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

 描述（由申请人提供）：本项目旨在通过开发新的计算图像分析算法来表征与1型糖尿病（T1 D）相关的胰岛炎症（称为胰岛炎）和体积损失的局部变化，该算法可根据磁共振成像（MRI）数据量化此类变化。仅在美国，就有多达300万人可能患有T1 D，每天诊断出80例新病例，每年花费近150亿美元（来源：JDRF）。在器官水平上了解自身免疫性破坏胰岛细胞的机制对于开发新的早期诊断标准和有效的治疗策略和预防性疗法非常重要。自身免疫过程的临床隐匿性，沿着难以进入内分泌胰岛的位置，减缓了对T1 D病因和进展的理解。然而，MRI通过允许非侵入性的，局部测量胰腺解剖结构（如体积），除了通过使用磁性纳米颗粒（MNP）剂的胰岛炎，使横截面和纵向T1 D成像研究可行。为此，需要两个或更多个图像的胰腺区域之间的准确对应，以便计算1）来自输注前/后MNP-MR图像的胰岛炎，2）胰岛炎随时间的进展，和3）胰腺体积随时间的局部变化，以及4）跨受试者比较所有这些量。这种逐点对应由图像配准（对准）提供。这个建议的目的是建立在我的背景在脑图像分析和开发新的图像配准（对准）工具来准确地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，并且随后将它们用于横截面和纵向胰腺成像以开发新的生物标志物，通过局部跟踪临床和/或隐匿性T1 D患者的长期炎症和体积变化。具体而言，我们建议开发一种固有对称的准体积保持（QVP）的非刚性图像配准算法的胰腺，其中，在文献中现有的算法相比，避免区域偏见和伴随的错误，通过定义一个统一的目标函数。此外，组间差异和组内变异性通过构建健康和T1 D队列的无偏统计胰腺图谱来测量，使用一种新的，改进的组配准算法。我的长期职业目标是在一流的学术机构建立和指导一个跨学科的研究项目，该项目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的计算工具，以促进医学图像和临床数据之间关系的调查，并改善患者诊断和结果。我在K 01奖期间的主要目标是成为T1 D专家，除了腹部-尤其是胰腺- MR采集和图像分析，并通过翻译我以前在脑图像重建和分析方面获得的技能来推进这一领域。为了实现这一目标，我需要在三个重要领域进行额外的培训、指导和经验：1）糖尿病，2）使用造影剂进行腹部成像，3）先进的研究设计和生物统计学。我建议通过直接指导，教学课程，模块课程，系列研讨会和科学会议来获得这种培训。拟议的项目将形成我的独立计算腹部成像研究计划的基础，该计划将糖尿病作为其临床重点的核心。

 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.