Literature DB >> 25968400

Compressive sensing in medical imaging.

Christian G Graff, Emil Y Sidky.   

Abstract

The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

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Year:  2015        PMID: 25968400      PMCID: PMC4669980          DOI: 10.1364/AO.54.000C23

Source DB:  PubMed          Journal:  Appl Opt        ISSN: 1559-128X            Impact factor:   1.980


  76 in total

1.  Unmatched projector/backprojector pairs in an iterative reconstruction algorithm.

Authors:  G L Zeng; G T Gullberg
Journal:  IEEE Trans Med Imaging       Date:  2000-05       Impact factor: 10.048

2.  Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities.

Authors:  Bradley P Sutton; Douglas C Noll; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2003-02       Impact factor: 10.048

3.  Using human and model performance to compare MRI reconstructions.

Authors:  M Dylan Tisdall; M Stella Atkins
Journal:  IEEE Trans Med Imaging       Date:  2006-11       Impact factor: 10.048

Review 4.  Computed tomography--an increasing source of radiation exposure.

Authors:  David J Brenner; Eric J Hall
Journal:  N Engl J Med       Date:  2007-11-29       Impact factor: 91.245

Review 5.  Flat-detector computed tomography (FD-CT).

Authors:  Willi A Kalender; Yiannis Kyriakou
Journal:  Eur Radiol       Date:  2007-06-23       Impact factor: 5.315

6.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography.

Authors:  R Gordon; R Bender; G T Herman
Journal:  J Theor Biol       Date:  1970-12       Impact factor: 2.691

7.  Ordered subsets algorithms for transmission tomography.

Authors:  H Erdogan; J A Fessler
Journal:  Phys Med Biol       Date:  1999-11       Impact factor: 3.609

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Authors:  Polad M Shikhaliev; Shannon G Fritz
Journal:  Phys Med Biol       Date:  2011-03-02       Impact factor: 3.609

9.  A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction.

Authors:  Sathish Ramani; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2011-11-08       Impact factor: 10.048

10.  Spatially regularized compressed sensing for high angular resolution diffusion imaging.

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Journal:  IEEE Trans Med Imaging       Date:  2011-05       Impact factor: 10.048

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Authors:  Tzu C Lee; Adam M Alessio; Robert M Miyaoka; Paul E Kinahan
Journal:  Q J Nucl Med Mol Imaging       Date:  2015-11-17       Impact factor: 2.346

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Review 6.  Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist.

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7.  Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories.

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Journal:  Sensors (Basel)       Date:  2022-03-02       Impact factor: 3.576

  9 in total

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