GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation Journal Article


Authors: Feng, L.; Wen, Q.; Huang, C.; Tong, A.; Liu, F.; Chandarana, H.
Article Title: GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation
Abstract: Purpose: To propose a highly accelerated, high-resolution dynamic contrast-enhanced MRI (DCE-MRI) technique called GRASP-Pro (golden-angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self-calibrating subspace constraint with automated selection of contrast phases. Methods: GRASP-Pro reconstruction enforces a combination of an explicit low-rank subspace-constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low-resolution portion of radial k-space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP-Pro was demonstrated for high spatiotemporal resolution DCE-MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level. Results: Compared to GRASP, GRASP-Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection. Conclusion: GRASP-Pro is a promising method for rapid and continuous DCE-MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow. © 2019 International Society for Magnetic Resonance in Medicine
Keywords: image analysis; automation; contrast enhancement; image quality; artifact; image processing; dynamic contrast-enhanced magnetic resonance imaging; image reconstruction; image segmentation; compressed sensing; radiological procedures; human; male; article; prostate tissue; convolutional neural network; gadobutrol; golden-angle radial; grasp-pro; rapid dce-mri; subspace modeling
Journal Title: Magnetic Resonance in Medicine
Volume: 83
Issue: 1
ISSN: 0740-3194
Publisher: John Wiley & Sons  
Date Published: 2020-01-01
Start Page: 94
End Page: 108
Language: English
DOI: 10.1002/mrm.27903
PUBMED: 31400028
PROVIDER: scopus
PMCID: PMC6778712
DOI/URL:
Notes: Article -- Export Date: 1 November 2019 -- Source: Scopus
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  1. Li Feng
    9 Feng