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: 10668430
• 负责人: Maria I. Altbach
• 金额: $ 57.49万
• 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/10668430
• 关键词: Abdomen; Acceleration; Adoption; Area; Arizona; Attention; Biological Markers; Biomedical Engineering; Biopsy; Breathing; California; Cells; Cessation of life; Characteristics; Cirrhosis; Classification; Clinic; Clinical; Compensation; Data; Detection; Development; Diagnosis; Disease; Early Diagnosis; Fatty Liver; Fatty acid glycerol esters; Fibrosis; Future; Goals; Health Care Costs; Hepatic; Image Enhancement; Inflammation; Interdisciplinary Study; Interobserver Variability; Iron Overload; Joints; Label; 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; Primary carcinoma of the liver cells; 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; 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.

项目总结