TR&D1: Ultra-Fast Multiparametric MR Imaging Methods for Quantitative PET/MR

基本信息

批准号：
10651766
负责人：
Georges El Fakhri
金额：
$ 34.67万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10651766
关键词：
3-Dimensional Acceleration Affect Algorithms Biological Markers CYP1A1 gene CYP1A2 gene Cardiac Cardiotoxicity Characteristics Clinical Collaborations Compensation Complex Computing Methodologies Data Degenerative polyarthritis Demyelinations Disease Family suidae Fibrosis Functional Imaging Funding Heart Heart failure Image Imaging Techniques Imaging technology Immune response Individual Inflammation Label Lipids Magnetic Resonance Imaging Malignant Neoplasms Maps Measures Membrane Potentials Metabolic Metabolism Methodology Methods Modeling Molecular Morphologic artifacts Motion Multiple Sclerosis Myelin Water Imaging Myocardial Necrosis Noise Pathogenesis Performance Physics Physiological Positron-Emission Tomography Radiation therapy Research Resolution Running Sampling Software Tools Structure Techniques Technology Time Tissue Model Tissues Water Work attenuation cardiac magnetic resonance imaging clinical practice computerized data processing data acquisition data framework data space deep learning extracellular ferumoxytol human data human subject image reconstruction image translation imaging biomarker imaging modality improved learning strategy magnetic resonance spectroscopic imaging molecular imaging monocyte multimodality new technology novel reconstruction remyelination respiratory sarcoma sequence learning trafficking transmission process tumor

项目摘要

Combining quantitative MR (qMR) with PET synergistically provides perfectly registered, complementary physiological biomarkers. Our pioneering work during the initial funding cycle, Y1-4, of our P41 in quantitative PET/MR, has provided solutions to some of the most vexing problems affecting PET/MR, including reliable and accurate corrections for attenuation correction and involuntary subject motion. These techniques achieve PET resolution close to the intrinsic resolution of the scanner and enable a range of novel PET/MR applications. In this renewal application, we will develop and deliver unique PET/MR imaging technologies that not only enable physiological high- impact novel PET/MR but will also benefit standalone MR research. We focus our efforts on multi- parametric qMR techniques by leveraging our recent breakthroughs on subspace/manifold modeling, MR-physics informed deep learning (DL), and multi-contrast ultra-fast MR sequences. Our proposed research focuses on developing and delivering new PET/MR imaging techniques along three axes: i) multi-parametric, free-running, cardiac MRI method for motion corrected whole-heart membrane potential imaging using PET/MR; ii) high resolution MRSI for multi-modality, multi-parametric, molecular imaging using PET/MR; and iii) ultra-fast, multi-component quantitative MR using deep learning and MR physics modeling. We will validate the proposed methods in TR&D3 PET/MR axes of research and through collaborations with our Collaborative Projects. We will leverage the DL methodologies proposed in TR&D2 to further improve image quality of quantitative PET/MR techniques in TR&D1.

将定量MR（QMR）与PET协同作用相结合，提供完美的注册，互补的生理生物标志物。我们在最初的资金周期中的开创性工作，Y1-4，我们的p41在定量宠物/MR中，为一些最烦人的问题提供了解决方案影响宠物/MR，包括可靠和准确的衰减校正校正和非自愿运动。这些技术可实现接近固有分辨率的宠物分辨率扫描仪，并启用一系列新型PET/MR应用。在此续订应用程序中，我们将开发和提供独特的宠物/MR成像技术，不仅可以使生理高级影响新颖的宠物/MR，但也将使独立的MR研究受益。我们将精力集中于多参数QMR技术通过利用我们最近在子空间/歧管建模上的突破， MR-PHYSICS告知深度学习（DL）和多对比度超快速MR序列。我们提出的研究重点是沿三个轴开发和交付新的宠物/MR成像技术：i）多参数，自由运行的心脏MRI运动方法校正了全心膜使用PET/MR进行潜在成像； ii）高分辨率MRSI用于多模式，多参数，使用PET/MR进行分子成像； iii）使用深层的超快速，多组分定量MR 学习和MR物理建模。我们将验证TR＆D3 PET/MR轴中提出的方法研究以及通过与我们的协作项目合作。我们将利用DL 在TR＆D2中提出的方法学进一步提高了定量PET/MR的图像质量 TR＆D1中的技术。