Motion-compensated compressed sensing for dynamic imaging

Rajagopalan Sundaresan, Yookyung Kim, Mariappan S. Nadar, Ali Bilgin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume7798
DOIs
StatePublished - 2010
EventApplications of Digital Image Processing XXXIII - San Diego, CA, United States
Duration: Aug 2 2010Aug 4 2010

Other

OtherApplications of Digital Image Processing XXXIII
CountryUnited States
CitySan Diego, CA
Period8/2/108/4/10

Fingerprint

Compressed sensing
Compressed Sensing
Magnetic Resonance Imaging
magnetic resonance
Imaging
Imaging techniques
Motion
Image Sequence
Cardiac
Motion Compensation
Motion compensation
high resolution
Motion Estimation
Motion estimation
Phantom
Image resolution
Protein Sorting Signals
temporal resolution
Sparsity
imaging techniques

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Sundaresan, R., Kim, Y., Nadar, M. S., & Bilgin, A. (2010). Motion-compensated compressed sensing for dynamic imaging. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 7798). [77980A] https://doi.org/10.1117/12.861113

Motion-compensated compressed sensing for dynamic imaging. / Sundaresan, Rajagopalan; Kim, Yookyung; Nadar, Mariappan S.; Bilgin, Ali.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 7798 2010. 77980A.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sundaresan, R, Kim, Y, Nadar, MS & Bilgin, A 2010, Motion-compensated compressed sensing for dynamic imaging. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 7798, 77980A, Applications of Digital Image Processing XXXIII, San Diego, CA, United States, 8/2/10. https://doi.org/10.1117/12.861113
Sundaresan R, Kim Y, Nadar MS, Bilgin A. Motion-compensated compressed sensing for dynamic imaging. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 7798. 2010. 77980A https://doi.org/10.1117/12.861113
Sundaresan, Rajagopalan ; Kim, Yookyung ; Nadar, Mariappan S. ; Bilgin, Ali. / Motion-compensated compressed sensing for dynamic imaging. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 7798 2010.
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