Computational Tools for the Analysis of MRI Images in Type-1 Diabetes

用于分析 1 型糖尿病 MRI 图像的计算工具

• 批准号: 9260874
• 负责人: Iman Aganj
• 金额: $ 15.92万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9260874
• 关键词: Abdomen; Algorithm Design; Algorithmic Analysis; Algorithms; Anatomy; Animals; Area; Atlases; Autoimmune Process; Beta Cell; Biological Markers; Biological Preservation; Biometry; Brain imaging; Breathing; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Contrast Media; Data; Data Set; Databases; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Disease Progression; Effectiveness of Interventions; Endocrine; Etiology; Evaluation; Exhibits; Exocrine pancreas; Feasibility Studies; Foundations; Goals; Gold; Heterogeneity; Human; Human Development; Image; Image Analysis; Individual; Inflammation; Inflammatory; Infusion procedures; Institution; Insulin-Dependent Diabetes Mellitus; Interdisciplinary Study; Intervention; Investigation; Islets of Langerhans; Literature; Location; Longitudinal Studies; Magnetic Resonance Imaging; Magnetic nanoparticles; Maps; Measurement; Measures; Medical Imaging; Mentored Research Scientist Development Award; Mentors; Methods; Modeling; Morphology; Motion; Nature; Onset of illness; Organ; Outcome; Pancreas; Patients; Pattern; Phenotype; Physiological; Population; Positioning Attribute; Preventive therapy; Process; Recruitment Activity; Research; Research Design; Research Personnel; Research Training; Scanning; Series; Shapes; Source; Techniques; Technology; Testing; Time; Training; Translating; United States; base; bioimaging; career; cohort; computerized tools; cost; design; diabetes control; effective therapy; experience; experimental study; flexibility; image processing; image reconstruction; image registration; imaging study; improved; innovation; islet; macrophage; meetings; monocyte; non-invasive imaging; notch protein; novel; pancreas imaging; programs; prospective; public health relevance; skills; tool; treatment strategy

 DESCRIPTION (provided by applicant): This project aims to characterize the local changes in pancreatic islet inflammation (referred to as insulitis) and volume loss associated with type-1 diabetes (T1D) by developing novel computational image analysis algorithms that quantify such changes from magnetic resonance imaging (MRI) data. In the United States alone, as many as three million people may have T1D, with 80 new cases diagnosed every day, costing almost $15B annually (source: JDRF). Understanding the mechanisms of autoimmune destruction of ß cells at the organ level is important for developing new early diagnostic criteria and effective treatment strategies and preventative therapies. Clinical occultness of much of the autoimmune process, along with the difficult access to the location of the endocrine islets of Langerhans have slowed progress in understanding the etiology and progression of T1D. However, MRI alleviates this by permitting noninvasive, local measurement of pancreatic anatomy (such as the volume), in addition to insulitis via the use of magnetic nanoparticle (MNP) agents, making cross-sectional and longitudinal T1D imaging studies feasible. To that end, accurate correspondence among pancreatic regions of two or more images are required in order to compute 1) insulitis from pre/post- infusion MNP-MR images, 2) the progress of insulitis over time, and 3) the local change in pancreatic volume over time, in addition to 4) comparing all of these quantities across subjects. Such a point-wise correspondence is provided by image registration (alignment). This proposal aims to build on my background in brain image analysis and develop novel image registration (alignment) tools to accurately compute point- wise correspondence between pancreas images acquired from different subjects at different times, and subsequently use them in cross-sectional and longitudinal pancreatic imaging to develop new biomarkers, by locally tracking long-term inflammatory and volume changes in individuals with clinical and/or occult T1D. Specifically, we propose to develop an inherently-symmetric quasi-volume-preserving (QVP) non-rigid image registration algorithm for the pancreas, which, in contrast to the existing algorithms in the literature, avoids regional biases and the concomitant errors by defining a uniform objective function. Furthermore, the intergroup differences and intragroup variability are measured by constructing unbiased statistical pancreatic atlases of healthy and T1D cohorts, using a novel, improved group-wise registration algorithm. My long-term career goal is to establish and direct an inter-disciplinary research program at a top-notch academic institution, which will focus on developing creative approaches and innovative computational tools for processing biomedical images, in order to facilitate the investigation of the relationship between medical images and clinical data, and improve patient diagnosis and outcomes. My main objective for the K01 award period is to become an expert in T1D, in addition to abdominal - and especially pancreatic - MR acquisition and image analysis, and to advance this field by translating the skills I had previously acquired in brain image reconstruction and analysis. To achieve this goal, there are three important areas where I require additional training, mentoring, and experience: 1) diabetes, 2) abdominal imaging with contrast agents, and 3) advanced study design and biostatistics. I propose to acquire this training through direct mentoring, didactic coursework, modular courses, seminar series, and scientific meetings. The proposed project will form the foundation of my independent computational abdominal imaging research program, which will have diabetes at the core of its clinical focus.

 描述（由申请人提供）：该项目旨在通过开发新颖的计算图像分析算法来量化胰岛炎症（称为胰岛炎）和与 1 型糖尿病 (T1D) 相关的体积损失的局部变化，该算法可量化磁性变化仅在美国，就有多达 300 万人患有 T1D，每天诊断出 80 个新病例，每年花费近 15B 美元（来源：JDRF）。在器官水平上β细胞的自身免疫破坏机制对于制定新的早期诊断标准和有效的治疗策略以及预防性疗法非常重要，因为许多自身免疫过程的临床隐蔽性以及难以接近朗格汉斯内分泌岛的位置。减缓了对 T1D 病因和进展的了解，但除了胰岛炎之外，MRI 还允许对胰腺解剖结构（例如体积）进行无创局部测量，从而缓解了这一问题。通过使用磁性纳米颗粒 (MNP) 试剂，使横断面和纵向 T1D 成像研究成为可能，为此，需要两个或多个图像的胰腺区域之间的准确对应，以便计算 1) 前/后的胰岛炎。输注 MNP-MR 图像，2) 胰岛炎随时间的进展，3) 胰腺体积随时间的局部变化，此外 4) 比较受试者之间的所有这些量。该提案旨在以我在大脑图像分析方面的背景为基础，开发新颖的图像配准（对齐）工具，以准确计算在不同时间从不同受试者获取的胰腺图像之间的逐点对应关系。通过对临床和/或隐匿性 T1D 个体的长期炎症和体积变化进行局部跟踪，将它们用于横断面和纵向胰腺成像，以开发新的生物标志物。我们建议开发一种固有对称的准体积保留（QVP）胰腺的非刚性图像配准算法，与文献中的现有算法相比，它通过定义统一的目标函数来避免区域偏差和伴随的错误。此外，组间差异和组内变异性通过以下方式测量。使用一种新颖的、改进的分组配准算法构建健康和 T1D 队列的公正的统计胰腺图谱。我的长期职业目标是在一流的学术机构中建立和指导一个跨学科研究项目。该机构将专注于开发用于处理生物医学图像的创造性方法和创新计算工具，以促进医学图像与临床数据之间关系的研究，并改善患者的诊断和结果。我在 K01 奖项期间的主要目标是。除了腹部（尤其是胰腺）MR 采集和图像分析之外，要成为 T1D 领域的专家，并通过转化我之前在大脑图像重建和分析方面获得的技能来推进这一领域要实现这一目标，有三个目标。我需要额外培训的重要领域，我建议通过直接指导、教学课程、模块化课程、研讨会系列和科学导师会议来获得这种培训，拟议项目将构成我的独立计算腹部成像研究项目的基础，该项目将以糖尿病为临床重点。 。