Real-time R-peak detection for 5T MRI Gating based on Template Matching

Cardiovascular Magnetic Resonance Imaging (CMR) is essential for detailed cardiac assessment but faces challenges in gating accuracy at higher magnetic field strengths like 5T due to magnetohydrodynamic (MHD) artifacts. Current ECG-based methods often failed in detecting precise R-peaks due to these artifacts, compromising imaging quality. In this study, we proposed a novel single-channel ECG gating framework tailored for 5T MRI scanners. Our approach integrated template matching and adaptive thresholding to enhance R-peak detection reliability in real-time. Evaluation on a dataset from 21 healthy volunteers demonstrated superior performance over conventional methods, achieving a Positive Predictive Value of 99.85% and Sensitivity of 89.69%. This method holds promise for improving CMR synchronization accuracy and image quality, paving the way for enhanced clinical applications in high-field MRI environments.

Authors

Institutions

Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1109/bibm62325.2024.10822293
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-time R-peak detection for 5T MRI Gating based on Template Matching

Lin Xu, Zuyu Du, Xuanyu Zhu, Peng Hu et al.
Medical Image Segmentation Techniques
article

Real-time R-peak detection for 5T MRI Gating based on Template Matching

Lin Xu, Zuyu Du, Xuanyu Zhu, Peng Hu, Tangwei Cao, Jiameng Diao
article en

Abstract

Cardiovascular Magnetic Resonance Imaging (CMR) is essential for detailed cardiac assessment but faces challenges in gating accuracy at higher magnetic field strengths like 5T due to magnetohydrodynamic (MHD) artifacts. Current ECG-based methods often failed in detecting precise R-peaks due to these artifacts, compromising imaging quality. In this study, we proposed a novel single-channel ECG gating framework tailored for 5T MRI scanners. Our approach integrated template matching and adaptive thresholding to enhance R-peak detection reliability in real-time. Evaluation on a dataset from 21 healthy volunteers demonstrated superior performance over conventional methods, achieving a Positive Predictive Value of 99.85% and Sensitivity of 89.69%. This method holds promise for improving CMR synchronization accuracy and image quality, paving the way for enhanced clinical applications in high-field MRI environments.

ShanghaiTech University (CN)
Openalex Percentile: Top 75%
Medical Image Segmentation Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.