TH-C-T-6E-10: The Impact of Calculation Grid Size On the Accuracy of IMRT Dose Distribution

2005 ◽  
Vol 32 (6Part21) ◽  
pp. 2168-2168
Author(s):  
H Chung ◽  
H Jin ◽  
C Liu ◽  
J Palta ◽  
T Suh ◽  
...  
Keyword(s):  
Dose Distribution ◽  
Grid Size ◽  
The Impact ◽  
Calculation Grid

scholarly journals Study on the ability of the three-dose-volume-histogram-gamma (3DVH-γ) analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

2020 ◽  
Author(s):  
Han Bai ◽  
Sijin Zhu ◽  
Xingrao Wu ◽  
Xuhong Liu ◽  
Feihu Chen ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cervical Cancer ◽  
Dose Distribution ◽  
Measurement Data ◽  
Dose Calculation ◽  
Grid Size ◽  
Dose Effect ◽  
P Value ◽  
Data Set ◽  
Calculation Grid

Abstract Objective : To explore the efficacy and sensitivity of 3DVH-γanalysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size(DCGS). Methods： 17 patients’ plans for cervical cancer were enrolled（Pinnacle TPS，VMAT）, and the DCGS was changed from 2.0mm to 5.0mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0mm as the “ reference ” data set (RDS) , the dose distribution calculated by the rest DCGS as the“measurement”data set (MDS), the 3DVH-γ passing rates and the (N)TCPs of the all structures under different DCGS were obtained , and then analyze the ability of 3DVH-γ analysis and (N)TCP model in detecting dose changes and what factors affect this ability. Results: The effect of DCGS on planned dose was obvious. When the γ-standard was 1.0mm, 1.0% and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3DVH-γ analysis ( p s<0.05). With the decline of the standard, 3DVH-γ analysis’ ability to detect this difference shows weaker. When the standard was 1.0mm, 3.0% and 10.0%, the p value of >0.05 accounted for the majority. With DCGS=2.0mm being RDS, ∆γ-passing-rate presented the same trend with ∆(N)TCPs of all structures except for the femurs only when the 1.0mm, 1.0% and 10.0% standards were adopted for the 3DVH-γ analysis. Conclusions: The 3DVH-γ analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former’s detection ability has a lot to do with the designed standard, and the latter’s capability is related to the parameters and calculated accuracy instrinsically.

scholarly journals Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

2020 ◽  
Author(s):  
Han Bai ◽  
Sijin Zhu ◽  
Xingrao Wu ◽  
Xuhong Liu ◽  
Feihu Chen ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cervical Cancer ◽  
Dose Distribution ◽  
Measurement Data ◽  
Dose Calculation ◽  
Grid Size ◽  
Dose Effect ◽  
Data Set ◽  
Gamma Analysis ◽  
Calculation Grid

Abstract Objective: To explore the efficacy and sensitivity of 3D gamma analysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size (DCGS).Methods：17 patients’ plans for cervical cancer were enrolled (Pinnacle TPS，VMAT), and the DCGS was changed from 2.0mm to 5.0mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0mm as the “reference” data set (RDS), the dose distribution calculated by the rest DCGS as the“measurement”data set (MDS), the 3D gamma passing rates and the (N)TCPs of the all structures under different DCGS were obtained, and then analyze the ability of 3D gamma analysis and (N)TCP model in detecting dose changes and what factors affect this ability.Results: The effect of DCGS on planned dose was obvious. When the gamma standard was 1.0mm, 1.0% and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3D gamma analysis (all p value < 0.05). With the decline of the standard, 3D gamma analysis’ ability to detect this difference shows weaker. When the standard was 1.0mm, 3.0% and 10.0%, the p value of > 0.05 accounted for the majority. With DCGS=2.0mm being RDS, ∆gamma-passing-rate presented the same trend with ∆(N)TCPs of all structures except for the femurs only when the 1.0mm, 1.0% and 10.0% standards were adopted for the 3D gamma analysis.Conclusions: The 3D gamma analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former’s detection ability has a lot to do with the designed standard, and the latter’s capability is related to the parameters and calculated accuracy instrinsically.

