Cover Image
QR Code

Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint.

We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations...

Full description

Published in:Journal of Medical Systems Vol. 43; no. 8
Main Authors: Arif, Omar, Afzal, Hammad, Abbas, Haider, Amjad, Muhammad Faisal, Wan, Jiafu, Nawaz, Raheel
Format: diagnostic images equations & formulas tables/charts Journal Article
Published: Springer Nature Aug2019
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137490058&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 137490058
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01485598
        4N0
      jtl: Journal of Medical Systems
      issn: 01485598
      maglogo: N
    pubinfo:
      dt: Aug2019
      vid: 43
      iid: 8
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        137490058
        137490058
        137490058
        10.1007/s10916-019-1399-x
        137490058
      ppct: 1
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint.
      aug:
        au:
          Arif, Omar
          Afzal, Hammad
          Abbas, Haider
          Amjad, Muhammad Faisal
          Wan, Jiafu
          Nawaz, Raheel
        affil: National University of Sciences and Technology (NUST), Islamabad, Pakistan
      sug:
        subj:
          Magnetic Resonance Imaging Methods
          Algorithms Methods
          Image Processing, Computer Assisted Methods
          Human
          Linear Regression
          In Vivo Studies
          Perfusion Imaging
      ab: We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N