Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties

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

Abstract

This paper introduces a general reconstruction technique for using unregistered prior images within model-based penalized- likelihood reconstruction. The resulting estimator is implicitly defined as the maximizer of an objective composed of a likelihood term that enforces a fit to data measurements and that incorporates the heteroscedastic statistics of the tomographic problem; and a penalty term that penalizes differences from prior image. Compressed sensing (p-norm) penalties are used to allow for differences between the reconstruction and the prior. Moreover, the penalty is parameterized with registration terms that are jointly optimized as part of the reconstruction to allow for mismatched images. We apply this novel approach to synthetic data using a digital phantom as well as tomographic data derived from a conebeam CT test bench. The test bench data includes sparse data acquisitions of a custom modifiable anthropomorphic lung phantom that can simulate lung nodule surveillance. Sparse reconstructions using this approach demonstrate the simultaneous incorporation of prior imagery and the necessary registration to utilize those priors.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2011
Subtitle of host publicationPhysics of Medical Imaging
DOIs
StatePublished - May 13 2011
EventMedical Imaging 2011: Physics of Medical Imaging - Lake Buena Vista, FL, United States
Duration: Feb 13 2011Feb 17 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7961
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2011: Physics of Medical Imaging
CountryUnited States
CityLake Buena Vista, FL
Period2/13/112/17/11

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Stayman, J. W., Zbijewski, W., Otake, Y., Uneri, A., Schafer, S., Lee, J., Prince, J. L., & Siewerdsen, J. H. (2011). Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties. In Medical Imaging 2011: Physics of Medical Imaging [79611L] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7961). https://doi.org/10.1117/12.878075