|M.Sc Student||Mazor Gal|
|Subject||Sub-Nyquist Quantitative Estimation of Magnetic Resonance|
|Department||Department of Electrical Engineering||Supervisor||Professor Yonina Eldar|
|Full Thesis text|
Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition.
Extraction of physical quantitative tissue parameters is performed off-line, without the need for patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations.
MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore a high undersampling ratio in the sampling domain (k-space) is required for fast scanning time. This undersampling causes spatial artifacts that hamper the ability to accurately estimate the tissue's quantitative values. In this work, we introduce a new approach for quantitative MRI using MRF, called magnetic resonance Fingerprinting with LOw Rank (FLOR).
We exploit the low rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition to retrieve more accurate temporal contrasts and eventually more accurate magnetic parameter maps. Our approach is validated by experimental results that consist of retrospective sampling, allowing comparison to a well defined reference, and prospective sampling that shows the performance of FLOR in a real-data sampling scenario.
Both experiments demonstrate improved parameter accuracy compared to other compressed-sensing and low-rank based methods for MRF at 5\% and 9\% sampling ratios, for the retrospective and prospective experiments, respectively.
Next, using the retrospective experiments, we consider additional tensor tools that exploit the low rankness property of the data.
In particular, we extend FLOR for tensors and show that this leads to improved reconstruction of the magnetic parameter maps compared to flattening the data.
As a complimentary tool for the quantitative maps, we build a MRI simulator that translates those maps into common clinical MRI images. We use the main protocols received from the neuro department of RAMBAM hospital for this task. Our tool simulates MR images interactively with user specific requirements. This ability emphasizes a prominent advantage of acquiring the magnetic parameter maps: providing many different images required by the radiologist with only one scan.