Literature DB >> 26777856

Diagnostic quality assessment of compressed sensing accelerated magnetic resonance neuroimaging.

Mohammad Kayvanrad1,2, Amy Lin3, Rohit Joshi3,4, Jack Chiu3, Terry Peters1,2,3.   

Abstract

PURPOSE: To determine the efficacy of compressed sensing (CS) reconstructions for specific clinical magnetic resonance neuroimaging applications beyond more conventional acceleration techniques such as parallel imaging (PI) and low-resolution acquisitions.
MATERIALS AND METHODS: Raw k-space data were acquired from five healthy volunteers on a 3T scanner using a 32-channel head coil using T2 -FLAIR, FIESTA-C, time of flight (TOF), and spoiled gradient echo (SPGR) sequences. In a series of blinded studies, three radiologists independently evaluated CS, PI (GRAPPA), and low-resolution images at up to 5× accelerations. Synthetic T2 -FLAIR images with artificial lesions were used to assess diagnostic accuracy for CS reconstructions.
RESULTS: CS reconstructions were of diagnostically acceptable quality at up to 4× acceleration for T2 -FLAIR and FIESTA-C (average qualitative scores 3.7 and 4.3, respectively, on a 5-point scale at 4× acceleration), and at up to 3× acceleration for TOF and SPGR (average scores 4.0 and 3.7, respectively, at 3× acceleration). The qualitative scores for CS reconstructions were significantly better than low-resolution images for T2 -FLAIR, FIESTA-C, and TOF and significantly better than GRAPPA for TOF and SPGR (Wilcoxon signed rank test, P < 0.05) with no significant difference found otherwise. Diagnostic accuracy was acceptable for both CS and low-resolution images at up to 3× acceleration (area under the ROC curve 0.97 and 0.96, respectively.)
CONCLUSION: Mild to moderate accelerations are possible for those sequences by a combined CS and PI reconstruction. Nevertheless, for certain sequences/applications one might mildly reduce the acquisition time by appropriately reducing the imaging resolution rather than the more complicated CS reconstruction. J. Magn. Reson. Imaging 2016;44:433-444.
© 2016 Wiley Periodicals, Inc.

Entities:  

Keywords:  accelerated magnetic resonance imaging; compressed sensing MRI; diagnostic accuracy; diagnostic quality assessment; magnetic resonance neuroimaging; parallel imaging

Mesh:

Year:  2016        PMID: 26777856     DOI: 10.1002/jmri.25149

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  8 in total

1.  Compressed sensing MRI of different organs: ready for clinical daily practice?

Authors:  Bénédicte Marie Anne Delattre; Sana Boudabbous; Catrina Hansen; Angeliki Neroladaki; Anne-Lise Hachulla; Maria Isabel Vargas
Journal:  Eur Radiol       Date:  2019-07-01       Impact factor: 5.315

2.  Combination of compressed sensing and parallel imaging for T2-weighted imaging of the oral cavity in healthy volunteers: comparison with parallel imaging.

Authors:  Hayato Tomita; Yuki Deguchi; Hirofumi Fukuchi; Atsuko Fujikawa; Yoshiko Kurihara; Kaoru Kitsukawa; Hidefumi Mimura; Yasuyuki Kobayashi
Journal:  Eur Radiol       Date:  2021-01-30       Impact factor: 5.315

3.  Prospective motion correction enables highest resolution time-of-flight angiography at 7T.

Authors:  Hendrik Mattern; Alessandro Sciarra; Frank Godenschweger; Daniel Stucht; Falk Lüsebrink; Georg Rose; Oliver Speck
Journal:  Magn Reson Med       Date:  2017-12-11       Impact factor: 4.668

4.  Compressed Sensing-Sensitivity Encoding (CS-SENSE) Accelerated Brain Imaging: Reduced Scan Time without Reduced Image Quality.

Authors:  J E Vranic; N M Cross; Y Wang; D S Hippe; E de Weerdt; M Mossa-Basha
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-06       Impact factor: 3.825

5.  Super-resolution head and neck MRA using deep machine learning.

Authors:  Ioannis Koktzoglou; Rong Huang; William J Ankenbrandt; Matthew T Walker; Robert R Edelman
Journal:  Magn Reson Med       Date:  2021-02-22       Impact factor: 3.737

6.  Accelerated Time-of-Flight Magnetic Resonance Angiography with Sparse Undersampling and Iterative Reconstruction for the Evaluation of Intracranial Arteries.

Authors:  Hehan Tang; Na Hu; Yuan Yuan; Chunchao Xia; Xiumin Liu; Panli Zuo; Aurelien F Stalder; Michaela Schmidt; Xiaoyue Zhou; Bin Song; Jiayu Sun
Journal:  Korean J Radiol       Date:  2019-02       Impact factor: 3.500

7.  Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.

Authors:  Masafumi Kidoh; Kensuke Shinoda; Mika Kitajima; Kenzo Isogawa; Masahito Nambu; Hiroyuki Uetani; Kosuke Morita; Takeshi Nakaura; Machiko Tateishi; Yuichi Yamashita; Yasuyuki Yamashita
Journal:  Magn Reson Med Sci       Date:  2019-09-04       Impact factor: 2.471

8.  A Review of Translational Magnetic Resonance Imaging in Human and Rodent Experimental Models of Small Vessel Disease.

Authors:  Michael S Stringer; Hedok Lee; Mikko T Huuskonen; Bradley J MacIntosh; Rosalind Brown; Axel Montagne; Sarah Atwi; Joel Ramirez; Maurits A Jansen; Ian Marshall; Sandra E Black; Berislav V Zlokovic; Helene Benveniste; Joanna M Wardlaw
Journal:  Transl Stroke Res       Date:  2020-09-16       Impact factor: 6.829

  8 in total

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