Modeling of body tissues for Monte Carlo simulation of radiotherapy treatments planned with conventional x-ray CT systems

Kanematsu, Nobuyuki; Inaniwa, Taku; Nakao, Minoru

doi:10.1088/0031-9155/61/13/5037

Cited by 38 publications

(54 citation statements)

References 28 publications

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“…The 52 tissues were classified by mass density into five tissues groups including lung, adipose/muscle, cartilage/spongy‐bone, cortical bone, and tooth tissues. The mass density border between lung tissue and adipose tissue is ρ = 0.90 g cm −3 . Only lung tissue had a mass density below the border value of ρ = 0.90 g cm −3 , while adipose, muscle, general organ, and some spongy‐bone tissues had values between 0.90 g cm −3 and 1.07 g cm −3 (Male: cervical spine, sternum, and sacrum, Female: sacrum and femora).…”

Section: Methodsmentioning

confidence: 99%

Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system

Nakao¹,

Ozawa

Yamada³

et al. 2017

J Applied Clin Med Phys

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The accuracy of computed tomography number to electron density (CT‐ED) calibration is a key component for dose calculations in an inhomogeneous medium. In a previous work, it was shown that the tolerance levels of CT‐ED calibration became stricter with an increase in tissue thickness and decrease in the effective energy of a photon beam. For the last decade, a low effective energy photon beam (e.g., flattening‐filter‐free (FFF)) has been used in clinical sites. However, its tolerance level has not been established yet. We established a relative electron density (ED) tolerance level for each tissue type with an FFF beam. The tolerance levels were calculated using the tissue maximum ratio (TMR) and each corresponding maximum tissue thickness. To determine the relative ED tolerance level, TMR data from a Varian accelerator and the adult reference computational phantom data in the International Commission on Radiological Protection publication 110 (ICRP‐110 phantom) were used in this study. The 52 tissue components of the ICRP‐110 phantom were classified by mass density as five tissues groups including lung, adipose/muscle, cartilage/spongy‐bone, cortical bone, and tooth tissue. In addition, the relative ED tolerance level of each tissue group was calculated when the relative dose error to local dose reached 2%. The relative ED tolerances of a 6 MVFFF beam for lung, adipose/muscle, and cartilage/spongy‐bone were ±0.044, ±0.022, and ±0.044, respectively. The thicknesses of the cortical bone and tooth groups were too small to define the tolerance levels. Because the tolerance levels of CT‐ED calibration are stricter with a decrease in the effective energy of the photon beam, the tolerance levels are determined by the lowest effective energy in useable beams for radiotherapy treatment planning systems.

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Section: Methodsmentioning

confidence: 99%

Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system

Nakao¹,

Ozawa

Yamada³

et al. 2017

J Applied Clin Med Phys

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“…To produce the CT number to SPR conversion functions, H -to-( S/S w ), we followed the procedure reported by Kanematsu et al [ 1 ], based on the standard tissue data in the International Commission on Radiological Protection Publication 110 [ 12 ]. They defined 11 representative tissues of the human body with mass density ρ and elemental weights w and compiled their SPRs.…”

Section: Methodsmentioning

confidence: 99%

“…Particle range is determined by integrating the stopping power ratios (SPRs) of body tissues relative to water along the beam path in a patient. The volumetric distribution of the SPRs in a patient is conventionally obtained from the x-ray computed tomography (CT) data, using a predetermined polyline relationship between CT number and SPR of the body tissues, referred to as the CT number-to-SPR conversion function [ 1 – 3 ]. Uncertainties of SPR estimation can induce range uncertainties of up to 3.5% [ 4 , 5 ], which in current clinical practice are considered by adjusting the corresponding distal and proximal margins to the target.…”

Section: Introductionmentioning

confidence: 99%

Optimum size of a calibration phantom for x-ray CT to convert the Hounsfield units to stopping power ratios in charged particle therapy treatment planning

