Robust quantitative MR imaging markers of response to therapy in Crohn’s Disease

克罗恩病治疗反应的稳健定量 MR 成像标记

• 批准号: 10536634
• 负责人: Sila Kurugol
• 金额: $ 39.83万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10536634
• 关键词: Abdominal Pain; Acceleration; Adult; Affect; Aftercare; Biological; Biological Markers; Blood capillaries; Brain; Cell Proliferation; Characteristics; Child; Clinical; Clinical Markers; Coin; Compensation; Computer software; Contrast Media; Crohn' s disease; Deposition; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Digestive System Disorders; Disease; Disease Management; Disease remission; Endoscopy; Estimation Techniques; Evaluation; Fever; Gadolinium; Goals; Health Care Costs; Hemorrhagic colitis; Hospital Costs; Hospitalization; Image; Image Analysis; Image Enhancement; Imaging Techniques; Immunomodulators; Individual; Inflammation; Intestines; Ionizing radiation; Laboratory Markers; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Malaise; Maps; Measurement; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Mucous Membrane; Operative Surgical Procedures; Outcome; Parameter Estimation; Patient Outcomes Assessments; Patients; Perfusion; Pharmaceutical Preparations; Physiological; Predisposition; Prevalence; Procedures; Protocols documentation; Quality of life; Radiation; Relapse; Reporting; Reproducibility; Respiration; Safety; Slice; Software Tools; Standardization; Structure; Symptoms; Techniques; Testing; Time; Tissues; Treatment Efficacy; burden of illness; cell motility; chronic inflammatory disease; clinical decision-making; clinical practice; clinical remission; contrast enhanced; deep learning; deep learning model; density; diagnostic accuracy; economic impact; efficacy evaluation; gut inflammation; healing; imaging biomarker; imaging modality; imaging software; improved; indexing; individual patient; individualized medicine; molecular marker; novel; open source; quantitative imaging; response; response biomarker; soft tissue; software development; success; tool; treatment response; treatment strategy; water diffusion

Project Summary: Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract that has a prevalence of over 800,000 in the US alone. The economic impact is disproportionally high because it affects primarily young individuals. The characteristic periods of remission and relapse necessitate frequent hospitalizations. There has been a recent shift in clinical practice from a reactive to a proactive CD treatment strategy, recently coined as “treat-to-target”, where patients are treated to achieve not only an initial response-to-therapy, but also longer clinical remissions and improved mucosal healing. However, this approach requires the regular assessment of disease activity using objective markers enabling treatments to be tailored to the individual patient. Therefore, there is an unmet need for new tools to improve diagnostic accuracy, to quantify disease burden, and to monitor treatment efficacy. Our application in response to PAR-19-056, “Robust quantitative MR imaging markers of response to therapy in Crohn's disease” is aimed at developing and evaluating noninvasive, contrast and radiation-free quantitative imaging markers for assessing disease activity and for monitoring response to therapy. Currently available non-contrast MR imaging (MRI) sequences such as diffusion-weighted MRI (DW-MRI) and the calculated apparent diffusion coefficient (ADC) maps are attractive but limited in providing robust and reproducible markers. Different imaging protocols or different scanners result in different ADC values. Our primary goal is to develop robust and reliable quantitative DW-MR imaging markers for the non-contrast and non-invasive assessment of CD. The proposed novel MRI acquisition and model fitting techniques will provide measurements of slow and fast diffusion as well as fraction of fast diffusion, as highly accurate, quantitative biomarkers for cell proliferation, density and size, and tissue perfusion—all indices that characterize the extent of disease activity (i.e., inflammation) in the tissue micro-structure of the bowel. To achieve this goal, we will first develop and implement an advanced distortion and motion corrected (DiMoCo) DW-MRI technique and a spatially constrained probabilistic intravoxel incoherent motion (SPIM) model to compute robust and reproducible markers. Next, we will reduce the imaging time with estimated x4 acceleration with an accelerated image acquisition and a new, advanced deep learning-based parameter estimation technique. The proposed imaging techniques and software tools will provide robust quantitative markers, thereby enabling the accurate assessment of CD activity and response to therapy. They will also reduce the need for invasive endoscopy procedures as well as the total number of other tests typically ordered for monitoring disease, effectively reducing both the disease burden and the overall cost of healthcare. Another important goal is to develop and broadly disseminate open source software that will enable the standardized evaluation of other diseases presently evaluated with DW-MRI that would benefit from the advanced diagnostic and assessment capabilities of the proposed DiMoCo SPIM-DW-MRI technique.

项目摘要： 克罗恩病（CD）是胃肠道的一种慢性炎症性疾病，患病率是 仅在美国，就超过80万。经济影响过高，因为它影响了初级年轻人 个人。经常住院的特征时期经常住院。那里 最近从反应性转变为主要创造的临床实践转变为主动的CD治疗策略 作为“治疗至目标”，对患者进行治疗，不仅可以实现对治疗的初步反应，还可以实现 更长的临床缓解和改善的粘膜愈合。但是，这种方法需要常规 使用目标标记物评估疾病活动的评估，使治疗能够为个人量身定制 病人。因此，有未满足的新工具可以提高诊断准确性，以量化疾病 负担，并监控治疗效率。我们响应第19段的申请，“强大 定量的MR成像标志物在克罗恩病中对治疗的反应标记”旨在发展 并评估无创，对比和无辐射定量成像标记以进行评估 疾病活动和监测对治疗的反应。目前可用的非对比度MR成像 （MRI）序列，例如扩散加权MRI（DW-MRI）和计算出的明显扩散系数 （ADC）地图具有吸引力，但在提供健壮和可重复的标记方面有限。不同的成像协议 或不同的扫描仪会导致不同的ADC值。我们的主要目标是发展强大而可靠 CD的非对比度和非侵入性评估的定量DW-MR成像标记。提议 新颖的MRI获取和模型拟合技术也将提供缓慢和快速扩散的测量 作为快速扩散的一部分，作为细胞增殖，密度和大小的高度准确的定量生物标志物， 和组织灌注 - 所有表征组织中疾病活性程度（即炎症）的索引 肠道的微结构。为了实现这一目标，我们将首先发展并实施高级失真 和运动校正（DIMOCO）DW-MRI技术和一种空间约束的概率Intravoxel 不一致的运动（SPIM）模型，以计算健壮和可再现标记。接下来，我们将减少 估计的X4加速度成像，并获得加速图像采集和新的高级深层 基于学习的参数估计技术。提出的成像技术和软件工具将 提供强大的定量标记，从而可以准确评估CD活性并响应 治疗。他们还将减少对侵入性内窥镜程序的需求以及其他的总数 通常订购的测试以监测疾病，有效地减少了疾病燃烧和整体成本 医疗保健。另一个重要目标是开发和广泛传播开源软件 启用对通过DW-MRI评估的其他疾病的标准化评估，该疾病将受益于 提出的DIMOCO SPIM-DW-MRI技术的高级诊断和评估功能。