Paper
9 March 2017 Comparative study of bowtie and patient scatter in diagnostic CT
Prakhar Prakash, John M. Boudry
Author Affiliations +
Abstract
A fast, GPU accelerated Monte Carlo engine for simulating relevant photon interaction processes over the diagnostic energy range in third-generation CT systems was developed to study the relative contributions of bowtie and object scatter to the total scatter reaching an imaging detector. Primary and scattered projections for an elliptical water phantom (major axis set to 300mm) with muscle and fat inserts were simulated for a typical diagnostic CT system as a function of anti-scatter grid (ASG) configurations. The ASG design space explored grid orientation, i.e. septa either a) parallel or b) parallel and perpendicular to the axis of rotation, as well as septa height. The septa material was Tungsten. The resulting projections were reconstructed and the scatter induced image degradation was quantified using common CT image metrics (such as Hounsfield Unit (HU) inaccuracy and loss in contrast), along with a qualitative review of image artifacts. Results indicate object scatter dominates total scatter in the detector channels under the shadow of the imaged object with the bowtie scatter fraction progressively increasing towards the edges of the object projection. Object scatter was shown to be the driving factor behind HU inaccuracy and contrast reduction in the simulated images while shading artifacts and elevated loss in HU accuracy at the object boundary were largely attributed to bowtie scatter. Because the impact of bowtie scatter could not be sufficiently mitigated with a large grid ratio ASG, algorithmic correction may be necessary to further mitigate these artifacts.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Prakhar Prakash and John M. Boudry "Comparative study of bowtie and patient scatter in diagnostic CT", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322G (9 March 2017); https://doi.org/10.1117/12.2253012
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KEYWORDS
Sensors

Monte Carlo methods

Diagnostics

Computed tomography

Image sensors

Signal attenuation

Tungsten

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