Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

自由呼吸的新颖计算框架

• 批准号: 9217649
• 负责人: Mathews Jacob
• 金额: $ 48.92万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9217649
• 关键词: 3-Dimensional; Abdomen; Address; Adult; Adverse effects; Algorithms; Breathing; Cardiac; Cardiopulmonary; Cardiovascular Diseases; Cardiovascular system; Child; Clinical Protocols; Comorbidity; Custom; Data; Data Set; Dependency; Development; Dimensions; Epidemic; Evaluation; Event; Fibrosis; Financial compensation; Formulation; Gadolinium; Goals; Image; Imagery; Joints; Learning; Lung; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modeling; Motion; Myocardial perfusion; Obesity; Organ; Patients; Performance; Perfusion; Phase; Physics; Physiologic pulse; Physiological; Population; Prevalence; Protocols documentation; Public Health; Radiology Specialty; Recovery; Research; Resolution; Risk; Risk stratification; Sampling; Scanning; Scheme; Signal Transduction; Slice; Stress; Structure; Three-Dimensional Image; Three-Dimensional Imaging; Time; Variant; cardiovascular risk factor; cardiovascular visualization; computer framework; cost; flexibility; heart imaging; heart motion; high dimensionality; image reconstruction; imaging modality; improved; innovation; liver imaging; lung imaging; novel; perfusion imaging; public health relevance; reconstruction; respiratory; signal processing

 DESCRIPTION (provided by applicant): Obesity has reached global epidemic proportions in both adults and children. Obesity has a major impact on cardiovascular (CV) disorders because of its adverse effects on cardiac function, structure, and various CV risk factors. MR imaging has great potential in estimating these changes and stratifying obese subjects for risk of major advanced cardiac events. However, the physiological changes resulting from obesity and associated pulmonary comorbidities often make it difficult for many obese subject to comply with current clinical protocols that require several breath holds and long scan time. Short free breathing protocols are urgently needed for the cardiac evaluation of obese subjects. The main goal of this proposal is to develop a short 3-D free-breathing & un-gated cardiac imaging protocol to evaluate cardiac structure, function, perfusion, and fibrosis in obese subjects in around twenty minutes of scan time. This protocol is enabled by synergistic developments in novel ungated sequences and a novel manifold regularization framework. The reconstruction framework, which exploits the manifold structure of images and patches in the dataset, is ideally suited to harness the flexibility and high acquisition efficiency of ungated 3-D sequences. The main hypothesis is that the implicit motion compensated and motion resolved reconstruction scheme will provide good recovery of the datasets in the protocol from highly under sampled data. We will quantitatively determine the utility of the free-breathing & ungated framework to provide reconstructions that are equivalent to current breath-hold acquisitions. This framework is expected to significantly improve the compliance of obese subjects. In addition, this approach also provides co-registered 3-D volumes with different contrasts, which will greatly improve quantification, visualization, and radiologic interpretation. The manifold learning framework is powerful and highly innovative; it can be readily applied to a variety of dynamic applications beyond cardiac imaging (vocal tract imaging, liver imaging, lung imaging). Our team is well qualified to perform the proposed research because of our combined scope and breadth in expertise (including signal processing, MR physics, and radiology), in addition to the extensive preliminary data.

 描述（由适用提供）：肥胖症已达到成人和儿童的全球流行比例。肥胖对心血管疾病（CV）疾病产生了重大影响，因为它对心脏功能，结构和各种简历危险因素的不利影响。 MR成像在估计这些变化并将肥胖受试者分类为重大高级心脏事件的风险方面具有很大的潜力。但是，肥胖和相关肺合并症导致的身体变化通常使许多肥胖受试者难以遵守当前需要几次呼吸和长时间扫描时间的临床方案。迫切需要短暂的自由呼吸方案来对肥胖受试者进行心脏评估。该提案的主要目的是开发短暂的3D游离呼吸和未门控的心脏成像方案，以评估肥胖受试者的心脏结构，功能，灌注和纤维化在扫描时间的二十分钟内。该协议是由新型不调节序列和一种新型的多种调节框架中的协同发展实现的。利用数据集中图像和斑块的多种结构的重建框架非常适合利用未测定的3-D序列的灵活性和高采集效率。主要的假设是，隐式运动补偿和运动解决的重建方案将从高度下的数据下从高度下的数据中恢复该协议中数据集的良好。我们将定量确定自由呼吸和未经框架的效用，以提供相当于当前呼吸供电的重建。预计该框架将显着提高肥胖受试者的依从性。此外，这种方法还提供了具有不同对比度的共同注册的3-D体积，这将大大改善定量，可视化和放射学解释。多种学习框架是强大的，高度创新的。它可以很容易地应用于心脏成像（声道成像，肝脏成像，肺成像）之外的各种动态应用。除了广泛的初步数据之外，我们的团队有资格进行拟议的研究（包括信号处理，MR物理学和放射学）的范围（包括信号处理，MR物理学和放射学）。