Tuan Anh Le, Hoang Ngoc Tran, Serena Fattori, Viet Cuong Phan, Sebastien Incerti
The differences in H2O2 production between conventional (CONV) and ultra-high dose rate (UHDR) irradiations in water radiolysis are still not fully understood. The lower levels of this radiolytic species, as a critical end product of water radiolysis, are particularly relevant for investigating the connection between the high-density energy deposition during short-duration physical events (ionizations or excitations) and biological responses of the FLASH effect. In this study, we developed a new Geant4-DNA chemistry model to simulate radiolysis considering the time structure of the irradiation pulse at different absorbed doses to liquid water of 0.01, 0.1, 1, and 2 Gy under 1 MeV electron irradiation. The model allows the description of the beam's temporal structure, including the pulse duration, the pulse repetition frequency, and the pulse amplitude for the different beam irradiation conditions through a wide dose rate range, from 0.01 Gy/s up to about 105 Gy/s, at various oxygen concentrations. The preliminary results indicate a correlation between the temporal structure of the pulses and a significant reduction in the production of reactive oxygen species (ROS) at different dose rates.
{"title":"Modeling water radiolysis with Geant4-DNA: Impact of the temporal structure of the irradiation pulse under oxygen conditions","authors":"Tuan Anh Le, Hoang Ngoc Tran, Serena Fattori, Viet Cuong Phan, Sebastien Incerti","doi":"arxiv-2409.11993","DOIUrl":"https://doi.org/arxiv-2409.11993","url":null,"abstract":"The differences in H2O2 production between conventional (CONV) and ultra-high\u0000dose rate (UHDR) irradiations in water radiolysis are still not fully\u0000understood. The lower levels of this radiolytic species, as a critical end\u0000product of water radiolysis, are particularly relevant for investigating the\u0000connection between the high-density energy deposition during short-duration\u0000physical events (ionizations or excitations) and biological responses of the\u0000FLASH effect. In this study, we developed a new Geant4-DNA chemistry model to\u0000simulate radiolysis considering the time structure of the irradiation pulse at\u0000different absorbed doses to liquid water of 0.01, 0.1, 1, and 2 Gy under 1 MeV\u0000electron irradiation. The model allows the description of the beam's temporal\u0000structure, including the pulse duration, the pulse repetition frequency, and\u0000the pulse amplitude for the different beam irradiation conditions through a\u0000wide dose rate range, from 0.01 Gy/s up to about 105 Gy/s, at various oxygen\u0000concentrations. The preliminary results indicate a correlation between the\u0000temporal structure of the pulses and a significant reduction in the production\u0000of reactive oxygen species (ROS) at different dose rates.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viktor Wase, Oscar Widenfalk, Rasmus Nilsson, Claes Fälth, Albin Fredriksson
The advent of ultra-high dose rate irradiation, known as FLASH radiation therapy, has shown promising potential in reducing toxicity while maintaining tumor control. However, the clinical translation of these benefits necessitates efficient treatment planning strategies. This study introduces a novel approach to optimize proton therapy for FLASH effects using traveling salesperson problem (TSP) heuristics. We applied these heuristics to optimize the arrangement of proton spots in treatment plans for 26 prostate cancer patients, comparing the performance against conventional sorting methods and global optimization techniques. Our results demonstrate that TSP-based heuristics significantly enhance FLASH coverage to the same extent as the global optimization technique, but with computation times reduced from hours to a few seconds. This approach offers a practical and scalable solution for enhancing the effectiveness of FLASH therapy, paving the way for more effective and personalized cancer treatments. Future work will focus on further optimizing run times and validating these methods in clinical settings.
