Quantitative MRI and Deep Learning Technologies for Classification of NAFLD

用于 NAFLD 分类的定量 MRI 和深度学习技术

• 批准号: 10453927
• 负责人: Maria I. Altbach
• 金额: $ 60.83万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10453927
• 关键词: Abdomen; Adoption; Area; Arizona; Attention; Biological Markers; Biomedical Engineering; Biopsy; Breathing; California; Cells; Cessation of life; Characteristics; Cirrhosis; Classification; Clinic; Clinical; Data; Detection; Development; Diagnosis; Disease; Fatty Liver; Fatty acid glycerol esters; Fibrosis; Financial compensation; Future; Goals; Health Care Costs; Hepatic; Image Enhancement; Imaging Techniques; Inflammation; Interdisciplinary Study; Interobserver Variability; Iron Overload; Joints; Label; Lead; Life; Liver; Liver Failure; Liver Fibrosis; Liver parenchyma; Los Angeles; MRI Scans; Magnetic Resonance; Magnetic Resonance Elastography; Magnetic Resonance Imaging; Maps; Measures; Medical Imaging; Modeling; Monitor; Morbidity - disease rate; Morphologic artifacts; Motion; Outcome; Patients; Performance; Perfusion; Physics; Protocols documentation; Protons; ROC Curve; Radial; Reproducibility; Research; Risk Factors; Sampling Errors; Scanning; Scheme; Signal Transduction; Staging; Techniques; Technology; Testing; Time; Tissues; Training; Uncertainty; Universities; Validation; Water; base; chronic liver disease; contrast enhanced; deep learning; deep learning model; density; disease classification; elastography; hepatocyte injury; image processing; improved; learning strategy; liver biopsy; liver inflammation; magnetic resonance imaging biomarker; new technology; non-alcoholic fatty liver; non-alcoholic fatty liver disease; nonalcoholic steatohepatitis; novel therapeutics; prospective; prospective test; reconstruction; sensor; simple steatosis; treatment response

PROJECT SUMMARY Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the U.S. and ranges from simple fatty liver (or non-alcoholic fatty liver, NAFL) to the progressive form, non-alcoholic steatohepatitis (NASH). About 20-30% of subjects with NAFL develop NASH, which is caused by hepatocyte injury, hepatic inflammation, and resultant hepatic fibrosis. NASH can lead to life-threatening conditions, but is difficult to diagnose at early stages. Liver biopsy is the current standard to diagnose NAFL/NASH, but biopsy is invasive, has associated morbidity, and is limited by sampling errors and inter-observer variability. Many patients present with later stage NASH, adversely impacting outcomes and healthcare costs, which are estimated at $32 billion annually in the U.S. Magnetic resonance imaging (MRI), including elastography (MRE), is a technology that can non-invasively quantify hepatic fat (MRI proton-density fat fraction), iron overload (MRI R2*), and fibrosis (MRE stiffness). However, current liver MRI is challenged by motion artifacts and incomplete signal models, which can compromise the accuracy and reproducibility of the quantitative parameters derived from them. In addition, early tissue changes associated with NASH are not adequately characterized using conventional MRI. The common requirements of breath-holding and long protocols also severely limit the adoption of liver MRI in the clinic. Furthermore, the present clinical interpretation of MRI has limited ability to distinguish NASH from NAFL. The research teams at the University of California Los Angeles, University of Arizona, and Siemens have been leading the development of motion-robust radial MRI to quantify hepatic PDFF and R2*, T2 and T1, perfusion, and stiffness. The Siemens team has also developed deep learning methods for medical image processing and disease detection and classification. In this bioengineering research partnership project, the multi-disciplinary research team will investigate four aims: (1) Develop a robust motion compensation framework for free-breathing multi-parametric quantitative radial liver MRI; (2) Accelerate quantitative liver MRI scans through combined acquisition and joint modeling of multiple parameters, data undersampling, and deep learning-based reconstruction and quantification; (3) Develop deep learning models to accurately classify NAFL versus NASH and measure the degree of fibrosis based on quantitative MRI; (4) Prospectively assess the new quantitative MRI and deep learning technologies for classifying NAFL versus NASH and measuring fibrosis in patients, with respect to liver biopsy. The new free- breathing quantitative MRI and deep learning technologies developed in this project will accurately classify NAFL versus NASH and measure fibrosis using data from the entire liver and thus help to avoid liver biopsy, allow monitoring of treatment responses, and accelerate the development and implementation of new therapies.

项目摘要 非酒精性脂肪肝病（NAFLD）是美国最常见的慢性肝病和范围 从简单的脂肪肝（或非酒精脂肪肝，NAFL）到进行性形式，非酒精性脂肪性肝炎 （纳什）。大约20-30％的NAFL受试者发展为NASH，这是由肝细胞损伤引起的 炎症和导致的肝纤维化。纳什可能导致威胁生命的条件，但很难 在早期诊断。肝活检是诊断NAFL/NASH的当前标准，但活检是侵入性的， 有关联的发病率，并且受到采样错误和观察者间变异性的限制。许多患者 在以后的纳什阶段，不利影响成果和医疗保健费用，估计 每年320亿美元在美国磁共振成像（MRI）（包括弹性图（MRE））是一个 可以非侵入性量化肝脂肪（MRI质子密度分数），铁超载的技术（MRI R2*）和纤维化（MRE刚度）。但是，当前的肝脏MRI受到运动伪像挑战和不完整 信号模型，这可能损害定量参数的准确性和可重复性 从他们那里。此外，与NASH相关的早期组织变化没有充分表征 常规MRI。呼吸持续和长协议的共同要求也严重限制了 在诊所采用肝脏MRI。此外，MRI的当前临床解释的能力有限 区分纳什和纳夫。加利福尼亚大学洛杉矶分校的研究团队，大学 亚利桑那州和西门子一直在领导运动刺激径向MRI的发展来量化肝 PDFF和R2*，T2和T1，灌注和刚度。西门子团队还发展了深度学习 医学图像处理和疾病检测和分类的方法。在这个生物工程中 研究伙伴关系项目，多学科研究团队将研究四个目标：（1）开发一个 自由呼吸多参数radial肝脏MRI的强大运动补偿框架； （2） 通过多个的联合采集和联合建模加速定量肝MRI扫描 参数，数据不足采样以及基于深度学习的重建和量化； （3）深度发展 学习模型，以准确对NAFL与NASH进行分类，并基于 定量MRI； （4）前瞻性评估新的定量MRI和深度学习技术 就肝活检对NAFL与NASH进行分类和测量患者的纤维化。新的自由 在本项目中开发的呼吸定量MRI和深度学习技术将准确对 NAFL与NASH的NAFL并使用来自整个肝脏的数据测量纤维化，从而有助于避免肝活检， 允许监视治疗反应，并加速新的新型 疗法。