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Influence of temporal regularization and radial undersampling factor on compressed sensing reconstruction in dynamic contrast enhanced MRI of the breast
Author(s) -
Kim Sungheon G.,
Feng Li,
Grimm Robert,
Freed Melanie,
Block Kai Tobias,
Sodickson Daniel K.,
Moy Linda,
Otazo Ricardo
Publication year - 2016
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24961
Subject(s) - undersampling , compressed sensing , temporal resolution , artificial intelligence , dynamic contrast enhanced mri , computer science , regularization (linguistics) , pattern recognition (psychology) , similarity (geometry) , iterative reconstruction , image resolution , dynamic contrast , contrast (vision) , mathematics , computer vision , nuclear medicine , magnetic resonance imaging , medicine , radiology , physics , image (mathematics) , quantum mechanics
Background To evaluate the influence of temporal sparsity regularization and radial undersampling on compressed sensing reconstruction of dynamic contrast‐enhanced (DCE) MRI, using the iterative Golden‐angle RAdial Sparse Parallel (iGRASP) MRI technique in the setting of breast cancer evaluation. Methods DCE‐MRI examinations of the breast (n = 7) were conducted using iGRASP at 3 Tesla. Images were reconstructed with five different radial undersampling schemes corresponding to temporal resolutions between 2 and 13.4 s/frame and with four different weights for temporal sparsity regularization (λ = 0.1, 0.5, 2, and 6 times of noise level). Image similarity to time‐averaged reference images was assessed by two breast radiologists and using quantitative metrics. Temporal similarity was measured in terms of wash‐in slope and contrast kinetic model parameters. Results iGRASP images reconstructed with λ = 2 and 5.1 s/frame had significantly ( P < 0.05) higher similarity to time‐averaged reference images than the images with other reconstruction parameters (mutual information (MI) >5%), in agreement with the assessment of two breast radiologists. Higher undersampling (temporal resolution < 5.1 s/frame) required stronger temporal sparsity regularization (λ ≥ 2) to remove streaking aliasing artifacts (MI > 23% between λ = 2 and 0.5). The difference between the kinetic‐model transfer rates of benign and malignant groups decreased as temporal resolution decreased (82% between 2 and 13.4 s/frame). Conclusion This study demonstrates objective spatial and temporal similarity measures can be used to assess the influence of sparsity constraint and undersampling in compressed sensing DCE‐MRI and also shows that the iGRASP method provides the flexibility of optimizing these reconstruction parameters in the postprocessing stage using the same acquired data. J. MAGN. RESON. IMAGING 2016;43:261–269.