{"title":"Fast Spot Order Optimization to Increase Dose Rates in Scanned Particle Therapy FLASH Treatments","authors":"Viktor Wase, Oscar Widenfalk, Rasmus Nilsson, Claes Fälth, Albin Fredriksson","doi":"arxiv-2409.11794","DOIUrl":"https://doi.org/arxiv-2409.11794","url":null,"abstract":"The advent of ultra-high dose rate irradiation, known as FLASH radiation\u0000therapy, has shown promising potential in reducing toxicity while maintaining\u0000tumor control. However, the clinical translation of these benefits necessitates\u0000efficient treatment planning strategies. This study introduces a novel approach\u0000to optimize proton therapy for FLASH effects using traveling salesperson\u0000problem (TSP) heuristics. We applied these heuristics to optimize the\u0000arrangement of proton spots in treatment plans for 26 prostate cancer patients,\u0000comparing the performance against conventional sorting methods and global\u0000optimization techniques. Our results demonstrate that TSP-based heuristics\u0000significantly enhance FLASH coverage to the same extent as the global\u0000optimization technique, but with computation times reduced from hours to a few\u0000seconds. This approach offers a practical and scalable solution for enhancing\u0000the effectiveness of FLASH therapy, paving the way for more effective and\u0000personalized cancer treatments. Future work will focus on further optimizing\u0000run times and validating these methods in clinical settings.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clément JailinLMPS, Antoine BenadyLMPS, Remi LegrouxLMPS, Emmanuel BarangerLMPS
The recent development of Physics-Augmented Neural Networks (PANN) opens new opportunities for modeling material behaviors. These approaches have demonstrated their efficiency when trained on synthetic cases. This study aims to demonstrate the effectiveness of training PANN using real experimental data for modeling hyperelastic behavior. The approach involved two uni-axial experiments equipped with digital image correlation and force sensors. The tests achieved axial deformations exceeding 200% and presented non-linear responses. Twenty loading steps extracted from one experiment were used to train the PANN. The model architecture was optimized based on results from a validation dataset, utilizing equilibrium gap loss computed on six loading steps. Finally, 544 loading steps from the first experiment and 80 steps from a second independent experiment were used for testing purposes. The PANN model effectively captured the hyperelastic behavior across and beyond the training loads, showing superior performance compared to the standard Neo-Hookean model when assessed using various evaluation metrics. Training PANN with experimental mechanical data shows promising results, outperforming traditional modeling approaches.
{"title":"Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network","authors":"Clément JailinLMPS, Antoine BenadyLMPS, Remi LegrouxLMPS, Emmanuel BarangerLMPS","doi":"arxiv-2409.11763","DOIUrl":"https://doi.org/arxiv-2409.11763","url":null,"abstract":"The recent development of Physics-Augmented Neural Networks (PANN) opens new\u0000opportunities for modeling material behaviors. These approaches have\u0000demonstrated their efficiency when trained on synthetic cases. This study aims\u0000to demonstrate the effectiveness of training PANN using real experimental data\u0000for modeling hyperelastic behavior. The approach involved two uni-axial\u0000experiments equipped with digital image correlation and force sensors. The\u0000tests achieved axial deformations exceeding 200% and presented non-linear\u0000responses. Twenty loading steps extracted from one experiment were used to\u0000train the PANN. The model architecture was optimized based on results from a\u0000validation dataset, utilizing equilibrium gap loss computed on six loading\u0000steps. Finally, 544 loading steps from the first experiment and 80 steps from a\u0000second independent experiment were used for testing purposes. The PANN model\u0000effectively captured the hyperelastic behavior across and beyond the training\u0000loads, showing superior performance compared to the standard Neo-Hookean model\u0000when assessed using various evaluation metrics. Training PANN with experimental\u0000mechanical data shows promising results, outperforming traditional modeling\u0000approaches.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norah Ger, Alice Ku, Jasmyn Lopez, N. Robert Bennett, Jia Wang, Grace Ateka, Enoch Anyenda, Matthias Rosezky, Adam S. Wang, Kian Shaker
We present OpenDosimeter (https://opendosimeter.org/), an open hardware solution for real-time personal X-ray dose monitoring based on a scintillation counter. Using an X-ray sensor assembly (LYSO + SiPM) on a custom board powered by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data logging (10 hours), and battery-powered operation. One of the core innovations is that we calibrate the device using $^{241}$Am found in ionization smoke detectors. Specifically, we use the $gamma$-emissions to spectrally calibrate the dosimeter, then calculate the effective dose from X-ray exposure by compensating for the scintillator absorption efficiency and applying energy-to-dose coefficients derived from tabulated data in the ICRP 116 publication. We demonstrate that this transparent approach enables real-time dose rate readings with a linear response between 0.1-1000 $mu$Sv/h at $pm$25% accuracy, tested for energies up to 120 keV. The maximum dose rate readings are limited by pile-up effects when approaching count rate saturation ($sim$77 kcps at $sim$13 $mu$s average pulse processing time). The total component cost for making an OpenDosimeter is <$100, which, combined with its open design (both hardware and software), enables cost-effective local reproducibility on a global scale. This paper complements the open-source documentation by explaining the underlying technology, the algorithm for dose calculation, and areas for future improvement.
