The goal of this research project is to develop new, ultra-fast methods for dynamic imaging applications to enable greater clinical utility in the future. We intend to meet this goal by combining several existing image reconstruction methods, namely parallel imaging and non-Cartesian trajectories, to generate novel fast acquisition methods. Our current research involves the use of radial trajectories, as opposed to the standard, rectilinear trajectory, to acquire highly accelerated datasets in a very short time. These data can then be reconstructed using a special formulation of a parallel imaging method known as GRAPPA in order to reconstruct error-free images. Using this technique, we have acquired images with a temporal resolution of 60ms. We plan to expand this concept to trajectories which have the potential for even fast data acquisition, namely spiral and anisotropic field-of-view trajectories. Using these methods, we believe that it will be possible to generate images in less than 40ms, which will allow the acquisition real-time, free-breathing cardiac images, making EKG gating and breath holding unnecessary for cardiac function exams. In order to make these reconstructions possible in a clinically acceptable timeframe, they will be implemented on a GPU platform, which will reduce the reconstruction time from minutes to seconds. In the independent phase of the project, the GPU platform will be exploited in order to investigate different constrained reconstruction methods for MRI data. In addition to parallel imaging and non-Cartesian acquisitions, these techniques which include compressed sensing have also emerged as a new and important category of possible fast imaging methods. Early work has demonstrated an up to 20-fold reduction in data, and thus time, needed for an image. The power of these methods is obvious, although it is not yet clear if they will be viable in a clinical setting, due to, for instance, incredibly long computation times (sometimes up to days). Thus based on our experience in the first stage of this proposal, the independent portion of this project will explore the potential of these constrained reconstruction methods and examines the possibility of combining them with the non- Cartesian parallel imaging methods developed in the earlier phase. The rapid computational platform, in the form of the GPU implementations, will allow these novel image reconstruction techniques to be vigorously tested, paving the way for these methods to become practical for widespread clinical use.
While magnetic resonance imaging (MRI) is in widespread clinical use because of its sensitivity to a broad range of diseases, the relatively slow acquisition of MRI data limits its applicability to many dynamic imaging situations such as cardiac imaging or MR angiography. The goal of this project is to develop image reconstruction techniques for ultra-fast MRI imaging using a combination of novel acquisition and signal processing methods. Rapid computing using GPU implementations of these techniques will allow the reconstructions to take place in a matter of seconds, allowing this technology to be implemented in a clinical setting. These methods will revolutionize the acquisition and reconstruction of dynamic MRI data.
|Wech, Tobias; Seiberlich, Nicole; Schindele, Andreas et al. (2016) Development of Real-Time Magnetic Resonance Imaging of Mouse Hearts at 9.4 Tesla--Simulations and First Application. IEEE Trans Med Imaging 35:912-20|
|Wright, Katherine L; Seiberlich, Nicole; Jesberger, John A et al. (2013) Simultaneous magnetic resonance angiography and perfusion (MRAP) measurement: initial application in lower extremity skeletal muscle. J Magn Reson Imaging 38:1237-44|
|Seiberlich, Nicole; Lee, Gregory; Ehses, Philipp et al. (2011) Improved temporal resolution in cardiac imaging using through-time spiral GRAPPA. Magn Reson Med 66:1682-8|
|Seiberlich, Nicole; Ehses, Philipp; Duerk, Jeff et al. (2011) Improved radial GRAPPA calibration for real-time free-breathing cardiac imaging. Magn Reson Med 65:492-505|
|Saybasili, Haris; Derbyshire, J Andrew; Kellman, Peter et al. (2010) RT-GROG: parallelized self-calibrating GROG for real-time MRI. Magn Reson Med 64:306-12|