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A fast, linear Boltzmann transport equation solver for computed tomography dose calculation (Acuros CTD).

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Title: A fast, linear Boltzmann transport equation solver for computed tomography dose calculation (Acuros CTD).
Authors: Wang, Adam1 (AUTHOR) adamwang@stanford.edu, Maslowski, Alexander1 (AUTHOR), Wareing, Todd1 (AUTHOR), Star‐Lack, Josh1 (AUTHOR), Schmidt, Taly Gilat2 (AUTHOR)
Source: Medical Physics. Feb2019, Vol. 46 Issue 2, p925-933. 9p.
Subjects: Computed tomography, Boltzmann's equation, Radiation doses, Problem solving, Monte Carlo method
Abstract: Purpose: To improve dose reporting of CT scans, patient‐specific organ doses are highly desired. However, estimating the dose distribution in a fast and accurate manner remains challenging, despite advances in Monte Carlo methods. In this work, we present an alternative method that deterministically solves the linear Boltzmann transport equation (LBTE), which governs the behavior of x‐ray photon transport through an object. Methods: Our deterministic solver for CT dose (Acuros CTD) is based on the same approach used to estimate scatter in projection images of a CT scan (Acuros CTS). A deterministic method is used to compute photon fluence within the object, which is then converted to deposited energy by multiplying by known, material‐specific conversion factors. To benchmark Acuros CTD, we used the AAPM Task Group 195 test for CT dose, which models an axial, fan beam scan (10 mm thick beam) and calculates energy deposited in each organ of an anthropomorphic phantom. We also validated our own Monte Carlo implementation of Geant4 to use as a reference to compare Acuros against for other common geometries like an axial, cone beam scan (160 mm thick beam) and a helical scan (40 mm thick beam with table motion for a pitch of 1). Results: For the fan beam scan, Acuros CTD accurately estimated organ dose, with a maximum error of 2.7% and RMSE of 1.4% when excluding organs with <0.1% of the total energy deposited. The cone beam and helical scans yielded similar levels of accuracy compared to Geant4. Increasing the number of source positions beyond 18 or decreasing the voxel size below 5 × 5 × 5 mm3 provided marginal improvement to the accuracy for the cone beam scan but came at the expense of increased run time. Across the different scan geometries, run time of Acuros CTD ranged from 8 to 23 s. Conclusions: In this digital phantom study, a deterministic LBTE solver was capable of fast and accurate organ dose estimates. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Medical+Physics%22&quot;&gt;Medical Physics&lt;/searchLink&gt;. Feb2019, Vol. 46 Issue 2, p925-933. 9p.
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  Data: Purpose: To improve dose reporting of CT scans, patient‐specific organ doses are highly desired. However, estimating the dose distribution in a fast and accurate manner remains challenging, despite advances in Monte Carlo methods. In this work, we present an alternative method that deterministically solves the linear Boltzmann transport equation&#160;(LBTE), which governs the behavior of x‐ray photon transport through an object. Methods: Our deterministic solver for CT dose (Acuros CTD) is based on the same approach used to estimate scatter in projection images of a CT scan (Acuros CTS). A deterministic method is used to compute photon fluence within the object, which is then converted to deposited energy by multiplying by known, material‐specific conversion factors. To benchmark Acuros CTD, we used the AAPM Task Group 195 test for CT dose, which models an axial, fan beam scan (10&#160;mm thick beam) and calculates energy deposited in each organ of an anthropomorphic phantom. We also validated our own Monte Carlo implementation of Geant4 to use as a reference to compare Acuros against for other common geometries like an axial, cone beam scan (160&#160;mm thick beam) and a helical scan (40&#160;mm thick beam with table motion for a pitch of 1). Results: For the fan beam scan, Acuros CTD accurately estimated organ dose, with a maximum error of 2.7% and RMSE of 1.4% when excluding organs with &lt;0.1% of the total energy deposited. The cone beam and helical scans yielded similar levels of accuracy compared to Geant4. Increasing the number of source positions beyond 18 or decreasing the voxel size below 5&#160;&#215;&#160;5 &#215; 5&#160;mm3 provided marginal improvement to the accuracy for the cone beam scan but came at the expense of increased run time. Across the different scan geometries, run time of Acuros CTD ranged from 8 to 23&#160;s. Conclusions: In this digital phantom study, a deterministic LBTE solver was capable of fast and accurate organ dose estimates. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=134577203
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        Value: 10.1002/mp.13305
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      – SubjectFull: Boltzmann's equation
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      – SubjectFull: Radiation doses
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      – SubjectFull: Monte Carlo method
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              Text: Feb2019
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              Y: 2019
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