我们介绍 OpenDosimeter (https://opendosimeter.org/)，这是一种基于闪烁计数器的开放式个人 X 射线剂量实时监测解决方案。OpenDosimeter 在由 Raspberry Pi Pico 驱动的定制电路板上使用 X 射线传感器组件（LYSO + SiPM），提供实时反馈（1 Hz）、数据记录（10 小时）和电池供电操作。我们的核心创新之一是使用电离烟雾探测器中的 $^{241}$Am 对设备进行校准。具体来说，我们使用伽马射线发射来对剂量计进行光谱校准，然后通过补偿闪烁体的吸收效率并应用根据国际放射防护委员会第 116 号出版物中的表格数据得出的能量-剂量系数来计算 X 射线照射的有效剂量。我们证明了这种透明的方法能够实现实时剂量率读数，其线性响应在 0.1-1000 $m$Sv/h 之间，精度为 $/pm$25%，测试能量高达 120 keV。当接近计数率饱和时，最大剂量率读数受到堆积效应的限制（在平均脉冲处理时间为13秒时，最大剂量率为77 kcps）。制作 OpenDosimeter 的总元件成本小于 100 美元，再加上其开放式设计（包括硬件和软件），可以在全球范围内实现具有成本效益的本地可重复性。本文通过解释底层技术、度量计算算法和未来改进领域，对开源文档进行了补充。
{"title":"OpenDosimeter: Open Hardware Personal X-ray Dosimeter","authors":"Norah Ger, Alice Ku, Jasmyn Lopez, N. Robert Bennett, Jia Wang, Grace Ateka, Enoch Anyenda, Matthias Rosezky, Adam S. Wang, Kian Shaker","doi":"arxiv-2409.09993","DOIUrl":"https://doi.org/arxiv-2409.09993","url":null,"abstract":"We present OpenDosimeter (https://opendosimeter.org/), an open hardware\u0000solution for real-time personal X-ray dose monitoring based on a scintillation\u0000counter. Using an X-ray sensor assembly (LYSO + SiPM) on a custom board powered\u0000by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data\u0000logging (10 hours), and battery-powered operation. One of the core innovations\u0000is that we calibrate the device using $^{241}$Am found in ionization smoke\u0000detectors. Specifically, we use the $gamma$-emissions to spectrally calibrate\u0000the dosimeter, then calculate the effective dose from X-ray exposure by\u0000compensating for the scintillator absorption efficiency and applying\u0000energy-to-dose coefficients derived from tabulated data in the ICRP 116\u0000publication. We demonstrate that this transparent approach enables real-time\u0000dose rate readings with a linear response between 0.1-1000 $mu$Sv/h at\u0000$pm$25% accuracy, tested for energies up to 120 keV. The maximum dose rate\u0000readings are limited by pile-up effects when approaching count rate saturation\u0000($sim$77 kcps at $sim$13 $mu$s average pulse processing time). The total\u0000component cost for making an OpenDosimeter is <$100, which, combined with its\u0000open design (both hardware and software), enables cost-effective local\u0000reproducibility on a global scale. This paper complements the open-source\u0000documentation by explaining the underlying technology, the algorithm for dose\u0000calculation, and areas for future improvement.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pablo Torres-Sánchez, Jorge Lerendegui-Marco, Javier Balibrea-Correa, Victor Babiano-Suárez, Bernardo Gameiro, Ion Ladarescu, Patricia Álvarez-Rodríguez, Jean-Michel Daugas, Ulli Koester, Caterina Michelagnoli, Maria Pedrosa-Rivera, Ignacio Porras, Maria José Ruiz-Magaña, Carmen Ruiz-Ruiz, César Domingo-Pardo
This paper explores the adaptation and application of i-TED Compton imagers for real-time dosimetry in Boron Neutron Capture Therapy (BNCT). The i-TED array, previously utilized in nuclear astrophysics experiments at CERN, is being optimized for detecting and imaging 478 keV gamma-rays, critical for accurate BNCT dosimetry. Detailed Monte Carlo simulations were used to optimize the i-TED detector configuration and enhance its performance in the challenging radiation environment typical of BNCT. Additionally, advanced 3D image reconstruction algorithms, including a combination of back-projection and List-Mode Maximum Likelihood Expectation Maximization (LM-MLEM), are implemented and validated through simulations. Preliminary experimental tests at the Institut Laue-Langevin (ILL) demonstrate the potential of i-TED in a clinical setting, with ongoing experiments focusing on improving imaging capabilities in realistic BNCT conditions.
