Accelerating advanced MRI reconstructions on GPUs

描述（由申请人提供）：本提案的主要假设是，由于缺乏可访问的计算资源，临床磁共振成像（MRI）目前在空间分辨率，扫描时间和信噪比方面的权衡受到限制，无法实现高级MRI采集和重建方法的临床应用。虽然研究中使用了先进的MRI采集和重建技术，但临床应用要求在图像采集的顺序（一分钟或几分钟）内完成图像重建。该提案将开发、验证和测试一个灵活的软件包，以允许高级MRI重建在已经广泛、经济、计算效率高的多核计算平台上执行，这些平台由基于gpu的商品个人计算机和集群提供。具体来说，基于gpu的图像重建框架将创建一个简单的接口到c代码和Matlab，允许用户执行与三维非笛卡尔轨迹采集的数据重建；利用多个接收线圈进行并行成像；补偿与长时间数据采集读数相关的磁场不均匀性；并将先前的解剖信息整合到图像重建中。该技术将通过模拟、模拟和人体MRI采集进行验证，测量指标包括计算时间、标准化均方根误差和噪声方差。该软件将与自动优化例程打包，以便在各种计算平台上快速执行，包括pc和集群中的多核cpu和多核gpu。该软件连同示例重建、示例数据、用户手册和编程文档将通过网络分发，根据开源许可免费提供给教育用户。在项目结束时，学术和医疗机构的医学物理学家将能够为他们特定的MR采集定制软件，并轻松利用多核CPU和多核GPU计算能力。将提出的计算实用程序集成到临床将使当前先进的图像重建技术转化为临床，并使下一代MRI诊断技术的发展成为可能。

DESCRIPTION (provided by applicant): The main hypothesis of this proposal is that clinical magnetic resonance imaging (MRI) is currently limited in its tradeoffs of spatial resolution, scan time, and signal-to-noise by a lack of accessible computational resources to enable clinical application of advanced MRI acquisition and reconstruction methods. While advanced MRI acquisition and reconstruction techniques are used in research, clinical utility requires that image reconstructions be completed in times that are on the order of the image acquisition (one or a few minutes). This proposal will develop, validate, and benchmark a flexible software package to allow for advanced MRI reconstructions to be executed on the already widespread, economical, and computationally-efficient many-core computing platforms offered by GPU-based commodity personal computers and clusters. Specifically, a GPU-based image reconstruction framework will be created with an easy interface to C-code and Matlab that allows users to perform reconstruction of data acquired with 3D non-Cartesian trajectories; utilizing multiple receiver coils for parallel imaging; compensating for magnetic field inhomogeneities associated with long data acquisition readouts; and incorporating prior anatomical information into the image reconstruction. The techniques will be validated through simulation, phantom, and human MRI acquisitions with metrics including computation time, normalized root mean square error, and noise variance. The software will be packaged with automatic optimization routines to enable fast execution on a variety of computational platforms, including both multi-core CPUs and many-core GPUs in PCs and clusters. The software, along with example reconstructions, sample data, user manuals, and programming documents will be distributed through the web, free of charge to educational users in accordance with the open source license. At the conclusion of the project, medical physicists at academic and medical institutions will be able to customize the software for their specific MR acquisitions and easily harness multi-core CPU and many-core GPU computational power. Integration of the proposed computational utility into the clinic will enable translation of current advanced image reconstruction techniques to the clinic and enable development of the next generation of MRI diagnostic technology. PUBLIC HEALTH RELEVANCE: An advanced image reconstruction software library will be developed that allows clinical magnetic resonance imaging (MRI) to harness the emerging computational power provided by multi-core and many-core computational utilities in PCs and GPU-based clusters. The advanced image reconstruction software will allow medical physicists in the clinic to easily integrate custom imaging protocols into the general MR reconstruction framework and reap computational speed-ups on the order of 10 to 100 times. Leveraging this computational power, clinical imaging will be able to adopt advanced MR acquisition strategies that will lead to shorter scan sessions, higher signal-to-noise ratios, and higher spatial resolution than is possible with traditional MRI acquisitions.