Developing a rapid MRI technique for simultaneous structural and functional susceptibility mapping

开发用于同步结构和功能磁化率绘图的快速 MRI 技术

• 批准号: 2417003
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2417003
• 关键词: Developing; rapid; MRI; technique; simultaneous

1) Brief description of the context of the research including potential impactMRI is indispensable in the diagnosis of neurodegenerative diseases. These are poorly understood while their prevalence and socio-economic burden continue to rise. Structural and functional Magnetic Resonance Imaging (MRI) can provide biomarkers for early diagnosis and potential therapeutic intervention in neurodegenerative diseases. The vision for this research is to optimise MRI methods for simultaneous structural and functional mapping of tissue magnetic susceptibility as quantitative susceptibility mapping (QSM) has shown promise for neuroimaging, revealing changes in brain tissue composition in diseases such as Parkinson's and Alzheimer's disease (AD). Functional QSM may provide even more accurate localisation of brain activity than standard functional MRI but is extremely challenging, requiring specialised physiological noise removal. The rapid, efficient integrated scan developed in this research will be ideal for AD patients. It has the potential to provide a rich set of novel, multimodal MRI contrasts (including conductivity mapping) to allow development of new combined structural and functional biomarkers for early diagnosis of AD and other diseases.2) Aims and ObjectivesTo optimise MRI acquisition and QSM processing methods to provide simultaneous structural and functional susceptibility maps in a much shorter time than typical gradient-echo MRI pulse sequences used for QSM. The MRI pulse sequence will be designed so that it can also be used for electrical conductivity mapping.The specific objectives are to:- Design and build a phantom (MRI test object) with several compartments with known tissue equivalent magnetic susceptibilities- Develop and test rapid MRI pulse sequences such as multi-echo echo-planar imaging (ME-EPI) suitable for both structural and functional QSM- Develop and optimise structural QSM techniques for ME-EPI data acquired in healthy volunteers: this may involve incorporating new regularisation methods for this inverse problem and deep-learning based techniques.- Develop and test image processing techniques for resting-state functional QSM- Develop and optimise physiological noise removal methods for functional QSMThe ultimate goal is to apply the optimised sequence and processing techniques in a cohort of early AD patients and age-matched controls to observe whether any differences can be detected between these groups. The optimisation of MRI acquisition pulse sequences and QSM algorithms will be carried out in both phantoms and healthy volunteers. The student will work primarily at the 3 Tesla Prisma MRI system at the National Hospital for Neurology and Neurosurgery. QSM is based on the phase (time-evolution ) of the complex MRI signal so the phase (offset) can be used for conductivitymapping and the magnitude signal (used for conventional imaging) is still available and can be utilised for standard T2*-weighted imaging and standard functional MRI with no extra scan time cost.3)Novelty of Research MethodologyThe student will develop rapid acquisition and processing techniques optimised for both accurate structural and functional QSM. Novel MRI pulse sequences will be developed and tested incorporating new image acceleration techniques. Resting-state functional QSM is extremely challenging so we will develop novel techniques for correction of artifacts due to e.g. motion and /or geometric image distortion as well as physiological noise removal techniques. Novel QSM reconstruction methods / algorithms will be developed to process the resulting images to produce accurate structural and functional susceptibility maps.4)Alignment to strategies and research areasHealthcare technologies (aims to accelerate and translate research to healthcare applications). The specific research area is Medical Imaging and potentially artificial intelligence technologies.5) No collaborators.

1) Brief description of the context of the research including potential impactMRI is indispensable in the diagnosis of neurodegenerative diseases. These are poorly understood while their prevalence and socio-economic burden continue to rise. Structural and functional Magnetic Resonance Imaging (MRI) can provide biomarkers for early diagnosis and potential therapeutic intervention in neurodegenerative diseases. The vision for this research is to optimise MRI methods for simultaneous structural and functional mapping of tissue magnetic susceptibility as quantitative susceptibility mapping (QSM) has shown promise for neuroimaging, revealing changes in brain tissue composition in diseases such as Parkinson's and Alzheimer's disease (AD). Functional QSM may provide even more accurate localisation of brain activity than standard functional MRI but is extremely challenging, requiring specialised physiological noise removal. The rapid, efficient integrated scan developed in this research will be ideal for AD patients. It has the potential to provide a rich set of novel, multimodal MRI contrasts (including conductivity mapping) to allow development of new combined structural and functional biomarkers for early diagnosis of AD and other diseases.2) Aims and ObjectivesTo optimise MRI acquisition and QSM processing methods to provide simultaneous structural and functional susceptibility maps in a much shorter time than typical gradient-echo MRI pulse sequences used for QSM. The MRI pulse sequence will be designed so that it can also be used for electrical conductivity mapping.The specific objectives are to:- Design and build a phantom (MRI test object) with several compartments with known tissue equivalent magnetic susceptibilities- Develop and test rapid MRI pulse sequences such as multi-echo echo-planar imaging (ME-EPI) suitable for both structural and functional QSM- Develop and optimise structural QSM techniques for ME-EPI data acquired in healthy volunteers: this may involve incorporating new regularisation methods for this inverse problem and deep-learning based techniques.- Develop and test image processing techniques for resting-state functional QSM- Develop and optimise physiological noise removal methods for functional QSMThe ultimate goal is to apply the optimised sequence and processing techniques in a cohort of early AD patients and age-matched controls to observe whether any differences can be detected between these groups. The optimisation of MRI acquisition pulse sequences and QSM algorithms will be carried out in both phantoms and healthy volunteers. The student will work primarily at the 3 Tesla Prisma MRI system at the National Hospital for Neurology and Neurosurgery. QSM is based on the phase (time-evolution ) of the complex MRI signal so the phase (offset) can be used for conductivitymapping and the magnitude signal (used for conventional imaging) is still available and can be utilised for standard T2*-weighted imaging and standard functional MRI with no extra scan time cost.3)Novelty of Research MethodologyThe student will develop rapid acquisition and processing techniques optimised for both accurate structural and functional QSM. Novel MRI pulse sequences will be developed and tested incorporating new image acceleration techniques. Resting-state functional QSM is extremely challenging so we will develop novel techniques for correction of artifacts due to e.g. motion and /or geometric image distortion as well as physiological noise removal techniques. Novel QSM reconstruction methods / algorithms will be developed to process the resulting images to produce accurate structural and functional susceptibility maps.4)Alignment to strategies and research areasHealthcare technologies (aims to accelerate and translate research to healthcare applications). The specific research area is Medical Imaging and potentially artificial intelligence technologies.5) No collaborators.