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Abstract #3995

Cardiac MR T1 Mapping with Shortened Acquisition Time and Window via Deep Learning

Chun Liu1, Hanxi Liao1, Qinfang Miao1, Peng Hu1, and Haikun Qi1
1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Analysis/Processing, Quantitative Imaging, Transformer

Motivation: MOLLI is a widely used technique for cardiac MR (CMR) T1 mapping, which, however suffers from long breath-hold and acquisition window. We aim to propose a deep learning network to mitigate these challenges.

Goal(s): To develop a deep learning model to simultaneously reduce the acquisition time and window of MOLLI.

Approach: A novel transformer model is designed to explore the spatial and inter-contrast correlations the MOLLI T1-weighted images to subsequently generate high-quality CMR T1 maps using only four low-resolution single-shot T1-weighted images.

Results: The proposed deep learning model is able to achieve accurate quantification and super-resolution of CMR T1 maps simultaneously.

Impact: The proposed method can generate high-quality cardiac T1 maps using MOLLI images acquired with reduced acquisition time (~4s) and three to four-fold shortened acquisition window, thereby enhancing the clinical applicability of cardiac T1 mapping.

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Keywords