Accelerating cardiac diffusion tensor imaging with deep learning-based tensor de-noising and breath hold reduction. A step towards improved efficiency and clinical feasibility.

Michael Tänzer¹, Andrew D Scott², Zohya Khalique³, Maria Molto³, Ramyah Rajakulasingam³, Ranil De Silva³, Dudley J Pennell², Pedro F Ferreira², Guang Yang⁴, Daniel Rueckert⁵, Sonia Nielles-Vallespin²

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

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

³Royal Brompton and Harefield Hospital, London, United Kingdom.

⁴Imperial College London, London, United Kingdom. Electronic address: g.yang@imperial.ac.uk.

⁵Imperial College London, London, United Kingdom; Technische Universität München, München, Germany.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Cardiac diffusion tensor imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve an adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.…

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