title: User-guided compressed sensing for magnetic resonance angiography.
abstract: Compressed sensing (CS) magnetic resonance imaging (MRI) enables the reconstruction of MRI images with fewer samples in k-space. One requirement is that the acquired image has a sparse representation in a known transform domain. MR angiograms are already sparse in the image domain. They can be further sparsified through finite-differences. Therefore, it is a natural application for CS-MRI. However, low-contrast vessels are likely to disappear at high undersampling ratios, since the commonly used £(1) reconstruction tends to underestimate the magnitude of the transformed sparse coefficients. These vessels, however, are likely to be clinically important for medical diagnosis. To avoid the fading of low-contrast vessels, we propose a user-guided CS MRI that is able to mitigate the reduction of vessel contrast within a region of interest (ROI). Simulations show that these low-contrast vessels can be well maintained via our method which results in higher local quality compared to conventional CS.
authors.lastname: Changgong Zhang
authors.forename: Martijn
authors.initials: M
authors.lastname: van de Giessen
authors.forename: Elmar
authors.initials: E
authors.lastname: Eisemann
authors.forename: Anna
authors.initials: A
authors.lastname: Vilanova
mesh_terms: D000465:Algorithms; D003287:Contrast Media; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods*; D013997:Time Factors
chemical_list: D003287:Contrast Media