Faster MRI with Sparse Sampling and Low-Rank Modeling Current clinical and research MRI applications are frequently limited by the slow speed and high cost of MRI data acquisition. Faster MRI technology would enable clinicians and researchers to acquire image datasets that are substantially higher quality and more comprehensive than is currently possible with modern imaging protocols. The guiding hypothesis of this project is that substantial improvements in MRI speed can be achieved by combining sparse sampling methods with novel structured low-rank subspace modeling approaches. We have recently developed a novel low-rank subspace modeling framework (low-rank modeling of local k-space neighborhoods, or ?LORAKS?), which has the unique feature that it can be used to model multiple standard MRI constraints (sparse image support, smooth phase, and parallel imaging) in a calibration-free way. We have also demonstrated that LORAKS can enable high-quality MRI images to be reconstructed from much less data than required by the conventional sampling theorem. In contrast to many of the alternative reconstruction methods for undersampled data, LORAKS offers high acceleration rates and unprecedented compatibility with a wide variety of MRI data sampling schemes. The objective of this project is the further investigation and development of LORAKS for practical MRI neuroimaging applications.
Aim 1 involves the practical implementation and optimization of LORAKS for high-resolution anatomical imaging, and includes pulse sequence development, sampling-scheme optimization, development of publicly-distributed software, and an empirical evaluation of the impact of accelerated MRI on practical human brain morphometry studies.
Aim 2 involves the extension of the LORAKS framework to advanced fast imaging protocols that utilize simultaneous multi-slice data acquisition and echo-planar imaging strategies.
This aim i ncludes further mathematical generalization of the LORAKS framework, pulse sequence development, exploration and optimization of novel sampling schemes, development of publicly-distributed software, and evaluations in the context of quantitative diffusion MRI. The methods developed through this work are expected to substantially enhance the performance of accelerated MRI, and are general enough that they can be used to enhance virtually any clinical MRI application. This work will also lay important groundwork for future studies of accelerated MRI.
Faster MRI with Sparse Sampling and Low-Rank Modeling Magnetic resonance imaging (MRI) is an important non-invasive imaging modality that can provide valuable insights into patient-specific disease processes, but is limited in clinical scenarios by the slow speed and high cost of MRI data acquisition. This project will develop novel fast MRI technology that would enable clinicians and researchers to acquire image datasets that are substantially higher quality and more comprehensive than is currently possible with modern imaging protocols.
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