Experimental evaluation of the impact of low tesla transverse magnetic field on dose distribution in presence of tissue interfaces

2018 ◽  
Vol 53 ◽  
pp. 80-85 ◽  
Author(s):  
Davide Cusumano ◽  
Stefania Teodoli ◽  
Francesca Greco ◽  
Andrea Fidanzio ◽  
Luca Boldrini ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Transverse Magnetic Field ◽  
Dose Distribution ◽  
Experimental Evaluation ◽  
Transverse Magnetic ◽  
The Impact ◽  
Tissue Interfaces

Modeling the dose distribution in a human brain structure using CT images on the surface of Mars

2020 ◽  
Author(s):  
Salman Khaksarighiri ◽  
Jingnan Guo ◽  
Robert Wimmer-Schweingruber ◽  
Lennart Rostl
Keyword(s):  
Radiation Dose ◽  
Human Brain ◽  
Brain Structure ◽  
Dose Distribution ◽  
Cosmic Radiation ◽  
Cosmic Ray ◽  
Ct Images ◽  
Human Brains ◽  
The Impact ◽  
The Brain

&lt;p&gt;One of the most important steps in the near-future space age will be a manned mission to Mars. Unfortunately, such a mission will cause astronauts to be exposed to unavoidable cosmic radiation in deep space and on the surface of Mars. Thus a better understanding of the radiation environment for a Mars mission and the consequent biological impacts on humans, in particular the human brains, is critical. To investigate the impact of cosmic radiation on human brains and the potential influence on the brain functions, we model and study the cosmic particle-induced radiation dose in a realistic head structure. Specifically speaking, 134 slices of computed tomography (CT) images of an actual human head have been used as a 3D phantom in Geant4 (GEometry ANd Tracking) which is a Monte Carlo tool simulating energetic particles impinging into different parts of the brain and deliver radiation dose therein. As a first step, we compare the influence of different brain structures (e.g., with or without bones, with or without soft tissues) to the resulting dose therein to demonstrate the necessity of using a realistic brain structure for our investigation. Afterwards, we calculate energy-dependent functions of dose distribution for the most important (most abundant and most biologically-relevant) particle types encountered in space and on Mars such as protons, Helium ions and neutrons. These functions are then used to fold with Galactic Cosmic Ray (GCR) spectra on the surface of Mars for obtaining the dose rate distribution at different lobes of the human brain. Different GCR spectra during various solar cycle conditions have also been studied and compared.&lt;/p&gt;

scholarly journals A Probability-Based Investigation on the Setup Robustness of Pencil-beam Proton Radiation Therapy for Skull-Base Meningioma

2021 ◽  
Vol 7 (3) ◽  
pp. 34-45
Author(s):  
Wei Zou ◽  
Goldie Kurtz ◽  
Mayisha Nakib ◽  
Brendan Burgdorf ◽  
Murat Alp ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Skull Base ◽  
Dose Distribution ◽  
Hot Spot ◽  
Published Data ◽  
Pencil Beam ◽  
Planning Techniques ◽  
Setup Errors ◽  
Skull Base Meningioma ◽  
The Impact

Abstract Introduction The intracranial skull-base meningioma is in proximity to multiple critical organs and heterogeneous tissues. Steep dose gradients often result from avoiding critical organs in proton treatment plans. Dose uncertainties arising from setup errors under image-guided radiation therapy are worthy of evaluation. Patients and Methods Fourteen patients with skull-base meningioma were retrospectively identified and planned with proton pencil beam scanning (PBS) single-field uniform dose (SFUD) and multifield optimization (MFO) techniques. The setup uncertainties were assigned a probability model on the basis of prior published data. The impact on the dose distribution from nominal 1-mm and large, less probable setup errors, as well as the cumulative effect, was analyzed. The robustness of SFUD and MFO planning techniques in these scenarios was discussed. Results The target coverage was reduced and the plan dose hot spot increased by all setup uncertainty scenarios regardless of the planning techniques. For 1 mm nominal shifts, the deviations in clinical target volume (CTV) coverage D99% was −11 ± 52 cGy and −45 ± 147 cGy for SFUD and MFO plans. The setup uncertainties affected the organ at risk (OAR) dose both positively and negatively. The statistical average of the setup uncertainties had &lt;100 cGy impact on the plan qualities for all patients. The cumulative deviations in CTV D95% were 1 ± 34 cGy and −7 ± 18 cGy for SFUD and MFO plans. Conclusion It is important to understand the impact of setup uncertainties on skull-base meningioma, as the tumor target has complex shape and is in proximity to multiple critical organs. Our work evaluated the setup uncertainty based on its probability distribution and evaluated the dosimetric consequences. In general, the SFUD plans demonstrated more robustness than the MFO plans in target coverages and brainstem dose. The probability-weighted overall effect on the dose distribution is small compared to the dosimetric shift during single fraction.

