title: Accelerating 4D flow MRI by exploiting low-rank matrix structure and hadamard sparsity.
abstract: To develop accelerated 4D flow MRI by exploiting low-rank matrix structure and Hadamard sparsity.
  
  
  
  4D flow MRI data can be represented as the sum of a low-rank and a sparse component. To optimize the sparse representation of the data, it is proposed to incorporate a Hadamard transform of the velocity-encoding segments. Retrospectively and prospectively, undersampled data of the aorta of healthy subjects are used to assess the reconstruction accuracy of the proposed method relative to k-t SPARSE-SENSE reconstruction. Image reconstruction from eight-fold prospective undersampling is demonstrated and compared with conventional SENSE imaging.
  
  
  
  Simulation results revealed consistently lower errors in velocity estimation when compared with k-t SPARSE-SENSE. In vivo data yielded reduced error of peak flow with the proposed method relative to k-t SPARSE-SENSE when compared with two-fold SENSE ( 2.5±4.6% versus 10.2±8.5% in the ascending aorta, 3.6±8.4% versus 9.2±9.0% in the descending aorta). Streamline visualization showed more consistent flow fields with the proposed technique relative to the benchmark methods.
  
  
  
  Image reconstruction by exploiting low-rank structure and Hadamard sparsity of 4D flow MRI data improves the reconstruction accuracy relative to current state-of-the-art methods and holds promise to reduce the long scan times of 4D flow MRI. Magn Reson Med 78:1330-1341, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors: Department of Information Engineering, University of Pisa, Pisa, Italy.|Fondazione G. Monasterio CNR-Regione Toscana, Massa, Italy.
authors: Giuseppe
authors: 0000-0001-5452-5708
authors: G
authors: Valvano
authors: Fondazione G. Monasterio CNR-Regione Toscana, Massa, Italy.
authors: Nicola
authors: N
authors: Martini
authors: Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
authors: Adrian
authors: A
authors: Huber
authors: Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
authors: Claudio
authors: C
authors: Santelli
authors: Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
authors: Christian
authors: C
authors: Binter
authors: Fondazione G. Monasterio CNR-Regione Toscana, Massa, Italy.
authors: Dante
authors: D
authors: Chiappino
authors: Department of Information Engineering, University of Pisa, Pisa, Italy.|Fondazione G. Monasterio CNR-Regione Toscana, Massa, Italy.
authors: Luigi
authors: L
authors: Landini
authors: Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
authors: Sebastian
authors: S
authors: Kozerke
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000000981:diagnostic imaging / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D012039:Regional Blood Flow
keywords: 4D flow MRI; Hadamard transform; complex differences; compressed sensing; low-rank and sparse decomposition