Development of a dose estimation code for BNCT with GPU accelerated Monte Carlo and collapsed cone Convolution method

Lee, Chang-Min; Lee, Hee‐Seock

doi:10.1016/j.net.2021.11.010

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…For example, Lee et al. proposed a GPU‐based MC method to calculate the BNCT dose, which can increase the calculation speed by 56 times 17 . Kotiluoto et al.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Lee et al proposed a GPU-based MC method to calculate the BNCT dose, which can increase the calculation speed by 56 times. 17 Kotiluoto et al developed a deterministic 3D radiation transport code MultiTrans SP 3 , which can be over an order of magnitude faster than stochastic MC codes under the similar resolution. 40 These methods can reduce the 3D dose calculation time of BNCT to tens of minutes, but the 3D dose results can be predicted within a few seconds by the NN method proposed in this work.…”

Section: Discussionmentioning

confidence: 99%

“…Considering the complexity of neutron transport, current BNCT treatment planning systems (BNCT‐TPSs) for example, NCT_Plan, 11 simulation environment for radiotherapy applications (SERA), 12 THORplan, 13 NeuCure system, 14 and JAEA computational dosimetry system, 15 all use the Monte Carlo (MC) method to evaluate the therapeutic dose 16 . Although it can achieve satisfactory three‐dimensional (3D) therapeutic dose calculations, the MC method is often time‐consuming, 17 that is, takes several hours. Recently, the neural network (NN) technology has been applied in various fields of medicine, such as medical image segmentation, automated radiation therapy treatment planning, dose prediction 18,19 .…”

Section: Introductionmentioning

confidence: 99%

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Use of a neural network‐based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma

et al. 2023

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Background: Boron neutron capture therapy (BNCT) is a binary radiotherapy based on the 10 B(n, α) 7 Li capture reaction. Nonradioactive isotope 10 B atoms which selectively concentrated in tumor cells will react with low energy neutrons (mainly thermal neutrons) to produce secondary particles with high linear energy transfer, thus depositing dose in tumor cells. In clinical practice, an appropriate treatment plan needs to be set on the basis of the treatment planning system (TPS). Existing BNCT TPSs usually use the Monte Carlo method to determine the three-dimensional (3D) therapeutic dose distribution, which often requires a lot of calculation time due to the complexity of simulating neutron transportation. Purpose: A neural network-based BNCT dose prediction method is proposed to achieve the rapid and accurate acquisition of BNCT 3D therapeutic dose distribution for patients with glioblastoma to solve the time-consuming problem of BNCT dose calculation in clinic. Methods: The clinical data of 122 patients with glioblastoma are collected.Eighteen patients are used as a test set, and the rest are used as a training set. The 3D-UNET is constructed through the design optimization of input and output data sets based on radiation field information and patient CT information to enable the prediction of 3D dose distribution of BNCT. Results: The average mean absolute error of the predicted and simulated equivalent doses of each organ are all less than 1 Gy. For the dose to 95% of the GTV volume (D 95 ), the relative deviation between predicted and simulated results are all less than 2%. The average 2 mm/2% gamma index is 89.67%, and the average 3 mm/3% gamma index is 96.78%. The calculation takes about 6 h to simulate the 3D therapeutic dose distribution of a patient with glioblastoma by Monte Carlo method using Intel Xeon E5-2699 v4, whereas the time required by the method proposed in this study is almost less than 1 s using a Titan-V graphics card. Conclusions: This study proposes a 3D dose prediction method based on 3D-UNET architecture in BNCT, and the feasibility of this method is demonstrated. Results indicate that the method can remarkably reduce the time required for calculation and ensure the accuracy of the predicted 3D therapeutic dose-effect. This work is expected to promote the clinical development of BNCT in the future.

