LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging

Aggarwal, HK and Chatterjee, S and Shanbhag, D and Patil, U and Hari, KVS (2025) LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging. [Preprint]

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Abstract

Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR imaging poses a challenge, as it introduces T2-blur in the acquired images. This study aims to enhance the reconstruction quality of SSFSE MR images by (a) optimizing the trajectory for measuring the k-space, (b) acquiring fewer samples to speed up the acquisition process, and (c) reducing the impact of T2-blur. The proposed method adheres to physics constraints due to maximum gradient strength and slew-rate available while optimizing the trajectory within an end-to-end learning framework. Experiments were conducted on publicly available fastMRI multichannel dataset with 8-fold and 16-fold acceleration factors. An experienced radiologist�s evaluation on a five-point Likert scale indicates improvements in the reconstruction quality as the ACL fibers are sharper than comparative methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Item Type:	Preprint
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to the publisher.
Keywords:	Deep learning; Diffusion tensor imaging; Magnetic resonance imaging; Nuclear magnetic resonance; Photons; Spin dynamics; Spin waves; Zero-shot learning, Deep learning; Echo magnetic resonance; Fast spin echos; K-space; Reconstruction networks; Reconstruction quality; Single-shot; Single-shot MRI; Space data; Trajectory optimization, Trajectories
Department/Centre:	Autonomous Societies / Centres > Centre for Brain Research
Date Deposited:	27 Nov 2024 13:57
Last Modified:	28 Nov 2024 10:34
URI:	http://eprints.iisc.ac.in/id/eprint/86877

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