Real-time motion correction for Magnetic Resonance Imaging of the human brain at 7 Tesla

Winata, Steven (2023) Real-time motion correction for Magnetic Resonance Imaging of the human brain at 7 Tesla. PhD thesis, University of Glasgow.

Abstract

Magnetic resonance imaging (MRI) at the magnetic field strength of 7 Tesla (7T) enhances the quality of images available for research and clinical use. The improvements are however accompanied by novel challenges that are specific to ultra high-field MRI, which includes field strengths of 7T and above. Transmit B +1 field inhomogeneity is also higher, causing uneven signal intensity and linking to an uneven SAR distribution, which is also higher than at lower field strengths. The potential for higher spatial resolution imaging can also result in more pronounced motion artefacts. To address these issues in routine clinical use, motion correction strategies are required. This thesis describe the implementation of real-time, image-based Multislice Prospective Acquisition Correction (MS-PACE) technique for 7T MRI. Firstly, developmental work was done to establish a 7T-specific MS-PACE implementation. Pulse sequence and image reconstruction pipeline work was implemented using the Siemens Integrated Development Environment for Applications (IDEA) and Image Calculation Environment (ICE) framework. The technique was then validated in a task-based functional MRI study with healthy subjects. It was also integrated with parallel transmit imaging using slice-by-slice B+1 shimming. Validation experiments were performed in vivo using the Siemens MAGNETOM Terra 7T MRI scanner (Siemens Healthineers, Erlangen, Germany) at the Imaging Centre of Excellence (ICE).

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry; T Technology > T Technology (General)
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Supervisor's Name: Porter, Professor David and Muir, Professor Keith
• Date of Award: 2023
• Depositing User: Theses Team
• Unique ID: glathesis:2023-83788
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 31 Aug 2023 10:19
• Last Modified: 31 Aug 2023 10:21
• Thesis DOI: 10.5525/gla.thesis.83788
• URI: https://theses.gla.ac.uk/id/eprint/83788