Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3009977.3010054acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicvgipConference Proceedingsconference-collections
research-article

Multi-slice tomographic reconstruction: to couple or not to couple

Published: 18 December 2016 Publication History

Abstract

Recent work in tomography focuses on algorithms that enable faster and more accurate reconstruction from as few measurements as possible. We review the advantage of jointly reconstructing multiple slices and show that joint reconstruction may suffer in the presence of adjacent dissimilar slices. This gives rise to the need to detect similarity or dissimilarity of unknown images before performing joint reconstruction.
We propose a method to detect 'similar' slices directly from their tomographic measurements and juxtapose these similar slices. Since the images themselves are not available by definition, we compute similarity between slices based on image moments; these in turn are estimated in a novel way from Radon projection moments. A segmented least squares algorithm is then designed to couple only similar slices. Our results confirm the benefit of this method for tomographic reconstruction.

References

[1]
Brainweb MRI dataset. http://brainweb.bic.mni.mcgill.ca/brainweb/selection_normal.html, last viewed-July, 2016.
[2]
Humerus CT dataset. http://isbweb.org/data/vsj/humeral/, last viewed-July, 2016.
[3]
Lumbar MRI dataset. http://www.osirix-viewer.com/datasets/, last viewed-July, 2016.
[4]
Walnut CT dataset. http://www.voreen.org/108-Data-Sets.html, last viewed-July, 2016.
[5]
M. Asif, L. Hamilton, M. Brummer, and J. Romberg. Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI. Magnetic Resonance in Medicine, 70(3):800--812, Sept. 2013.
[6]
E. Candès and J. Romberg. Sparsity and incoherence in compressive sampling. Inverse problems, 23(3):969, 2007.
[7]
E. Candès, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on pure and applied mathematics, 59(8):1207--1223, 2006.
[8]
D. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289--1306, April 2006.
[9]
S. Foucart and H. Rauhut. A Mathematical Introduction to Compressive Sensing. Birkhäser Basel, 2013.
[10]
R. C. Gonzalez and R. E. Woods. Digital Image Procesing. Pearson, third edition, 2009.
[11]
A. K. Jain. Fundamentals of Digital Image Procesing. Prentice-Hall of India Pvt. Ltd., first edition, 1989.
[12]
S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing, 1(4):606--617, Dec 2007. {13} J. Kleinberg and E. Tardos. Algorithm Design. Pearson, 2006.
[13]
K. Koh, S.-J. Kim, and S. Boyd. l1-ls: Simple matlab solver for l1-regularized least squares problems. https://stanford.edu/~boyd/l1_ls/, last viewed-July, 2016.
[14]
W. Lu and N. Vaswani. Modified compressive sensing for real-time dynamic MR imaging. In ICIP, pages 3045--3048, Nov 2009.
[15]
M. Lustig, D. Donoho, and J. Pauly. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medical Imaging, 58(6):1182--1195, 2007.
[16]
M. Lustig, J. Santos, D. Donoho, and J. Pauly. kt sparse: High frame rate dynamic MRI exploiting spatio-temporal sparsity. Proceedings of the 13th Annual Meeting of ISMRM, Seattle, 2006.
[17]
R. F. Marcia, Z. Harmany, and R. Willett. Compressive coded aperture imaging. In Proc. SPIE, page 72460, 2009.
[18]
T. Wang and T. Sze. The image moment method for the limited range CT image reconstruction and pattern recognition. Pattern Recognition, 34(11):2145--2154, 2001.

Cited By

View all
  • (2016)A Comparison of Some Methods for Direct 2D Reconstruction from Discrete Projected ViewsProceedings of the 19th IAPR International Conference on Discrete Geometry for Computer Imagery - Volume 964710.1007/978-3-319-32360-2_9(117-128)Online publication date: 18-Apr-2016

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
December 2016
743 pages
ISBN:9781450347532
DOI:10.1145/3009977
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Google Inc.
  • QI: Qualcomm Inc.
  • Tata Consultancy Services
  • NVIDIA
  • MathWorks: The MathWorks, Inc.
  • Microsoft Research: Microsoft Research

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. compressive sensing
  2. image moments
  3. radon projection moments
  4. tomographic reconstruction

Qualifiers

  • Research-article

Conference

ICVGIP '16
Sponsor:
  • QI
  • MathWorks
  • Microsoft Research

Acceptance Rates

ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;
Overall Acceptance Rate 95 of 286 submissions, 33%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2016)A Comparison of Some Methods for Direct 2D Reconstruction from Discrete Projected ViewsProceedings of the 19th IAPR International Conference on Discrete Geometry for Computer Imagery - Volume 964710.1007/978-3-319-32360-2_9(117-128)Online publication date: 18-Apr-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media