Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Modified restricted Boltzmann machine (mRBM) for denoising of motion artifact-induced MRI scans

Modified restricted Boltzmann machine (mRBM) for denoising of motion artifact-induced MRI scans Motion artifacts in magnetic resonance imaging (MRI) are one of the issues that can affect diagnosis. To remove this motion artifact from MRI, we propose a modified restricted Boltzmann machine (mRBM). mRBM can train itself using probability distribution over a set of input images and generate artifact-free images. We proposed a feedback network to the existing RBM for denoising motion artifact-induced MRI data. In mRBM, the number of weights and biases that must be tuned is confined to the size of the image and hence mRBM is significantly fast. For a 256 × 256-pixel image, mRBM output can be achieved within 2 s post-training. The proposed method has a root mean squared error (RMSE) of 0.0034. Since with the help of mRBM, we do not need MRI to be repeated; thus, the speed of diagnosis is significantly improved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Modified restricted Boltzmann machine (mRBM) for denoising of motion artifact-induced MRI scans

Download PDF
11 pages

Loading next page...
 
/lp/springer-journals/modified-restricted-boltzmann-machine-mrbm-for-denoising-of-motion-gh0LcDnYJV
Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-022-00252-w
Publisher site
See Article on Publisher Site

Abstract

Motion artifacts in magnetic resonance imaging (MRI) are one of the issues that can affect diagnosis. To remove this motion artifact from MRI, we propose a modified restricted Boltzmann machine (mRBM). mRBM can train itself using probability distribution over a set of input images and generate artifact-free images. We proposed a feedback network to the existing RBM for denoising motion artifact-induced MRI data. In mRBM, the number of weights and biases that must be tuned is confined to the size of the image and hence mRBM is significantly fast. For a 256 × 256-pixel image, mRBM output can be achieved within 2 s post-training. The proposed method has a root mean squared error (RMSE) of 0.0034. Since with the help of mRBM, we do not need MRI to be repeated; thus, the speed of diagnosis is significantly improved.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2023

Keywords: MRI; mRBM; Motion artifact

References

  • Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion
    Atkinson, D; Hill, DL; Stoyle, PN; Summers, PE; Keevil, SF
  • Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the International League Against Epilepsy Neuroimaging Task Force
    Bernasconi, A; Cendes, F; Theodore, WH; Gill, RS; Koepp, MJ; Hogan, RE; Jackson, GD; Federico, P; Labate, A; Vaudano, AE
  • Improving the generalizability of convolutional neural network-based segmentation on CMR images
    Chen, C; Bai, W; Davies, RH; Bhuva, AN; Manisty, CH; Augusto, JB; Moon, JC; Aung, N; Lee, AM; Sanghvi, MM
  • Joint neural collaborative filtering for recommender systems
    Chen, W; Cai, F; Chen, H; Rijke, MD
  • Mitigating head motion artifact in functional connectivity MRI
    Ciric, R; Rosen, AF; Erus, G; Cieslak, M; Adebimpe, A; Cook, PA; Bassett, DS; Davatzikos, C; Wolf, DH; Satterthwaite, TD
  • Follow-up of negative MRI-targeted prostate biopsies: when are we missing cancer?
    Gold, SA; Hale, GR; Bloom, JB; Smith, CP; Rayn, KN; Valera, V; Wood, BJ; Choyke, PL; Turkbey, B; Pinto, PA
  • Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks
    Hjelm, RD; Calhoun, VD; Salakhutdinov, R; Allen, EA; Adali, T; Plis, SM
  • Two stage residual CNN for texture denoising and structure enhancement on low dose CT image
    Huang, L; Jiang, H; Li, S; Bai, Z; Zhang, J
  • An overview of deep learning in medical imaging focusing on MRI
    Lundervold, AS; Lundervold, A
  • Measurement and correction of microscopic head motion during magnetic resonance imaging of the brain
    Maclaren, J; Armstrong, BS; Barrows, RT; Danishad, K; Ernst, T; Foster, CL; Gumus, K; Herbst, M; Kadashevich, IY; Kusik, TP
  • Improvement of motion artifacts in brain MRI using deep learning by simulation training data
    Muro, I; Shimizu, S; Tsukamoto, H
  • Unpaired MR motion artifact deep learning using outlier-rejecting bootstrap aggregation
    Oh, G; Lee, JE; Ye, JC
  • Highly accelerated submillimeter resolution 3D GRASE with controlled blurring in-weighted functional MRI at 7 Tesla: a feasibility study
    Park, S; Torrisi, S; Townsend, JD; Beckett, A; Feinberg, DA
  • Magnetic resonance microimaging for noninvasive quantification of myocardial function and mass in the mouse
    Ruff, J; Wiesmann, F; Hiller, K-H; Voll, S; von Kienlin, M; Bauer, WR; Rommel, E; Neubauer, S; Haase, A
  • Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver
    Tamada, D; Kromrey, M-L; Ichikawa, S; Onishi, H; Motosugi, U
  • Acute human stroke studied by whole brain echo planar diffusion-weighted magnetic resonance imaging
    Warach, S; Gaa, J; Siewert, B; Wielopolski, P; Edelman, RR
  • A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images
    Wu, J; Mazur, TR; Ruan, S; Lian, C; Daniel, N; Lashmett, H; Ochoa, L; Zoberi, I; Anastasio, MA; Gach, HM
  • Whole process monitoring based on unstable neuron output information in hidden layers of deep belief network
    Yu, J; Yan, X