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Title: SU-C-209-05: Monte Carlo Model of a Prototype Backscatter X-Ray (BSX) Imager for Projective and Selective Object-Plane Imaging

Abstract

Purpose: To develop a Monte Carlo N-Particle (MCNP) model for the validation of a prototype backscatter x-ray (BSX) imager, and optimization of BSX technology for medical applications, including selective object-plane imaging. Methods: BSX is an emerging technology that represents an alternative to conventional computed tomography (CT) and projective digital radiography (DR). It employs detectors located on the same side as the incident x-ray source, making use of backscatter and avoiding ring geometry to enclose the imaging object. Current BSX imagers suffer from low spatial resolution. A MCNP model was designed to replicate a BSX prototype used for flaw detection in industrial materials. This prototype consisted of a 1.5mm diameter 60kVp pencil beam surrounded by a ring of four 5.0cm diameter NaI scintillation detectors. The imaging phantom consisted of a 2.9cm thick aluminum plate with five 0.6cm diameter holes drilled halfway. The experimental image was created using a raster scanning motion (in 1.5mm increments). Results: A qualitative comparison between the physical and simulated images showed very good agreement with 1.5mm spatial resolution in plane perpendicular to incident x-ray beam. The MCNP model developed the concept of radiography by selective plane detection (RSPD) for BSX, whereby specific object planes can be imagedmore » by varying kVp. 10keV increments in mean x-ray energy yielded 4mm thick slice resolution in the phantom. Image resolution in the MCNP model can be further increased by increasing the number of detectors, and decreasing raster step size. Conclusion: MCNP modelling was used to validate a prototype BSX imager and introduce the RSPD concept, allowing for selective object-plane imaging. There was very good visual agreement between the experimental and MCNP imaging. Beyond optimizing system parameters for the existing prototype, new geometries can be investigated for volumetric image acquisition in medical applications. This material is based upon work supported under an Integrated University Program Graduate Fellowship sponsored by the Department of Energy Office of Nuclear Energy.« less

Authors:
; ; ;  [1]
  1. University of Florida, Gainesville, FL (United States)
Publication Date:
OSTI Identifier:
22624348
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED TOMOGRAPHY; ENERGY YIELD; IMAGES; MONTE CARLO METHOD; NAI DETECTORS; OPTIMIZATION; PHANTOMS; SCINTILLATIONS; SIMULATION; SODIUM IODIDES; SPATIAL RESOLUTION; VALIDATION; X-RAY SOURCES

Citation Formats

Rolison, L, Samant, S, Baciak, J, and Jordan, K. SU-C-209-05: Monte Carlo Model of a Prototype Backscatter X-Ray (BSX) Imager for Projective and Selective Object-Plane Imaging. United States: N. p., 2016. Web. doi:10.1118/1.4955594.
Rolison, L, Samant, S, Baciak, J, & Jordan, K. SU-C-209-05: Monte Carlo Model of a Prototype Backscatter X-Ray (BSX) Imager for Projective and Selective Object-Plane Imaging. United States. doi:10.1118/1.4955594.
Rolison, L, Samant, S, Baciak, J, and Jordan, K. 2016. "SU-C-209-05: Monte Carlo Model of a Prototype Backscatter X-Ray (BSX) Imager for Projective and Selective Object-Plane Imaging". United States. doi:10.1118/1.4955594.
@article{osti_22624348,
title = {SU-C-209-05: Monte Carlo Model of a Prototype Backscatter X-Ray (BSX) Imager for Projective and Selective Object-Plane Imaging},
author = {Rolison, L and Samant, S and Baciak, J and Jordan, K},
abstractNote = {Purpose: To develop a Monte Carlo N-Particle (MCNP) model for the validation of a prototype backscatter x-ray (BSX) imager, and optimization of BSX technology for medical applications, including selective object-plane imaging. Methods: BSX is an emerging technology that represents an alternative to conventional computed tomography (CT) and projective digital radiography (DR). It employs detectors located on the same side as the incident x-ray source, making use of backscatter and avoiding ring geometry to enclose the imaging object. Current BSX imagers suffer from low spatial resolution. A MCNP model was designed to replicate a BSX prototype used for flaw detection in industrial materials. This prototype consisted of a 1.5mm diameter 60kVp pencil beam surrounded by a ring of four 5.0cm diameter NaI scintillation detectors. The imaging phantom consisted of a 2.9cm thick aluminum plate with five 0.6cm diameter holes drilled halfway. The experimental image was created using a raster scanning motion (in 1.5mm increments). Results: A qualitative comparison between the physical and simulated images showed very good agreement with 1.5mm spatial resolution in plane perpendicular to incident x-ray beam. The MCNP model developed the concept of radiography by selective plane detection (RSPD) for BSX, whereby specific object planes can be imaged by varying kVp. 10keV increments in mean x-ray energy yielded 4mm thick slice resolution in the phantom. Image resolution in the MCNP model can be further increased by increasing the number of detectors, and decreasing raster step size. Conclusion: MCNP modelling was used to validate a prototype BSX imager and introduce the RSPD concept, allowing for selective object-plane imaging. There was very good visual agreement between the experimental and MCNP imaging. Beyond optimizing system parameters for the existing prototype, new geometries can be investigated for volumetric image acquisition in medical applications. This material is based upon work supported under an Integrated University Program Graduate Fellowship sponsored by the Department of Energy Office of Nuclear Energy.},
doi = {10.1118/1.4955594},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
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