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 breathholding 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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Transition Award (R00)
Project #
5R00EB011527-05
Application #
8596817
Study Section
Special Emphasis Panel (NSS)
Program Officer
Liu, Guoying
Project Start
2010-03-01
Project End
2014-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
5
Fiscal Year
2014
Total Cost
$220,408
Indirect Cost
$75,349
Name
Case Western Reserve University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106
Chen, Yong; Jiang, Yun; Pahwa, Shivani et al. (2016) MR Fingerprinting for Rapid Quantitative Abdominal Imaging. Radiology 279:278-86
Chen, Yong; Lee, Gregory R; Aandal, Gunhild et al. (2016) Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA. Magn Reson Med 75:1457-65
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
Deshmane, Anagha; Blaimer, Martin; Breuer, Felix et al. (2016) Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding. Magn Reson Med 75:883-96
Hamilton, Jesse I; Jiang, Yun; Chen, Yong et al. (2016) MR fingerprinting for rapid quantification of myocardial T1 , T2 , and proton spin density. Magn Reson Med :
Jiang, Yun; Ma, Dan; Jerecic, Renate et al. (2016) MR fingerprinting using the quick echo splitting NMR imaging technique. Magn Reson Med :
Jiang, Yun; Ma, Dan; Seiberlich, Nicole et al. (2015) MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med 74:1621-31
Barkauskas, Deborah S; Dixon Dorand, R; Myers, Jay T et al. (2015) Focal transient CNS vessel leak provides a tissue niche for sequential immune cell accumulation during the asymptomatic phase of EAE induction. Exp Neurol 266:74-85
Chen, Yong; Lee, Gregory R; Wright, Katherine L et al. (2015) Free-breathing liver perfusion imaging using 3-dimensional through-time spiral generalized autocalibrating partially parallel acquisition acceleration. Invest Radiol 50:367-75
Wright, Katherine L; Lee, Gregory R; Ehses, Philipp et al. (2014) Three-dimensional through-time radial GRAPPA for renal MR angiography. J Magn Reson Imaging 40:864-74

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