Regularization design and control of change admission in prior-image-based reconstruction

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

Abstract

Nearly all reconstruction methods are controlled Through various parameter selections. Traditionally, such parameters are used To specify a particular noise and resolution Trade-off in The reconstructed image volumes. The introduction of reconstruction methods That incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated The selection of reconstruction parameters including Those associated with balancing information used from prior images with That of The measurement data. While a noise-resolution Tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including The possible introduction of false features and The failure To incorporate sufficient prior information To gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject To error and suboptimal solutions while exhaustive searches can involve a large number of Time-consuming iterative reconstructions. We propose a novel approach That prospectively determines optimal prior image regularization strength To accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations To The implicitly defined prior image-based reconstruction solution and predictive metrics used To estimate imaging performance. The proposed method is investigated in phantom experiments and The shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on The predictive approach is shown To agree well with Traditional exhaustive reconstruction searches, while yielding substantial reductions in computation Time. This suggests great potential of The proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength To ensure accurate reconstruction of specific anatomical changes.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
PublisherSPIE
Volume9033
ISBN (Print)9780819498267
DOIs
StatePublished - 2014
EventMedical Imaging 2014: Physics of Medical Imaging - San Diego, CA, United States
Duration: Feb 17 2014Feb 20 2014

Other

OtherMedical Imaging 2014: Physics of Medical Imaging
CountryUnited States
CitySan Diego, CA
Period2/17/142/20/14

Fingerprint

Computer-Assisted Image Processing
Imaging techniques
Image quality
Noise
Experiments
tradeoffs
penalties
methodology
dosage
shift
estimates
approximation

Keywords

  • CT reconstruction
  • Dose reduction
  • Noise-resolution Tradeoff
  • Penalized-likelihood
  • Prior-image reconstruction
  • Regularization
  • Shift variance
  • Sparse data

ASJC Scopus subject areas

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

Cite this

Dang, H., Siewerdsen, J., & Stayman, J. W. (2014). Regularization design and control of change admission in prior-image-based reconstruction. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 9033). [90330O] SPIE. https://doi.org/10.1117/12.2043781

Regularization design and control of change admission in prior-image-based reconstruction. / Dang, Hao; Siewerdsen, Jeff; Stayman, Joseph Webster.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9033 SPIE, 2014. 90330O.

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

Dang, H, Siewerdsen, J & Stayman, JW 2014, Regularization design and control of change admission in prior-image-based reconstruction. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 9033, 90330O, SPIE, Medical Imaging 2014: Physics of Medical Imaging, San Diego, CA, United States, 2/17/14. https://doi.org/10.1117/12.2043781
Dang H, Siewerdsen J, Stayman JW. Regularization design and control of change admission in prior-image-based reconstruction. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9033. SPIE. 2014. 90330O https://doi.org/10.1117/12.2043781
Dang, Hao ; Siewerdsen, Jeff ; Stayman, Joseph Webster. / Regularization design and control of change admission in prior-image-based reconstruction. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9033 SPIE, 2014.
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