Simultaneous multi-slice Cardiac Diffusion Tensor Imaging with variable CAIPIRINHA shifts and artefact-aware AI.

Michael Tänzer¹, Eun Ji Lim², Huaqi Harvey Qiu³, Camila Munoz², Andrew Scott², Dudley Pennell², Pedro Ferreira², Daniel Rueckert⁴, Guang Yang⁵, Sonia Nielles-Vallespin²

¹Imperial College London, London, UK; Royal Brompton and Harefield Hospital, London, UK. Electronic address: m.tanzer@imperial.ac.uk.

²Imperial College London, London, UK; Royal Brompton and Harefield Hospital, London, UK.

³Chair for AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany; TUM University Hospital, Munich, Germany.

⁴Imperial College London, London, UK; Chair for AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany; TUM University Hospital, Munich, Germany.

⁵Imperial College London, London, UK. Electronic address: g.yang@imperial.ac.uk.

PubMed DOI

Abstract

Cardiac Diffusion Tensor Imaging (cDTI) provides unique insights into myocardial microstructure in-vivo but requires averaging multiple repetitions for adequate signal quality, leading to prohibitively long acquisition times. Standard acceleration strategies, such as reducing repetitions and employing simultaneous multi-slice (SMS) imaging, are limited by low signal-to-noise ratio (SNR) and inter-…

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