Development of advanced magnetic resonance spectroscopic imaging techniques for rapid high-resolution mapping of brain metabolite levels

这项研究计划的重点是发展一种叫做磁共振光谱成像（MRSI）的脑成像技术。核磁共振成像（MRSI）与磁共振成像（MRI）密切相关，是一种鲜为人知的技术，用于无创性地绘制组织内各种自然产生的化学物质（称为代谢物）的水平。核磁共振成像通常用于大脑成像，目前是唯一可用于脑组织代谢物水平非侵入性测绘的工具。因此，核磁共振成像（MRSI）作为一种诊断工具提供了巨大的希望，可以检测癌症、阿尔茨海默病、抑郁症和精神分裂症等脑部疾病背后的化学失衡。

This proposed research program is focused on the development of a brain imaging technique called magnetic resonance spectroscopic imaging (MRSI). Closely related to magnetic resonance imaging (MRI), MRSI is a lesser-known technique for non-invasively mapping the levels of various naturally occurring chemical substances, called metabolites, within tissue. MRSI is often used to image the brain, and currently stands as the only tool available for non-invasive mapping of brain tissue metabolite levels. As such, MRSI offers great promise as a diagnostic tool to detect chemical imbalances that underlie brain diseases such as cancer, Alzheimer's disease, depression, and schizophrenia, to name a few. Although MRSI has been in existence for more than three decades, the vast majority of the MRSI acquisition methods still used today are limited by poor spatial resolution, long imaging times, and image contamination issues which can reduce the accuracy and precision of the acquired measurements. As a result, MRSI has so far failed to gain widespread acceptance as a routine diagnostic tool. However, recent major advances in MRSI acquisition technology have shown the potential to address many of the above limitations. Specifically, the next generation of MRSI image acquisition techniques will offer dramatic improvements in acquisition speed and spatial resolution, and will be able to produce more accurate and precise metabolite maps. With these major advancements, MRSI appears more likely to be adopted for routine clinical use. Currently, however, this next generation of rapid MRSI techniques is in its infancy and major developments are still needed. New software tools are needed to help simulate and design better acquisition methods, and to process and analyse the large high-resolution datasets that are produced. Therefore, the overall aim of this proposed work is to develop and distribute advanced tools for rapid high-resolution MRSI of the brain. Specifically, I propose: 1) to develop advanced open-source simulation tools to allow detailed characterization and optimization of newly designed rapid MRSI acquisition methods; 2) to develop and distribute advanced acquisition methods for MRSI of the brain on 3 Tesla and 7 Tesla scanners; and 3) to develop advanced open-source software tools for processing, visualization and analysis of MRSI data. The primary outcomes of this work will be to significantly advance the state-of-the-art in MRSI acquisition methods, and to bring MRSI closer to becoming a routine clinical tool for diagnosing diseases and improving the lives of Canadians.