Simulation study on potential accuracy gains from dual energy CT tissue segmentation for low-energy brachytherapy Monte Carlo dose calculations

Department of Radiation Oncology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht 6201 BN, The Netherlands.
Physics in Medicine and Biology (Impact Factor: 2.76). 09/2011; 56(19):6257-78. DOI: 10.1088/0031-9155/56/19/007
Source: PubMed


This work compares Monte Carlo (MC) dose calculations for (125)I and (103)Pd low-dose rate (LDR) brachytherapy sources performed in virtual phantoms containing a series of human soft tissues of interest for brachytherapy. The geometries are segmented (tissue type and density assignment) based on simulated single energy computed tomography (SECT) and dual energy (DECT) images, as well as the all-water TG-43 approach. Accuracy is evaluated by comparison to a reference MC dose calculation performed in the same phantoms, where each voxel's material properties are assigned with exactly known values. The objective is to assess potential dose calculation accuracy gains from DECT. A CT imaging simulation package, ImaSim, is used to generate CT images of calibration and dose calculation phantoms at 80, 120, and 140 kVp. From the high and low energy images electron density ρ(e) and atomic number Z are obtained using a DECT algorithm. Following a correction derived from scans of the calibration phantom, accuracy on Z and ρ(e) of ±1% is obtained for all soft tissues with atomic number Z ∊ [6,8] except lung. GEANT4 MC dose calculations based on DECT segmentation agreed with the reference within ±4% for (103)Pd, the most sensitive source to tissue misassignments. SECT segmentation with three tissue bins as well as the TG-43 approach showed inferior accuracy with errors of up to 20%. Using seven tissue bins in our SECT segmentation brought errors within ±10% for (103)Pd. In general (125)I dose calculations showed higher accuracy than (103)Pd. Simulated image noise was found to decrease DECT accuracy by 3-4%. Our findings suggest that DECT-based segmentation yields improved accuracy when compared to SECT segmentation with seven tissue bins in LDR brachytherapy dose calculation for the specific case of our non-anthropomorphic phantom. The validity of our conclusions for clinical geometry as well as the importance of image noise in the tissue segmentation procedure deserves further experimental investigation.

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Available from: Frank Verhaegen, Nov 25, 2014
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    • "Therefore, in this work we investigate the impact of the recent availability of commercial dual-energy CT (DECT) scanners (Rutherford et al 1976), which has enabled routine extraction of additional information on the effective atomic number Z eff besides electron density ρ e . So far, the impact of DECT in radiation therapy has been explored in terms of improved dosimetric calculation for external photon beam radiotherapy (Bazalova et al 2008) and brachytherapy (Landry et al 2011a), as well as improved CT-based ion range calibration in ion beam therapy (Yang et al 2010). Therefore, this work investigates the possibility of an improved identification of the carbon and oxygen constituents, together with calcium and phosphor which are abundant in bony structures. "
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    ABSTRACT: Dedicated methods of in-vivo verification of ion treatment based on the detection of secondary emitted radiation, such as positron-emission-tomography and prompt gamma detection require high accuracy in the assignment of the elemental composition. This especially concerns the content in carbon and oxygen, which are the most abundant elements of human tissue. The standard single-energy computed tomography (SECT) approach to carbon and oxygen concentration determination has been shown to introduce significant discrepancies in the carbon and oxygen content of tissues. We propose a dual-energy CT (DECT)-based approach for carbon and oxygen content assignment and investigate the accuracy gains of the method. SECT and DECT Hounsfield units (HU) were calculated using the stoichiometric calibration procedure for a comprehensive set of human tissues. Fit parameters for the stoichiometric calibration were obtained from phantom scans. Gaussian distributions with standard deviations equal to those derived from phantom scans were subsequently generated for each tissue for several values of the computed tomography dose index (CTDIvol). The assignment of %weight carbon and oxygen (%wC,%wO) was performed based on SECT and DECT. The SECT scheme employed a HU versus %wC,O approach while for DECT we explored a Zeff versus %wC,O approach and a (Zeff, ρe) space approach. The accuracy of each scheme was estimated by calculating the root mean square (RMS) error on %wC,O derived from the input Gaussian distribution of HU for each tissue and also for the noiseless case as a limiting case. The (Zeff, ρe) space approach was also compared to SECT by comparing RMS error for hydrogen and nitrogen (%wH,%wN). Systematic shifts were applied to the tissue HU distributions to assess the robustness of the method against systematic uncertainties in the stoichiometric calibration procedure. In the absence of noise the (Zeff, ρe) space approach showed more accurate %wC,O assignment (largest error of 2%) than the Zeff versus %wC,O and HU versus %wC,O approaches (largest errors of 15% and 30%, respectively). When noise was present, the accuracy of the (Zeff, ρe) space (DECT approach) was decreased but the RMS error over all tissues was lower than the HU versus %wC,O (SECT approach) (5.8%wC versus 7.5%wC at CTDIvol = 20 mGy). The DECT approach showed decreasing RMS error with decreasing image noise (or increasing CTDIvol). At CTDIvol = 80 mGy the RMS error over all tissues was 3.7% for DECT and 6.2% for SECT approaches. However, systematic shifts greater than ±5HU undermined the accuracy gains afforded by DECT at any dose level. DECT provides more accurate %wC,O assignment than SECT when imaging noise and systematic uncertainties in HU values are not considered. The presence of imaging noise degrades the DECT accuracy on %wC,O assignment but it remains superior to SECT. However, DECT was found to be sensitive to systematic shifts of human tissue HU.
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    • "Calculated dose distributions from simulated CT images segmented into tissue composition and density using a SECT technique compared to a DECT technique proved more accurate using DECT. As our simulations [11] supported the use of DECT imaging for low energy photon dose calculations , validating our findings using a scanner readily available to the clinic is the focus of this manuscript. "
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    ABSTRACT: Dual energy CT (DECT) imaging can provide both the electron density ρ(e) and effective atomic number Z(eff), thus facilitating tissue type identification. This paper investigates the accuracy of a dual source DECT scanner by means of measurements and simulations. Previous simulation work suggested improved Monte Carlo dose calculation accuracy when compared to single energy CT for low energy photon brachytherapy, but lacked validation. As such, we aim to validate our DECT simulation model in this work. A cylindrical phantom containing tissue mimicking inserts was scanned with a second generation dual source scanner (SOMATOM Definition FLASH) to obtain Z(eff) and ρ(e). A model of the scanner was designed in ImaSim, a CT simulation program, and was used to simulate the experiment. Accuracy of measured Z(eff) (labelled Z) was found to vary from -10% to 10% from low to high Z tissue substitutes while the accuracy on ρ(e) from DECT was about 2.5%. Our simulation reproduced the experiments within ±5% for both Z and ρ(e). A clinical DECT scanner was able to extract Z and ρ(e) of tissue substitutes. Our simulation tool replicates the experiments within a reasonable accuracy.
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