Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification
نویسندگان
چکیده
منابع مشابه
Magnetic resonance fingerprinting (MRF) for rapid quantitative abdominal imaging
Target Audience This work targets those interested in fast quantitative imaging and abdominal MRI. Purpose Quantitative parameter measurement in the abdomen is extremely challenging due to the anatomy (large organs), field inhomogeneities and extreme physiological motion. Recently, we have introduced a revolutionary paradigm for MRI acquisition, reconstruction, and analysis of MR data, termed M...
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Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spinrelaxation parameters from magnetic-resonance data. Most current MRF approaches assume that only one tissue is present in each voxel, which neglects the tissue’s microstructure, and may lead to artifacts in the recovered parameter maps at boundaries between tissues. In this work, we propose a multicompartm...
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Understanding the need Quantitative vs Qualitative MRI data: The vast majority of common clinical MRI protocols rely on qualitative images reflecting the weighted effect of different tissue parameters. These contrast parameters include relaxation times, principally T1, T2 and T2*, as well as structural or functional quantities such as diffusion and blood flow. The absolute level of the signal v...
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Magnetic resonance imaging (MRI) is a powerful non-invasive medical imaging technique that encodes the mechanical, physiological and chemical structure of soft tissues. However, manual segmentation of tissue regions of interest (ROIs) can be a laborious process prone to operator error. In this project, we compared algorithms from 3 classes of supervised machine learning (ML) techniques for MRI ...
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We propose a merit function for the expected contrast to noise ratio in tissue quantifications, and formulate a nonlinear, nonconvex semidefinite optimization problem to select locally-optimal balanced steady-state free precession (bSSFP) pulse-sequence design variables. The method could be applied to other pulse sequence types, arbitrary numbers of tissues, and numbers of images. To solve the ...
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ژورنال
عنوان ژورنال: Proceedings of the IEEE
سال: 2020
ISSN: 0018-9219,1558-2256
DOI: 10.1109/jproc.2019.2936998