Bayesian Parallel Imaging For Arbitrarily Sampled MR Data Using Edge-Preserving S

使用边缘保留 S 的任意采样 MR 数据的贝叶斯并行成像

• 批准号: 7528771
• 负责人: Ashish Raj
• 金额: $ 25.24万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7528771
• 关键词: Acceleration; Address; Algorithms; Anatomy; Blur; Brain; Brain Diseases; Brain imaging; Breathing; Cardiac; Class; Clinical; Complex; Computational Technique; Computational algorithm; Computer Vision Systems; Condition; Coronary Angiography; Data; Data Quality; Detection; Diagnosis; Diagnostic; Dimensions; Drug Formulations; Future; Generic Drugs; Goals; Graph; Heart; Hippocampus (Brain); Image; Imaging Device; Imaging Techniques; Knowledge; Lead; Lesion; Magnetic Resonance Imaging; Measures; Methods; Metric; Modality; Modification; Morphologic artifacts; Motion; Neurodegenerative Disorders; Noise; Numbers; Patients; Pattern; Performance; Phase; Physiologic pulse; Problem Solving; Procedures; Process; Public Health; Pulse taking; Purpose; Range; Rate; Resolution; Sampling; Scanning; Scheme; Signal Transduction; Specific qualifier value; Speed; Structure; System; Techniques; Testing; Time; Tissues; Validation; Weight; Work; base; cerebral atrophy; clinical application; combinatorial; computerized data processing; computerized tools; design; expectation; heart motion; image reconstruction; imaging Segmentation; improved; in vivo; interest; model design; nervous system disorder; reconstruction; simulation; tumor; white matter

DESCRIPTION (provided by applicant): Magnetic Resonance imaging (MRI) is a powerful imaging tool but many important clinical applications are limited by long scan times and/or poor SNR. This proposal aims to improve the speed of MRI without losing SNR, through a Bayesian inference approach. Improvement in scan speed can enable new time-critical clinical and diagnostic MR applications, like cardiac imaging, time-resolved 4D coronary angiography, high-resolution volumetric brain imaging, dynamic contrast enhanced imaging, etc. A Bayesian framework for the reconstruction of raw MR data from multiple coils in parallel will be developed. This framework makes it possible to reduce the time taken during scanning multiple times by reducing the sampling rate of raw MR data. Our method will be generally applicable to most MR imaging modalities, targets and sampling schemes. Our method will then be validated and tested on the specific clinical application of volumetric structural brain imaging, which is an important procedure for the detection and diagnosis of neurodegenerative diseases, tumors, white matter lesions, measuring brain atrophy and hippocampal subfields, etc. The main goal of this project is to create a set of computational tools to perform the reconstruction of accelerated MRI data on arbitrary imaging targets, modalities and acquisition schemes, including random sampling schemes. Design of models to capture prior spatial information about images will be undertaken. Finally, the method will be validated on structural brain data in terms of metrics like SNR, partial voluming, test- retest repeatability, and the performance of subsequent processing steps like image segmentation. PUBLIC HEALTH RELEVANCE: This project has the potential to make clinical MR imaging much faster than currently possible. This will make many time-critical clinical applications of MRI more feasible, for instance real-time MRI of the heart. The resolving power of MRI to image finer, clinically interesting anatomical features will also increase, making more reliable diagnosis possible.

描述（由申请人提供）：磁共振成像（MRI）是一种强大的成像工具，但许多重要的临床应用受到扫描时间长和/或信噪比差的限制。该提案旨在通过贝叶斯推理方法在不损失信噪比的情况下提高 MRI 的速度。扫描速度的提高可以实现新的时间关键的临床和诊断 MR 应用，例如心脏成像、时间分辨 4D 冠状动脉造影、高分辨率体积脑成像、动态对比度增强成像等。将开发用于并行重建来自多个线圈的原始 MR 数据的贝叶斯框架。该框架可以通过降低原始 MR 数据的采样率来减少多次扫描所需的时间。我们的方法通常适用于大多数 MR 成像模式、目标和采样方案。然后，我们的方法将在体积结构脑成像的具体临床应用中进行验证和测试，体积结构脑成像是检测和诊断神经退行性疾病、肿瘤、白质病变、测量脑萎缩和海马亚区等的重要程序。该项目的主要目标是创建一套计算工具来对任意成像目标、模式进行加速 MRI 数据的重建 和采集方案，包括随机抽样方案。将设计模型来捕获有关图像的先前空间信息。最后，该方法将在结构性大脑数据上根据信噪比、部分体积、重测重复性以及图像分割等后续处理步骤的性能等指标进行验证。公共健康相关性：该项目有可能使临床 MR 成像比目前更快。这将使 MRI 的许多时间紧迫的临床应用变得更加可行，例如心脏的实时 MRI。 MRI 对更精细、临床上有趣的解剖特征进行成像的分辨率也将提高，从而使更可靠的诊断成为可能。