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A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials.

Publication ,  Journal Article
Sharma, S; Abadi, E; Kapadia, A; Segars, WP; Samei, E
Published in: Phys Med Biol
March 23, 2021

Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.

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Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

March 23, 2021

Volume

66

Issue

7

Location

England

Related Subject Headings

  • Scattering, Radiation
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Humans
  • Cone-Beam Computed Tomography
  • Computer Simulation
  • 5105 Medical and biological physics
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering
 

Citation

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Sharma, S., Abadi, E., Kapadia, A., Segars, W. P., & Samei, E. (2021). A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials. Phys Med Biol, 66(7). https://doi.org/10.1088/1361-6560/abeb32
Sharma, Shobhit, Ehsan Abadi, Anuj Kapadia, W Paul Segars, and Ehsan Samei. “A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials.Phys Med Biol 66, no. 7 (March 23, 2021). https://doi.org/10.1088/1361-6560/abeb32.
Sharma S, Abadi E, Kapadia A, Segars WP, Samei E. A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials. Phys Med Biol. 2021 Mar 23;66(7).
Sharma, Shobhit, et al. “A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials.Phys Med Biol, vol. 66, no. 7, Mar. 2021. Pubmed, doi:10.1088/1361-6560/abeb32.
Sharma S, Abadi E, Kapadia A, Segars WP, Samei E. A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials. Phys Med Biol. 2021 Mar 23;66(7).
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

March 23, 2021

Volume

66

Issue

7

Location

England

Related Subject Headings

  • Scattering, Radiation
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Humans
  • Cone-Beam Computed Tomography
  • Computer Simulation
  • 5105 Medical and biological physics
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering