University of Michigan

Yun Jiang

Nicole Seiberlich

Jesse Hamilton

Jon-Fredrik Nielsen

Matthew Zhu

Vikas Gulani

Yuchi Liu

Methods and systems perform magnetic resonance fingerprinting (MRF) that provides tissue characterization through simultaneous quantification of water tissue properties and proton density fat fraction (PDFF), by using water-only and fat-only images from MRF. MRF is performed using rosette trajectories scanning k-space to effectively isolate water tissue and fat tissue, by separating these rosette trajectories into individual segments that are then analyzed to enable signals from fat tissue to be distinguished from water.

Magnetic resonance fingerprinting using rosette trajectories for fat fraction mapping

Methods and systems perform magnetic resonance fingerprinting (MRF) by obtaining magnetic resonance data over a main field-of-view (FOV) and resulting from providing a magnetic resonance fingerprinting pulse sequence to a sample. The pulse sequence includes gradient waveforms and radio frequency (RF) pulses that have pulse sequence parameters specifically tailored for scanning, not the entire main FOV but rather a reduced portion of that main FOV. The methods and systems further include comparing the magnetic resonance data from the sample to a fingerprint dictionary of signal profiles that specifically correspond to the reduced portion of the main FOV and generating tissue property maps that correspond only to that reduced portion.

Quantitative reduced field-of-view imaging system using 3D tailored inner volume excitation and pattern recognition

A method for denoising magnetic resonance images and data is disclosed herein. An example method includes receiving a series of MRF images from a scanning device; identifying one or more subsets of voxels for the series of MRF images; generating one or more sets of eigenvectors, each set of the one or more sets of eigenvectors corresponding to one of the one or more subsets of voxels, and each eigenvector of the one or more sets of eigenvectors having a corresponding eigenvalue; applying a noise distribution model to each of the eigenvalues; identifying a subset of the eigenvalues as corresponding to noise based on the noise distribution model; and reconstructing the series of MRF images without the subset of eigenvalues identified as corresponding to noise.

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Ann Arbor, MI, United States of America

Yun Jiang