@article{672619e400664e938875ad97f320c5b5,
title = "Data-driven motion-corrected brain MRI incorporating pose-dependent B0 fields",
abstract = "Purpose: To develop a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose-dependent changes in polarizing magnetic (B0) fields. Theory and Methods: Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics-inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data-driven motion correction is validated in simulations and in vivo. Results: The applicability of the physics-inspired B0 model was confirmed in vivo. Simulations show the need to include the pose-dependent B0 fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion: We have developed a motion-correction framework that accounts for and estimates pose-dependent B0 fields. The method improves current state-of-the-art data-driven motion-correction techniques when B0 dependencies cannot be neglected. The use of a compact physics-inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.",
keywords = "motion correction, parallel imaging, reconstruction, susceptibility-induced B variation, ultrahigh field",
author = "Yannick Brackenier and Lucilio Cordero-Grande and Raphael Tomi-Tricot and Thomas Wilkinson and Philippa Bridgen and Anthony Price and Malik, {Shaihan J.} and {De Vita}, Enrico and Hajnal, {Joseph V.}",
note = "Funding Information: The author(s) would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging [EP/S022104/1] and by core the funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z], Wellcome Trust Collaboration in Science Award [WT 201526/Z/16/Z], National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust, King's College London, NIHR Clinical Research Facility, and Comunidad de Madrid‐Spain under the line support for R&D projects for Beatriz Galindo researchers. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. For the purpose of open access, the author has applied a CC BY public copyright license to any Author‐Accepted Manuscript version arising from this submission. Funding Information: Funding was provided by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging, grant EP/S022104/1; and by core the funding from the Wellcome/EPSRC Centre for Medical Engineering, grant WT203148/Z/16/Z; the Wellcome Trust Collaboration in Science Award, grant WT 201526/Z/16/Z; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust, King's College London, NIHR Clinical Research Facility; and Comunidad de Madrid‐Spain under the line support for R&D projects for Beatriz Galindo researchers. Funding information Funding Information: information Funding was provided by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging, grant EP/S022104/1; and by core the funding from the Wellcome/EPSRC Centre for Medical Engineering, grant WT203148/Z/16/Z; the Wellcome Trust Collaboration in Science Award, grant WT 201526/Z/16/Z; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust, King's College London, NIHR Clinical Research Facility; and Comunidad de Madrid-Spain under the line support for R&D projects for Beatriz Galindo researchers.The author(s) would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging [EP/S022104/1] and by core the funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z], Wellcome Trust Collaboration in Science Award [WT 201526/Z/16/Z], National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust, King's College London, NIHR Clinical Research Facility, and Comunidad de Madrid-Spain under the line support for R&D projects for Beatriz Galindo researchers. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. For the purpose of open access, the author has applied a CC BY public copyright license to any Author-Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.",
year = "2022",
month = aug,
doi = "10.1002/mrm.29255",
language = "English",
volume = "88",
pages = "817--831",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
publisher = "WILEY-BLACKWELL",
number = "2",
}