Paper
19 March 2014 Rapid scatter estimation for CBCT using the Boltzmann transport equation
Mingshan Sun, Alex Maslowski, Ian Davis, Todd Wareing, Gregory Failla, Josh Star-Lack
Author Affiliations +
Abstract
Scatter in cone-beam computed tomography (CBCT) is a significant problem that degrades image contrast, uniformity and CT number accuracy. One means of estimating and correcting for detected scatter is through an iterative deconvolution process known as scatter kernel superposition (SKS). While the SKS approach is efficient, clinically significant errors on the order 2-4% (20-40 HU) still remain. We have previously shown that the kernel method can be improved by perturbing the kernel parameters based on reference data provided by limited Monte Carlo simulations of a first-pass reconstruction. In this work, we replace the Monte Carlo modeling with a deterministic Boltzmann solver (AcurosCTS) to generate the reference scatter data in a dramatically reduced time. In addition, the algorithm is improved so that instead of adjusting kernel parameters, we directly perturb the SKS scatter estimates. Studies were conducted on simulated data and on a large pelvis phantom scanned on a tabletop system. The new method reduced average reconstruction errors (relative to a reference scan) from 2.5% to 1.8%, and significantly improved visualization of low contrast objects. In total, 24 projections were simulated with an AcurosCTS execution time of 22 sec/projection using an 8-core computer. We have ported AcurosCTS to the GPU, and current run-times are approximately 4 sec/projection using two GPU’s running in parallel.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mingshan Sun, Alex Maslowski, Ian Davis, Todd Wareing, Gregory Failla, and Josh Star-Lack "Rapid scatter estimation for CBCT using the Boltzmann transport equation", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90330Z (19 March 2014); https://doi.org/10.1117/12.2043312
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CITATIONS
Cited by 7 scholarly publications and 3 patents.
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KEYWORDS
Monte Carlo methods

Reconstruction algorithms

Sensors

Photons

Computer simulations

Data modeling

Detection and tracking algorithms

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