Work

Robust Outer Volume Subtraction with Deep Learning Ghosting Detection for Highly-Accelerated Real-Time Dynamic MRI

Year: 2024

Type: conference-paper

Abstract: Real-time dynamic MRI is important for visualizing time-varying processes in several applications, including cardiac imaging, where it enables free-breathing images of the beating heart without ECG gating. However, current real-time MRI techniques commonly face challenges in achieving the required spatio-temporal resolutions due to limited acceleration rates. In this study, we propose a deep learning (DL) technique for improving the estimation of stationary outer-volume signal from shifted time-interleaved undersampling patterns. Our approach utilizes the pseudo-periodic nature of the ghosting artifacts arising from the moving organs. Subsequently, this estimated outer-volume signal is subtracted from individual timeframes of the real-time MR time series, and each timeframe is reconstructed individually using physics-driven DL methods. Results show improved image quality at high acceleration rates, where conventional methods fail.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi56570.2024.10635530

Authors: Merve Gülle, Mehmet Akçakaya

Institution: University of Minnesota

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Biomedical Imaging and Bioengineering