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Comparison of iterative parametric and indirect deep learning‐based reconstruction methods in highly undersampled DCE‐MR Imaging of the breast
Author(s) -
Rastogi Aditya,
Yalavarthy Phaneendra K.
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14447
Subject(s) - iterative reconstruction , computer science , parametric statistics , solver , undersampling , artificial intelligence , compressed sensing , ground truth , deep learning , medical imaging , algorithm , pattern recognition (psychology) , mathematics , statistics , programming language
To compare the performance of iterative direct and indirect parametric reconstruction methods with indirect deep learning-based reconstruction methods in estimating tracer-kinetic parameters from highly undersampled DCE-MR Imaging breast data and provide a systematic comparison of the same.