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Abstract #2635

Accelerating DT-CMR with Variable CAIPIRINHA Shifts and Artefact-Aware AI-based SMS reconstruction

Michael Tänzer1,2, Eun ji Lim1,2, Huaqi (Harvey) Qiu3, Andrew Scott1,2, Pedro Ferreira1,2, Daniel Rueckert1,3,4, Guang Yang1,2, and Sonia Nielles-Vallespin1,2
1Imperial College London, London, United Kingdom, 2Royal Brompton Hospital, Guy’s and St Thomas, London, United Kingdom, 3Technische Universität München, Munich, Germany, 4TUM Universitätsklinikum, Munich, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Simultaneous multi-slice (SMS) methods could reduce Diffusion-Tensor Cardiac MR (DT-CMR) scan times from over 60 minutes to clinically-feasible durations, but interslice leakage artefacts currently prevent practical use.

Goal(s): Develop a method that enables SMS acceleration factors up to 3 while maintaining diffusion tensor map quality comparable to single-slice acquisition.

Approach: We combined a novel variable CAIPIRINHA shift strategy with a dual-stream AI reconstruction method, evaluated through retrospective SMS simulations based on ex-vivo DT-CMR acquisitions.

Results: Our method reduced DT-CMR map errors by up to 50% maintaining a 2.6× speed-up, with diffusion metrics closely matching non-accelerated acquisitions whereas standard SMS reconstruction showed significant deviations.

Impact: By substantially reducing acquisition times while preserving diffusion map quality, this work helps bridge the gap towards clinical adoption of DT-CMR, advancing non-invasive assessment of cardiac microstructure for diagnosing and monitoring of cardiac disease.

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Keywords