Inaniwa¹,

Tashima²,

Kanematsu³

2017

Journal of Radiation Research

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In charged-particle therapy treatment planning, the volumetric distribution of stopping power ratios (SPRs) of body tissues relative to water is used for patient dose calculation. The distribution is conventionally obtained from computed tomography (CT) images of a patient using predetermined conversion functions from the CT numbers to the SPRs. One of the biggest uncertainty sources of patient SPR estimation is insufficient correction of beam hardening arising from the mismatch between the size of the patient cross section and the calibration phantom for producing the conversion functions. The uncertainty would be minimized by selecting a suitable size for the cylindrical water calibration phantom, referred to as an ‘effective size’ of the patient cross section, Leffective. We investigated the Leffective for pelvis, abdomen, thorax, and head and neck regions by simulating an ideal CT system using volumetric models of the reference male and female phantoms. The Leffective values were 23.3, 20.3, 22.7 and 18.8 cm for the pelvis, abdomen, thorax, and head and neck regions, respectively, and the Leffective for whole body was 21.0 cm. Using the conversion function for a 21.0-cm-diameter cylindrical water phantom, we could reduce the root mean square deviation of the SPRs and their mean deviation to ≤0.011 and ≤0.001, respectively, in the whole body. Accordingly, for simplicity, the effective size of 21.0 cm can be used for the whole body, irrespective of body-part regions for treatment planning in clinical practice.

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“…Additionally, if improvements in CT# accuracy could be made the CBCT images could also be used to calculate the daily delivered doses. Unlike in CT where a calibration phantom is commonly used to map the measured CT# to the electron density of the tissues, in the native CBCT images this process is prohibited due to the resulting artifacts that are present. Improvement of the sources of artifacts in cone‐beam CT has been an area of active research within the past few years, with dedicated frameworks being researched to correct for: shading artifact, scatter, and system blur .…”

Section: Introductionmentioning

confidence: 99%

Cone‐Beam CT image contrast and attenuation‐map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

Hashemi

Huynh

Sahgal

et al. 2018

J Applied Clin Med Phys

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A Contrast and Attenuation‐map Linearity Improvement (CALI) framework is proposed for cone‐beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is tailored to improve soft tissue contrast of a new point‐of‐care image‐guided SRS system that employs a challenging half cone beam geometry, but can be readily reproduced on any CBCT platform. CALI includes a pre‐ and post‐processing step. In pre‐processing we apply a shading and beam hardening artifact correction to the projections, and in post‐processing step we correct the dome/capping artifact on reconstructed images caused by the spatial variations in X‐ray energy generated by the bowtie‐filter. The shading reduction together with the beam hardening and dome artifact correction algorithms aim to improve the linearity and accuracy of the CT‐numbers (CT#). The CALI framework was evaluated using CatPhan to quantify linearity, contrast‐to‐noise (CNR), and CT# accuracy, as well as subjectively on patient images acquired on a clinical system. Linearity of the reconstructed attenuation‐map was improved from 0.80 to 0.95. The CT# mean absolute measurement error was reduced from 76.1 to 26.9 HU. The CNR of the acrylic insert in the sensitometry module was improved from 1.8 to 7.8. The resulting clinical brain images showed substantial improvements in soft tissue contrast visibility, revealing structures such as ventricles which were otherwise undetectable in the original clinical images obtained from the system. The proposed reconstruction framework also improved CT# accuracy compared to the original images acquired on the system. For frameless image‐guided SRS, improving soft tissue visibility can facilitate evaluation of MR to CBCT co‐registration. Moreover, more accurate CT# may enable the use of CBCT for daily dose delivery measurements.

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Modeling of body tissues for Monte Carlo simulation of radiotherapy treatments planned with conventional x-ray CT systems

Cited by 38 publications

References 28 publications

Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system

Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system

Optimum size of a calibration phantom for x-ray CT to convert the Hounsfield units to stopping power ratios in charged particle therapy treatment planning

Cone‐Beam CT image contrast and attenuation‐map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

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