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Abstract #0032

A Framework for Inline Parallel Multi-Sequence Reconstructions: A Head Motion Correction Application

Zihan Ning1, Sarah McElroy1,2, Sara Neves Silva1, Yannick Brackenier1, Rebecca E Thornley1,3, Liane S Canas1, Anthony N Price1,3, Jon Cleary1,3, Lucilio Cordero Grande4, Donald J Tournier1, Jordina Aviles Verdera1, Jana Hutter1,3, Philippa Bridgen3,5, Pierluigi Di Cio3,5, Michela Cleri3,5, Marc Modat1, Claire J Steves3,6, and Joseph V Hajnal1
1Imaging physics and engineering research department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, 3Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom, 4Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain, 5London Collaborative Ultra high field System (LoCUS), Kings College London, London, United Kingdom, 6Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

Synopsis

Keywords: Software Tools, Software Tools, Generalized framework, Inline reconstruction, Motion Correction

Motivation: Offline MRI reconstruction limits clinical applicability due to lengthy processing-times and lack integration with other scanner-data. While Gadgetron enables customised inline reconstruction, it may introduce scanner-reconstruction delays with multi-sequence implementation when methods are time-consuming.

Goal(s): To develop a generalized framework for inline reconstruction capable of parallel multi-sequence operation without interrupting subsequent scans or scanner-reconstructions.

Approach: A Gadgetron-based, multi-GPU framework was developed and implemented with DISORDER motion correction reconstruction across three 3D neuroimaging sequences to validate feasibility and robustness.

Results: The framework demonstrated feasibility and high robustness (success in 271/273 cases) with DISORDER reconstruction implementation, effectively reducing motion artifacts while maintaining high image quality.

Impact: This generalized inline framework enables advanced but time-consuming, customised multi-sequence reconstructions within an MR examination without acquisition or scanner-reconstruction delays. It demonstrated high robustness and is extensible for future integration of customised methods across scanners via centralized servers.

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Keywords