Quantitative Magnetic Resonance Relaxometry and Diffusometry

定量磁共振弛豫测量和扩散测量

• 批准号: RGPIN-2018-06785
• 负责人: Xiao, Dan
• 金额: $ 1.75万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679945
• 关键词: Quantitative; Magnetic; Resonance; Relaxometry; Diffusometry

Magnetic resonance imaging (MRI) is well known as a flexible and powerful non-invasive diagnostic imaging technique available to clinical medicine. MRI as a largely qualitative tool is well established, principally used to visualize the internal structure of biological systems through mapping the spatial position of hydrogen. The magnetic resonance signal lifetimes (relaxation times) and molecular diffusion usually manifest qualitatively in the MR image contrast. Quantitative analysis of these parameters will reveal molecular scale information, since magnetic resonance is sensitive to a range of physical/chemical characteristics including molecular structure, motion, size and interactions, through the measurements of relaxation times (relaxometry) and molecular diffusion (diffusometry). The potential for quantitative MR has been recognized. However, significant methodological developments are still required for this potential to be fully realized. ****** Our goal is to quantitatively map the relaxation and diffusion correlations with conventional MRI instruments, achieving a spatial resolution beyond the limits set by traditional methods, as well as quantitative analysis of tissue structure with spot measurements utilizing a low cost handheld MR scanner. We will exploit artificial neural network (deep learning) based data processing in both scenarios to overcome the challenges inherent in these measurements and to enable efficient recovery of underlying tissue quantity and property.****** Quantitative and robust relaxometry and diffusometry will empower magnetic resonance and bring it to a new level as an analytical methodology and significantly enlarge its capacity in disease detection and quantification.****** Because of the inter-disciplinary nature of the work, involving biology and computer science as well as physics, the research shows promise of significant relevance to practical problems in these and other fields. Collaboration with colleagues at the University of Windsor and elsewhere will enable these avenues to be explored.

磁共振成像（MRI）是一种灵活而强大的非侵入性诊断成像技术，可用于临床医学。MRI作为一种主要的定性工具已经很好地建立起来，主要用于通过绘制氢的空间位置来可视化生物系统的内部结构。磁共振信号寿命（弛豫时间）和分子扩散通常在MR图像对比度中定性显示。这些参数的定量分析将揭示分子尺度的信息，因为磁共振是敏感的物理/化学特性，包括分子结构，运动，大小和相互作用，通过测量弛豫时间（弛豫）和分子扩散（扩散）。定量MR的潜力已经得到认可。然而，要充分实现这一潜力，仍需在方法上取得重大进展。** 我们的目标是用常规MRI仪器定量绘制弛豫和扩散相关性，实现超出传统方法限制的空间分辨率，以及利用低成本手持式MR扫描仪进行点测量对组织结构进行定量分析。我们将在这两种情况下利用基于人工神经网络（深度学习）的数据处理，以克服这些测量中固有的挑战，并有效恢复底层组织的数量和属性。定量和稳健的弛豫测量法和扩散测量法将增强磁共振的能力，并将其作为一种分析方法提高到一个新的水平，并显着扩大其在疾病检测和量化方面的能力。由于这项工作的跨学科性质，涉及生物学、计算机科学以及物理学，因此该研究显示出与这些领域和其他领域的实际问题具有重要相关性的希望。与温莎大学和其他地方的同事合作将使这些途径得以探索。