title: Low-rank and sparse matrix decomposition for compressed sensing reconstruction of magnetic resonance 4D phase contrast blood flow imaging (loSDeCoS 4D-PCI).
abstract: Blood flow measurements using 4D Phase Contrast blood flow imaging (PCI) provide an excellent fully non-invasive technique to assess the hemodynamics clinically in-vivo. Iterative reconstruction techniques combined with parallel MRI have been proposed to reduce the data acquisition time, which is the biggest drawback of 4D PCI. The novel LoSDeCoS technique combines these ideas with the separation into a low-rank and a sparse component. The high-dimensionality of the PC data renders it ideally suited for this approach. The proposed method is not limited to a single body region, but can be applied to any 4D flow measurement. The benefits of the new method are twofold: It allows to significantly accelerate the acquisition; and generates additional images highlighting temporal and directional flow changes. Reduction in acquisition time improves patient comfort and can be used to achieve better temporal or spatial resolution, which in turn allows more precise calculations of clinically important quantitative numbers such as flow rates or the wall shear stress. With LoSDeCoS, acceleration factors of 6-8 were achieved for 16 in-vivo datasets of both the carotid artery (6 datasets) and the aorta (10 datasets), while decreasing the Normalized Root Mean Square Error by over 10 % compared to a standard iterative reconstruction and by achieving similarity values of over 0.93. Inflow-Outflow phantom experiments showed good parabolic profiles and an excellent mass conservation.
authors: Pattern Recognition Lab, University Erlangen-Nuremberg, Germany.
authors: Jana
authors: J
authors: Hutter
authors: Siemens AG Healthcare Sector, Magnetic Resonance, Erlangen, Germany.
authors: Peter
authors: P
authors: Schmitt
authors: University Hospital Cleveland, Cleveland, Ohio, USA.
authors: Gunhild
authors: G
authors: Aandal
authors: Siemens AG Healthcare Sector, Magnetic Resonance, Erlangen, Germany.
authors: Andreas
authors: A
authors: Greiser
authors: Pattern Recognition Lab, University Erlangen-Nuremberg, Germany.
authors: Christoph
authors: C
authors: Forman
authors: Pattern Recognition Lab, University Erlangen-Nuremberg, Germany.
authors: Robert
authors: R
authors: Grimm
authors: Pattern Recognition Lab, University Erlangen-Nuremberg, Germany.
authors: Joachim
authors: J
authors: Horneggert
authors: Pattern Recognition Lab, University Erlangen-Nuremberg, Germany.
authors: Andreas
authors: A
authors: Maier
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000473:pathology / Q000503:physiopathology*; D001783:Blood Flow Velocity; D002339:Carotid Arteries / Q000473:pathology / Q000503:physiopathology*; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity