Accelerating advanced MRI reconstructions on GPUs

在 GPU 上加速高级 MRI 重建

• 批准号: 7896994
• 负责人: Bradley P Sutton
• 金额: $ 18.31万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7896994
• 关键词: Adopted; Adoption; Algorithms; Back; Benchmarking; Charge; Clinic; Clinical; Code; Communities; Computer software; Consumption; Custom; Data; Data Set; Development; Diagnostic; Engineering; Environment; Family; Future; Goals; Human; Image; Institution; Internet; Investments; Laboratories; Lead; Libraries; Licensing; Longevity; Magnetic Resonance Imaging; Magnetism; Mainstreaming; Manuals; Medical; Medical Imaging; Methods; Metric; Microprocessor; Modeling; Noise; Performance; Personal Computers; Plant Roots; Predisposition; Process; Protocols documentation; Research; Research Infrastructure; Resolution; Sampling; Scanning; Scheme; Science; Scientist; Signal Transduction; Speed; Structure; System; Techniques; Technology; Time; Translations; base; clinical application; clinically relevant; computer cluster; computing resources; cost; data acquisition; flexibility; image reconstruction; improved; innovation; magnetic field; next generation; open source; programs; public health relevance; reconstruction; simulation; tool

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.

描述（由申请人提供）：该提案的主要假设是，临床磁共振成像（MRI）目前在空间分辨率，扫描时间和信噪比的权衡方面受到限制，缺乏可访问的计算资源来启用高级MRI采集和重建方法的临床应用。虽然在研究中使用了高级MRI采集和重建技术，但临床实用程序要求在图像获取的顺序（一或几分钟）中完成图像重建。该建议将开发，验证和基准一个灵活的软件包，以允许在已经广泛的，经济和计算高效的多核计算平台上执行先进的MRI重构，该计算机基于GPU的商品个人计算机和集群。具体而言，将使用一个简单的接口与C代码和MATLAB创建一个基于GPU的图像重建框架，该框架允许用户使用3D非现行轨迹对获得的数据进行重建；利用多个接收器线圈进行并行成像；补偿与长数据采集读数相关的磁场不均匀性；并将以前的解剖信息纳入图像重建中。这些技术将通过模拟，幻影和人类MRI获取（包括计算时间，归一化的均方根误差和噪声方差）来验证。该软件将使用自动优化程序包装，以在各种计算平台上进行快速执行，包括PC和簇中的多核CPU和多核GPU。该软件以及示例重建，示例数据，用户手册和编程文档将通过网络分发，并根据开源许可免费向教育用户分发。该项目结束时，学术和医疗机构的医疗物理学家将能够为其特定的MR收购定制软件，并轻松利用多核CPU和多核GPU计算能力。将拟议的计算实用程序集成到诊所中，将使当前的先进图像重建技术转换为诊所，并能够开发下一代MRI诊断技术。 公共卫生相关性：将开发一个高级图像重建软件库，该库将允许临床磁共振成像（MRI）利用由PC和基于GPU的组中的多核和多核计算实用程序提供的新兴计算功率。高级图像重建软件将允许诊所的医学物理学家轻松地将自定义成像协议集成到一般的MR重建框架中，并以10至100次的阶段收获计算加速。利用这种计算能力，临床成像将能够采用先进的MR采集策略，这将导致扫描次数较短，信噪比更高，并且空间分辨率更高，而不是传统的MRI获取。