MODELING MRI-BASED TISSUE RELAXATION IN THE PRESENCE OF IRON OVERLOAD AND STEATOSIS

在存在铁超载和脂肪变性的情况下对基于 MRI 的组织松弛进行建模

• 批准号: 10395564
• 负责人: Aaryani Tipirneni-Sajja
• 金额: $ 29.08万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10395564
• 关键词: Accounting; Adult; Affect; American; Behavior; Biopsy; Blood Transfusion; Chronic; Cirrhosis; Clinical; Data; Deposition; Developed Countries; Development; Diffuse; Disease; Disease Progression; Early Diagnosis; Ensure; Fatty acid glycerol esters; Fibrosis; Goals; Health; Hepatic; Hereditary hemochromatosis; Histology; Histopathology; Imaging Techniques; Inherited; Insulin Resistance; Intervention; Investigation; Iron; Iron Overload; Lead; Link; Lipid Inclusion; Liver; Liver diseases; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Mediating; Metabolic syndrome; Methods; Microscopic; Microscopy; Modeling; Monitor; Monte Carlo Method; Morbidity - disease rate; Morphology; Obesity; Organ failure; Particle Size; Patients; Performance; Population; Predisposition; Protons; Publishing; Reference Standards; Relaxation; Research; Sickle Cell Anemia; Signal Transduction; Techniques; Therapeutic Intervention; Time; Tissue Model; Tissues; Toxic effect; Water; base; beta Thalassemia; cancer therapy; cohort; effective therapy; experimental study; in vivo; in vivo Model; liver transplantation; mortality; non-alcoholic fatty liver disease; noninvasive diagnosis; obesity in children; particle; prevent; simulation; tool; virtual

Project Summary Iron overload, either inherited or acquired through chronic blood transfusions, affects about 16 million Americans. Steatosis (`fat overload') affects one-third of the US population and is linked with obesity, insulin resistance, and metabolic syndromes. Co-occurrence of hepatic iron overload and steatosis is a common manifestation of diffuse liver diseases, chronic hepatopathies, and cancer therapy and can cause iron- and lipo-toxicity leading to progressive fibrosis, irreversible cirrhosis, and ultimately, organ failure. Magnetic resonance imaging (MRI) is a clinically important non-invasive tool for assessing hepatic iron overload and steatosis independently. However, in co-existing conditions, MRI quantification is often inaccurate due to confounding effects of iron and fat on MRI signal. Multi-spectral signal models accounting for these confounding effects have been proposed for simultaneous quantification of transverse relaxation rate (R2*), a predictor for iron content, and fat fraction (FF). However, these models were optimized and validated in only patients with steatosis and failed in different co- existing hepatic iron and fat overload conditions. The models assume either single or dual R2* for water and fat protons, and any incorrect assumptions or instabilities in the signal model produce errors in R2* and FF calculations, leading to misdiagnosis. The assumption to use single or dual R2* depends on the dephasing effects of in vivo iron deposits on water and fat protons, and these effects, in turn, depend on the size and distribution of iron and fat deposits on the microscopic scale. Previous simulation and phantom studies investigating the performances of multi-spectral signal models did not use a realistic tissue model. In simulation study, the sizes and distribution of iron and fat molecules were not considered, and in phantom studies, the sizes of iron particles and fat droplets did not match the scales of in vivo iron and fat deposits. Hence, there is a void in our understanding of how the true microscopic arrangement of iron and fat deposits in vivo will cause susceptibility-induced inhomogeneities and affect the macroscopic MRI signal relaxation. In this proposed research, we will perform a rigorous investigation for evaluating the contribution of size and distribution of iron and fat deposits on MRI signal via simulations, phantom experiments, and in vivo studies to determine an accurate MRI signal model for simultaneous and accurate assessment of iron overload and steatosis. We will (a) develop a Monte Carlo–based approach for creating virtual liver models with iron overload, steatosis, or both and simulating iron-proton interactions; (b) construct realistic phantoms with different particle sizes mimicking in vivo iron and fat deposits; and (c) validate MRI signal behavior in phantoms and retrospective patients by using biopsy assessments as a reference standard. This research will aid our understanding and quantification of iron and fat mediated relaxivity in tissues and therefore, will help us to develop and validate accurate signal models that can simultaneously quantify R2* and FF, thus enabling the noninvasive diagnosis of both iron overload and steatosis. 1

项目摘要 铁超载，无论是遗传还是通过慢性输血获得，影响着大约1600万美国人。 脂肪变性（“脂肪超载”）影响三分之一的美国人口，并与肥胖、胰岛素抵抗和肥胖有关。 代谢综合征肝脏铁超载和脂肪变性的共同发生是弥漫性肝硬化的常见表现。 肝脏疾病、慢性肝病和癌症治疗，并可导致铁和脂毒性， 进行性纤维化不可逆的肝硬化最终器官衰竭磁共振成像（MRI）是一种 独立评估肝脏铁过载和脂肪变性的临床重要非侵入性工具。然而，在这方面， 在共存条件下，由于铁和脂肪对MRI的混杂影响，MRI定量通常不准确 信号了已经提出了考虑这些混杂效应的多光谱信号模型， 同时定量横向弛豫率（R2*）、铁含量的预测因子和脂肪分数（FF）。 然而，这些模型仅在脂肪变性患者中进行了优化和验证，并在不同的合并症中失败。 存在肝脏铁和脂肪超载状况。模型假设水和脂肪的R2* 为单R2* 或双R2* 质子，以及信号模型中任何不正确的假设或不稳定性都会产生R2* 和FF中的误差 计算，导致误诊。使用单或双R2* 的假设取决于失相 体内铁沉积物对水和脂肪质子的影响，这些影响反过来取决于铁沉积物的大小和 铁和脂肪沉积物的微观分布。先前的模拟和体模研究 研究多光谱信号模型的性能没有使用真实的组织模型。仿真中 研究中，未考虑铁和脂肪分子的大小和分布，在体模研究中， 铁颗粒和脂肪滴的比例与体内铁和脂肪沉积的比例不匹配。因此， 在我们理解体内铁和脂肪沉积的真正微观排列将如何导致 磁化率引起的不均匀性，并影响宏观MRI信号弛豫。在此提出的 研究中，我们将进行严格的调查，以评估铁的尺寸和分布的贡献 通过模拟、体模实验和体内研究， 准确的MRI信号模型，用于同时准确评估铁过载和脂肪变性。我们将 (a)开发一种基于蒙特卡罗的方法，用于创建具有铁过载、脂肪变性或两者兼而有之的虚拟肝脏模型 并模拟铁-质子相互作用;（B）构建具有不同颗粒尺寸的逼真幻影， 体内铁和脂肪沉积;以及（c）通过使用以下方法验证幻影和回顾性患者中的MRI信号行为： 活检评估作为参考标准。这项研究将有助于我们了解和量化铁 和脂肪介导的弛豫，因此，将有助于我们开发和验证准确的信号模型 它可以同时量化R2* 和FF，从而实现铁过载和 脂肪变性 1