Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise

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

Abstract

Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLSe). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLSâ-). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLSo-'' and PWLSâ-, and combined PWLSâ-e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLSe method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ∼15-25% at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ∼15% in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLSâ- method showed a ∼40% reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Guang-Hong Chen, Taly Gilat Schmidt
PublisherSPIE
ISBN (Electronic)9781510625433
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10948
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
CountryUnited States
CitySan Diego
Period2/17/192/20/19

Fingerprint

Least-Squares Analysis
readout
Noise
Cones
cones
fluence
Modulation
Detectors
modulation
electronics
Image quality
Cone-Beam Computed Tomography
Quantum noise
Imaging techniques
Head
detectors
least squares method
Statistical methods
filters
Weights and Measures

Keywords

  • Cone-beam CT
  • Dual-gain
  • Fluence modulation
  • Image noise
  • Image quality
  • Model-based image reconstruction

ASJC Scopus subject areas

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

Cite this

Wu, P., Sisniega, A., Stayman, J. W., Zbijewski, W., Foos, D., Wang, X., ... Siewerdsen, J. H. (2019). Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise. In H. Bosmans, G-H. Chen, & T. G. Schmidt (Eds.), Medical Imaging 2019: Physics of Medical Imaging [1094814] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948). SPIE. https://doi.org/10.1117/12.2513417

Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise. / Wu, P.; Sisniega, A.; Stayman, J. W.; Zbijewski, W.; Foos, D.; Wang, X.; Aygun, N.; Stevens, R.; Siewerdsen, J. H.

Medical Imaging 2019: Physics of Medical Imaging. ed. / Hilde Bosmans; Guang-Hong Chen; Taly Gilat Schmidt. SPIE, 2019. 1094814 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948).

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

Wu, P, Sisniega, A, Stayman, JW, Zbijewski, W, Foos, D, Wang, X, Aygun, N, Stevens, R & Siewerdsen, JH 2019, Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise. in H Bosmans, G-H Chen & TG Schmidt (eds), Medical Imaging 2019: Physics of Medical Imaging., 1094814, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10948, SPIE, Medical Imaging 2019: Physics of Medical Imaging, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2513417
Wu P, Sisniega A, Stayman JW, Zbijewski W, Foos D, Wang X et al. Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise. In Bosmans H, Chen G-H, Schmidt TG, editors, Medical Imaging 2019: Physics of Medical Imaging. SPIE. 2019. 1094814. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2513417
Wu, P. ; Sisniega, A. ; Stayman, J. W. ; Zbijewski, W. ; Foos, D. ; Wang, X. ; Aygun, N. ; Stevens, R. ; Siewerdsen, J. H. / Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise. Medical Imaging 2019: Physics of Medical Imaging. editor / Hilde Bosmans ; Guang-Hong Chen ; Taly Gilat Schmidt. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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abstract = "Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLSe). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLS{\^a}-). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLSo-'' and PWLS{\^a}-, and combined PWLS{\^a}-e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLSe method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ∼15-25{\%} at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ∼15{\%} in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLS{\^a}- method showed a ∼40{\%} reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.",
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