title: Signal compensation and compressed sensing for magnetization-prepared MR angiography.
abstract: Magnetization-prepared acquisitions offer a trade-off between image contrast and scan efficiency for magnetic resonance imaging. Because the prepared signals gradually decay, the contrast can be improved by frequently repeating the preparation, which in turn significantly increases the scan time. A common solution is to perform the data collection progressing from low- to high-spatial-frequency samples following each preparation. Unfortunately, this leads to loss of spatial resolution, and thereby image blurring. In this work, a new technique is proposed that first corrects the signal decay in high-frequency data to mitigate the resolution loss and improve the image contrast without reducing the scan efficiency. The proposed technique then employs a sparsity-based nonlinear reconstruction to further improve the image quality. In addition to reducing the amplified high-frequency noise, this reconstruction extrapolates missing k-space samples in the case of undersampled compressed-sensing acquisitions. The technique is successfully demonstrated for noncontrast-enhanced flow-independent angiography of the lower extremities, an application that substantially benefits from both the signal compensation and the nonlinear reconstruction.
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA. cukur@berkeley.edu
authors.forename: Tolga
authors.initials: T
authors.lastname: Çukur
authors.forename: Michael
authors.initials: M
authors.lastname: Lustig
authors.forename: Emine U
authors.initials: EU
authors.lastname: Saritas
authors.forename: Dwight G
authors.initials: DG
authors.lastname: Nishimura
mesh_terms: D000792:Angiography / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007866:Leg / Q000033:anatomy & histology / Q000098:blood supply; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted