Journal
JOURNAL OF MAGNETIC RESONANCE IMAGING
Volume 43, Issue 1, Pages 261-269Publisher
WILEY
DOI: 10.1002/jmri.24961
Keywords
compressed sensing; parallel imaging; temporal sparsity; radial undersampling; golden-angle; DCE-MRI; breast cancer; iGRASP
Funding
- National Institutes of Health [R01EB000447, R01CA160620, P41EB017183]
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BackgroundTo 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. MethodsDCE-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 (=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. ResultsiGRASP images reconstructed with =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 (2) to remove streaking aliasing artifacts (MI>23% between =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). ConclusionThis 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.
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