scholarly journals Potential Defects and Improvements of Equivalent Uniform Dose Prediction Model Based on the Analysis of Radiation-Induced Brain Injury

2022 ◽  
Vol 11 ◽  
Author(s):  
Qing-Hua Du ◽  
Jian Li ◽  
Yi-Xiu Gan ◽  
Hui-Jun Zhu ◽  
Hai-Ying Yue ◽  
...  
Keyword(s):  
Brain Injury ◽  
Dose Distribution ◽  
Predictive Ability ◽  
Volume Effect ◽  
Equivalent Uniform Dose ◽  
Critical Dose ◽  
Dose Volume ◽  
Radiation Induced ◽  
Effect Parameter ◽  
The Impact

PurposeTo study the impact of dose distribution on volume-effect parameter and predictive ability of equivalent uniform dose (EUD) model, and to explore the improvements.Methods and MaterialsThe brains of 103 nasopharyngeal carcinoma patients treated with IMRT were segmented according to dose distribution (brain and left/right half-brain for similar distributions but different sizes; VD with different D for different distributions). Predictive ability of EUDVD (EUD of VD) for radiation-induced brain injury was assessed by receiver operating characteristics curve (ROC) and area under the curve (AUC). The optimal volume-effect parameter a of EUD was selected when AUC was maximal (mAUC). Correlations between mAUC, a and D were analyzed by Pearson correlation analysis. Both mAUC and a in brain and half-brain were compared by using paired samples t-tests. The optimal DV and VD points were selected for a simple comparison.ResultsThe mAUC of brain/half-brain EUD was 0.819/0.821 and the optimal a value was 21.5/22. When D increased, mAUC of EUDVD increased, while a decreased. The mAUC reached the maximum value when D was 50–55 Gy, and a was always 1 when D ≥55 Gy. The difference of mAUC/a between brain and half-brain was not significant. If a was in range of 1 to 22, AUC of brain/half-brain EUDV55 Gy (0.857–0.830/0.845–0.830) was always larger than that of brain/half-brain EUD (0.681–0.819/0.691–0.821). The AUCs of optimal dose/volume points were 0.801 (brain D2.5 cc), 0.823 (brain V70 Gy), 0.818 (half-brain D1 cc), and 0.827 (half-brain V69 Gy), respectively. Mean dose (equal to EUDVD with a = 1) of high-dose volume (V50 Gy–V60 Gy) was superior to traditional EUD and dose/volume points.ConclusionVolume-effect parameter of EUD is variable and related to dose distribution. EUD with large low-dose volume may not be better than simple dose/volume points. Critical-dose-volume EUD could improve the predictive ability and has an invariant volume-effect parameter. Mean dose may be the case in which critical-dose-volume EUD has the best predictive ability.

scholarly journals Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 230
Author(s):  
Paweł Gilewski
Keyword(s):  
Hydrological Cycle ◽  
Interpolation Method ◽  
Grid Size ◽  
Rainfall Runoff ◽  
Degree Polynomial ◽  
Mountainous Catchment ◽  
Runoff Modeling ◽  
Simulation Results ◽  
The Impact ◽  
Inverse Distance

Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of interpolation is particularly challenging over mountainous areas due to complex orography and a usually sparse network of rain gauges. This paper investigates two deterministic interpolation methods (inverse distance weighting (IDW), and first-degree polynomial) and their impact on the outputs of semi-distributed rainfall-runoff modeling in a mountainous catchment. The performed analysis considers the aspect of interpolation grid size, which is often neglected in other than fully-distributed modeling. The impact of the inverse distance power (IDP) value in the IDW interpolation was also analyzed. It has been found that the best simulation results were obtained using a grid size smaller or equal to 750 m and the first-degree polynomial as an interpolation method. The results indicate that the IDP value in the IDW method has more impact on the simulation results than the grid size. Evaluation of the results was done using the Kling-Gupta efficiency (KGE), which is considered to be an alternative to the Nash-Sutcliffe efficiency (NSE). It was found that KGE generally tends to provide higher and less varied values than NSE which makes it less useful for the evaluation of the results.

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