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

描述（由申请人提供）：磁共振成像（MRI）是一种功能强大的成像工具，但许多重要的临床应用受到扫描时间长和/或SNR差的限制。该建议旨在通过贝叶斯推理方法在不损失SNR的情况下提高MRI的速度。扫描速度的提高可以使新的时间关键型临床和诊断MR应用，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，动态对比增强成像等贝叶斯框架重建原始MR数据从多个线圈并行将被开发。该框架使得可以通过降低原始MR数据的采样率来减少在多次扫描期间花费的时间。我们的方法将普遍适用于大多数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上进行验证和测试，体积结构脑成像是检测和诊断神经退行性疾病、肿瘤、白色病变、测量脑萎缩和海马子场等的重要程序。本项目的主要目标是创建一套计算工具，以在任意成像目标上执行加速MRI数据的重建，方式和获取计划，包括随机抽样计划。将设计模型，以捕捉有关图像的先验空间信息。最后，该方法将在结构脑数据上进行验证，如SNR、部分体积化、测试-重测重复性以及后续处理步骤（如图像分割）的性能等指标。公共卫生相关性：该项目有可能使临床MR成像比目前更快。这将使MRI的许多时间关键的临床应用更加可行，例如心脏的实时MRI。MRI成像更精细、临床上有趣的解剖特征的分辨率也将增加，使更可靠的诊断成为可能。

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.