Influence of temporal regularization and radial undersampling factor on compressed sensing reconstruction in dynamic contrast enhanced MRI of the breast

Sungheon G. Kim, Li Feng, Robert Grimm, Melanie Freed, Kai Tobias Block, Daniel K. Sodickson, Linda Moy, Ricardo Otazo

    Research output: Contribution to journalArticle

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)261-269
    Number of pages9
    JournalJournal of Magnetic Resonance Imaging
    Volume43
    Issue number1
    DOIs
    StatePublished - Jan 1 2016

    Keywords

    • DCE-MRI
    • breast cancer
    • compressed sensing
    • golden-angle
    • iGRASP
    • parallel imaging
    • radial undersampling
    • temporal sparsity

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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