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
基本信息
- 批准号:10453927
- 负责人:
- 金额:$ 60.83万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AbdomenAdoptionAreaArizonaAttentionBiological MarkersBiomedical EngineeringBiopsyBreathingCaliforniaCellsCessation of lifeCharacteristicsCirrhosisClassificationClinicClinicalDataDetectionDevelopmentDiagnosisDiseaseFatty LiverFatty acid glycerol estersFibrosisFinancial compensationFutureGoalsHealth Care CostsHepaticImage EnhancementImaging TechniquesInflammationInterdisciplinary StudyInterobserver VariabilityIron OverloadJointsLabelLeadLifeLiverLiver FailureLiver FibrosisLiver parenchymaLos AngelesMRI ScansMagnetic ResonanceMagnetic Resonance ElastographyMagnetic Resonance ImagingMapsMeasuresMedical ImagingModelingMonitorMorbidity - disease rateMorphologic artifactsMotionOutcomePatientsPerformancePerfusionPhysicsProtocols documentationProtonsROC CurveRadialReproducibilityResearchRisk FactorsSampling ErrorsScanningSchemeSignal TransductionStagingTechniquesTechnologyTestingTimeTissuesTrainingUncertaintyUniversitiesValidationWaterbasechronic liver diseasecontrast enhanceddeep learningdeep learning modeldensitydisease classificationelastographyhepatocyte injuryimage processingimprovedlearning strategyliver biopsyliver inflammationmagnetic resonance imaging biomarkernew technologynon-alcoholic fatty livernon-alcoholic fatty liver diseasenonalcoholic steatohepatitisnovel therapeuticsprospectiveprospective testreconstructionsensorsimple steatosistreatment response
项目摘要
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Maria I. Altbach其他文献
Maria I. Altbach的其他文献
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{{ truncateString('Maria I. Altbach', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 60.83万 - 项目类别:
Multi-Center Implementation and Validation of Efficient Magnetic Resonance Imaging and Analysis of Atherosclerotic Disease of the Cervical Carotid
颈动脉粥样硬化疾病高效磁共振成像和分析的多中心实施和验证
- 批准号:
10280858 - 财政年份:2021
- 资助金额:
$ 60.83万 - 项目类别:
Multi-Center Implementation and Validation of Efficient Magnetic Resonance Imaging and Analysis of Atherosclerotic Disease of the Cervical Carotid
颈动脉粥样硬化疾病高效磁共振成像和分析的多中心实施和验证
- 批准号:
10684192 - 财政年份:2021
- 资助金额:
$ 60.83万 - 项目类别:
Advancing MRI technology for early diagnosis of liver metastases
推进 MRI 技术用于肝转移的早期诊断
- 批准号:
10320434 - 财政年份:2019
- 资助金额:
$ 60.83万 - 项目类别:
Advancing MRI technology for early diagnosis of liver metastases
推进 MRI 技术用于肝转移的早期诊断
- 批准号:
10524177 - 财政年份:2019
- 资助金额:
$ 60.83万 - 项目类别:
Advancing MRI technology for early diagnosis of liver metastases
推进 MRI 技术用于肝转移的早期诊断
- 批准号:
10531585 - 财政年份:2019
- 资助金额:
$ 60.83万 - 项目类别:
Advancing MRI technology for early diagnosis of liver metastases
推进 MRI 技术用于肝转移的早期诊断
- 批准号:
10063981 - 财政年份:2019
- 资助金额:
$ 60.83万 - 项目类别:
Detection of Lipid Infiltration in the Heart with MRI
MRI 检测心脏脂质浸润
- 批准号:
7261647 - 财政年份:2007
- 资助金额:
$ 60.83万 - 项目类别:
Detection of Lipid Infiltration in the Heart with MRI
MRI 检测心脏脂质浸润
- 批准号:
7595080 - 财政年份:2007
- 资助金额:
$ 60.83万 - 项目类别:
Detection of Lipid Infiltration in the Heart with MRI
MRI 检测心脏脂质浸润
- 批准号:
7391543 - 财政年份:2007
- 资助金额:
$ 60.83万 - 项目类别:
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