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

TR

• 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
• 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

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协同地提供了完美的注册， 互补的生理生物标记物。我们在最初的资金周期中的开创性工作，Y1-4， 我们在定量PET/MR中的P41，为一些最令人烦恼的问题提供了解决方案 影响PET/MR，包括可靠和准确的衰减校正和 非自愿的主体运动。这些技术实现了接近固有分辨率的PET分辨率 并实现了一系列新颖的PET/MR应用。在此续期申请中，我们将 开发和提供独特的PET/MR成像技术，不仅能够实现生理上的高 Impact新型PET/MR，但也将有利于独立的MR研究。我们专注于多方面的努力 参数QMR技术通过利用我们最近在子空间/流形建模方面的突破， MR-物理学支持深度学习(DL)和多对比超快MR序列。我们的建议 研究重点是沿着三个轴开发和提供新的PET/MR成像技术：i) 运动校正全心膜的多参数自由运行心脏MRI方法 使用PET/MR的潜在成像；ii)多模式、多参数、高分辨率MRSI 使用PET/MR的分子成像；以及iii)使用DEEP的超高速、多组分定量MR 学习和mr物理建模。我们将在TR&D3PET/MR轴上验证所提出的方法 并通过与我们的协作项目的合作来实现。我们将利用DL 在TR&D2中提出的进一步提高定量PET/MR图像质量的方法 Tr&d1的技巧。