac MRI applications.},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/13991}
}