Literature DB >> 23538890

Clinical image quality assessment of accelerated magnetic resonance neuroimaging using compressed sensing.

Samir D Sharma1, Caroline L Fong, Brian S Tzung, Meng Law, Krishna S Nayak.   

Abstract

PURPOSE: The aim of this study was to determine to what degree current compressed sensing methods are capable of accelerating clinical magnetic resonance neuroimaging sequences.
METHODS: Two 2-dimensional clinical sequences were chosen for this study because of their long scan times. A pilot study was used to establish the sampling scheme and regularization parameter needed in compressed sensing reconstruction. These findings were used in a subsequent blinded study in which images reconstructed using compressed sensing were evaluated by 2 board-certified neuroradiologists. Image quality was evaluated at up to 10 anatomical features.
RESULTS: The findings indicate that compressed sensing may provide 2-fold acceleration of certain clinical magnetic resonance neuroimaging sequences. A global ringing artifact and image blurring were identified as the 2 primary artifacts that would hinder the ability to confidently discern abnormality.
CONCLUSION: Compressed sensing is able to moderately accelerate certain neuroimaging sequences without severe loss of clinically relevant information. For those sequences with coarser spatial resolution and/or at a higher acceleration factor, artifacts degrade the quality of the reconstructed image to a point where they are of little to no clinical value.

Mesh:

Year:  2013        PMID: 23538890     DOI: 10.1097/RLI.0b013e31828a012d

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  25 in total

Review 1.  Compressed sensing MRI: a review of the clinical literature.

Authors:  Oren N Jaspan; Roman Fleysher; Michael L Lipton
Journal:  Br J Radiol       Date:  2015-09-24       Impact factor: 3.039

2.  Simulation study of the effect of golden-angle KWIC with generalized kinetic model analysis on diagnostic accuracy for lesion discrimination.

Authors:  Melanie Freed; Sungheon G Kim
Journal:  Magn Reson Imaging       Date:  2014-09-28       Impact factor: 2.546

3.  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

4.  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

5.  Undersampling patterns in k-space for compressed sensing MRI using two-dimensional Cartesian sampling.

Authors:  Shinya Kojima; Hiroyuki Shinohara; Takeyuki Hashimoto; Shigeru Suzuki
Journal:  Radiol Phys Technol       Date:  2018-08-04

6.  Signal-to-noise ratio-enhancing joint reconstruction for improved diffusion imaging of mouse spinal cord white matter injury.

Authors:  Joong Hee Kim; Sheng-Kwei Song; Justin P Haldar
Journal:  Magn Reson Med       Date:  2015-03-30       Impact factor: 4.668

7.  3D true-phase polarity recovery with independent phase estimation using three-tier stacks based region growing (3D-TRIPS).

Authors:  Haining Liu; Gregory J Wilson; Niranjan Balu; Jeffrey H Maki; Daniel S Hippe; Wei Wu; Hiroko Watase; Jinnan Wang; Martin L Gunn; Chun Yuan
Journal:  MAGMA       Date:  2017-12-07       Impact factor: 2.310

8.  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

9.  Reconstruction of randomly under-sampled spectra for in vivo13C magnetic resonance spectroscopy.

Authors:  Ningzhi Li; Shizhe Li; Jun Shen
Journal:  Magn Reson Imaging       Date:  2016-12-07       Impact factor: 2.546

10.  Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Authors:  Mehmet Akçakaya; Steen Moeller; Sebastian Weingärtner; Kâmil Uğurbil
Journal:  Magn Reson Med       Date:  2018-09-18       Impact factor: 4.668

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