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“…For example, Lee et al. proposed a GPU‐based MC method to calculate the BNCT dose, which can increase the calculation speed by 56 times 17 . Kotiluoto et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of a neural network‐based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma

et al. 2023

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“…Some attempts have been made to improve the MC algorithms, e.g., by increasing the dose calculation speed of BNCT TPS, such as in GPU-accelerated Monte Carlo [ 57 ]. Although some companies are working on a commercial MC dose engine focused specifically on the clinical application of BNCT, the MC method is often used as an alternative tool to verify specific commercial TPS [ 58 ].…”

Section: Fundamental Aspects Of Bnctmentioning

confidence: 99%

Clinical Viability of Boron Neutron Capture Therapy for Personalized Radiation Treatment

Skwierawska

López-Valverde

Balcerzyk

et al. 2022

Cancers

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Boron Neutron Capture Therapy (BNCT) is a promising binary disease-targeted therapy, as neutrons preferentially kill cells labeled with boron (10B), which makes it a precision medicine treatment modality that provides a therapeutic effect exclusively on patient-specific tumor spread. Contrary to what is usual in radiotherapy, BNCT proposes cell-tailored treatment planning rather than to the tumor mass. The success of BNCT depends mainly on the sufficient spatial biodistribution of 10B located around or within neoplastic cells to produce a high-dose gradient between the tumor and healthy tissue. However, it is not yet possible to precisely determine the concentration of 10B in a specific tissue in real-time using non-invasive methods. Critical issues remain to be resolved if BNCT is to become a valuable, minimally invasive, and efficient treatment. In addition, functional imaging technologies, such as PET, can be applied to determine biological information that can be used for the combined-modality radiotherapy protocol for each specific patient. Regardless, not only imaging methods but also proteomics and gene expression methods will facilitate BNCT becoming a modality of personalized medicine. This work provides an overview of the fundamental principles, recent advances, and future directions of BNCT as cell-targeted cancer therapy for personalized radiation treatment.

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“…From formula (8), it can be seen that when the initial adjustment is made, ] andw are relatively large [12], its purpose is to expand the scope of exercise content and keep the diversity of exercise content as much as possible. In the later adjustment stage, ] andw are relatively small, and the judgment range is continuously reduced to improve the accuracy of the calculation results.…”

Section: Dynamic Adjustment Of Monte Carlo Algorithmmentioning

confidence: 99%

Analysis of the Influence of Sports on College Students’ Music Vocalization Training Based on Monte Carlo and Dynamic Adjustment Factor Algorithm

Hua

2022

Journal of Environmental and Public Health

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With the continuous improvement of people’s living standards, the requirements of music majors for their training standards are also increasing, which leads to the development of music training in the direction of intelligence. This paper discusses the problems of breathing, coordination, and muscle control ability in vocal training and puts forward a vocal training method based on dynamic adjustment factor and Monte Carlo algorithm to solve the difficult problem of vocal training for college students and understand the relationship between vocal training and exercise. Firstly, the sports training set and vocal pronunciation training set are constructed in the form of clustering, and the samples in the set are analyzed discretely to ensure that the samples conform to normal distribution; then, using the Monte Carlo algorithm analyzes the two sample sets and finds out the relationship between exercise and vocal training. Finally, according to breathing, coordination, muscle control ability, and other indicators, calculate the impact of exercise on vocal sound. MATLAB simulation shows that the method proposed in this paper can analyze the influence of exercise on vocal vocalization from the perspective of breathing, coordination, muscle control ability, and other indicators. The accuracy of judgment results is more than 95%, and the time is less than 1 min. All indicators are better than traditional vocal training methods (90%, 2 min), which shows the effectiveness of the method proposed in this paper.

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Development of a dose estimation code for BNCT with GPU accelerated Monte Carlo and collapsed cone Convolution method

Cited by 10 publications

References 22 publications

Use of a neural network‐based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma

Use of a neural network‐based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma

Clinical Viability of Boron Neutron Capture Therapy for Personalized Radiation Treatment

Analysis of the Influence of Sports on College Students’ Music Vocalization Training Based on Monte Carlo and Dynamic Adjustment Factor Algorithm

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