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.
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.
