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This is a Experimental Study study.

September 17, 2025

Fast Cardiac T1 Mapping in Three Heartbeats with A Transformer Integrated Network

Key Points

The method enables cardiac T1 mapping within three cardiac cycles, significantly improving patient comfort.
Combining deep learning techniques, the approach achieves accuracy comparable to traditional methods like MOLLI.
By shortening image acquisition time, the method reduces breath-hold duration and enhances operational efficiency.
Using an integrated network, reconstruction quality is improved, addressing issues of noise and motion artifacts.

Abstract

Motivation: Current cardiac T1 mapping technique suffers from long acquisition time and its sensitivity to noise or motion artifacts. Goal(s): To reduce the acquisition time and to improve the robustness against motion or noise artifacts in cardiac T1 mapping. Approach: A deep learning framework integrated with Trans-Unet and a fully connected network was developed to realize T1 mapping with T1 weighted images acquired within three cardiac cycles. Results: The method combined the advantages of current deep learning methods and achieved comparable accuracy to MOLLI, with shortened image acquisitions and enhanced robustness. Impact: The approach achieves cardiac T1 mapping within three cardiac cycles, clinically reducing the breath-hold time and causes less discomfort to patients. Meanwhile, the integrated network takes advantages of current deep learning methods and significantly improves the reconstruction quality.

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Fast Cardiac T1 Mapping in Three Heartbeats with A Transformer Integrated Network

Key Points

Abstract

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