Cover Image
QR Code

Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation.

Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality...

Full description

Published in:International Journal of Biomedical Imaging pp. 1 - 13
Main Authors: Bilal, Muhammad, Shah, Jawad Ali, Qureshi, Ijaz M., Kadir, Kushsairy
Format: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article
Published: Wiley-Blackwell 1/23/2018
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127516280&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 127516280
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        16874188
        1WZI
      jtl: International Journal of Biomedical Imaging
      issn: 16874188
      maglogo: N
    pubinfo:
      dt: 1/23/2018
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
    artinfo:
      ui:
        127516280
        127516280
        127516280
        10.1155/2018/7803067
        127516280
      ppf: 1
      ppct: 12
      formats:
      tig:
        atl: Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation.
      aug:
        au:
          Bilal, Muhammad
          Shah, Jawad Ali
          Qureshi, Ijaz M.
          Kadir, Kushsairy
        affil: Electrical Engineering Department, International Islamic University, Islamabad, Islamabad, Pakistan
      sug:
        subj:
          Magnetic Resonance Imaging
          Diagnosis, Cardiovascular Methods
          Respiration
          Motion
          Image Enhancement
          Algorithms
          Human
          Conceptual Framework
          In Vivo Studies
          Sensitivity and Specificity
      ab: Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.
      pubtype: Academic Journal
      doctype:
        algorithm
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N