{"title":"The i-TED Compton Camera Array for real-time boron imaging and determination during treatments in Boron Neutron Capture Therapy","authors":"Pablo Torres-Sánchez, Jorge Lerendegui-Marco, Javier Balibrea-Correa, Victor Babiano-Suárez, Bernardo Gameiro, Ion Ladarescu, Patricia Álvarez-Rodríguez, Jean-Michel Daugas, Ulli Koester, Caterina Michelagnoli, Maria Pedrosa-Rivera, Ignacio Porras, Maria José Ruiz-Magaña, Carmen Ruiz-Ruiz, César Domingo-Pardo","doi":"arxiv-2409.10107","DOIUrl":"https://doi.org/arxiv-2409.10107","url":null,"abstract":"This paper explores the adaptation and application of i-TED Compton imagers\u0000for real-time dosimetry in Boron Neutron Capture Therapy (BNCT). The i-TED\u0000array, previously utilized in nuclear astrophysics experiments at CERN, is\u0000being optimized for detecting and imaging 478 keV gamma-rays, critical for\u0000accurate BNCT dosimetry. Detailed Monte Carlo simulations were used to optimize\u0000the i-TED detector configuration and enhance its performance in the challenging\u0000radiation environment typical of BNCT. Additionally, advanced 3D image\u0000reconstruction algorithms, including a combination of back-projection and\u0000List-Mode Maximum Likelihood Expectation Maximization (LM-MLEM), are\u0000implemented and validated through simulations. Preliminary experimental tests\u0000at the Institut Laue-Langevin (ILL) demonstrate the potential of i-TED in a\u0000clinical setting, with ongoing experiments focusing on improving imaging\u0000capabilities in realistic BNCT conditions.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iymad R. Mansour, Nelson Miksys, Luc Beaulieu, Eric Vigneault, Rowan M. Thomson
Purpose: Demonstrate quantitative characterization of 3D patient-specific absorbed dose distributions using Haralick texture analysis and interpret measures in terms of underlying physics and radiation dosimetry. Methods: Retrospective analysis is performed for 137 patients who underwent permanent implant prostate brachytherapy using two simulation conditions: ``TG186'' (realistic tissues including 0-3.8% intraprostatic calcifications; interseed attenuation) and ``TG43'' (water-model; no interseed attenuation). Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are calculated using the original Haralick formalism, and a modified approach designed to reduce grey-level quantization sensitivity. Trends in textural features are compared to clinical dosimetric measures (D90; minimum absorbed dose to the hottest 90% of a volume) and changes in patient target volume % intraprostatic calcifications by volume (%IC). Results: Both original and modified measures quantify the spatial differences in absorbed dose distributions. Strong correlations between differences in textural measures calculated under TG43 and TG186 conditions and %IC are observed for all measures. For example, differences between measures of contrast and correlation increase and decrease respectively as patients with higher levels of %IC are evaluated, reflecting the large differences across adjacent voxels (higher dose in voxels with calcification) when calculated under TG186 conditions. Conversely, the D90 metric is relatively weakly correlated with textural measures, as it generally does not characterize the spatial distribution of absorbed dose. Conclusion: patient-specific 3D dose distributions may be quantified using Haralick analysis, and trends may be interpreted in terms of fundamental physics.
{"title":"Haralick texture feature analysis for Monte Carlo dose distributions of permanent implant prostate brachytherapy","authors":"Iymad R. Mansour, Nelson Miksys, Luc Beaulieu, Eric Vigneault, Rowan M. Thomson","doi":"arxiv-2409.10324","DOIUrl":"https://doi.org/arxiv-2409.10324","url":null,"abstract":"Purpose: Demonstrate quantitative characterization of 3D patient-specific\u0000absorbed dose distributions using Haralick texture analysis and interpret\u0000measures in terms of underlying physics and radiation dosimetry. Methods:\u0000Retrospective analysis is performed for 137 patients who underwent permanent\u0000implant prostate brachytherapy using two simulation conditions: ``TG186''\u0000(realistic tissues including 0-3.8% intraprostatic calcifications; interseed\u0000attenuation) and ``TG43'' (water-model; no interseed attenuation). Haralick\u0000features (homogeneity, contrast, correlation, local homogeneity, entropy) are\u0000calculated using the original Haralick formalism, and a modified approach\u0000designed to reduce grey-level quantization sensitivity. Trends in textural\u0000features are compared to clinical dosimetric measures (D90; minimum absorbed\u0000dose to the hottest 90% of a volume) and changes in patient target volume %\u0000intraprostatic calcifications by volume (%IC). Results: Both original and\u0000modified measures quantify the spatial differences in absorbed dose\u0000distributions. Strong correlations between differences in textural measures\u0000calculated under TG43 and TG186 conditions and %IC are observed for all\u0000measures. For example, differences between measures of contrast and correlation\u0000increase and decrease respectively as patients with higher levels of %IC are\u0000evaluated, reflecting the large differences across adjacent voxels (higher dose\u0000in voxels with calcification) when calculated under TG186 conditions.\u0000Conversely, the D90 metric is relatively weakly correlated with textural\u0000measures, as it generally does not characterize the spatial distribution of\u0000absorbed dose. Conclusion: patient-specific 3D dose distributions may be\u0000quantified using Haralick analysis, and trends may be interpreted in terms of\u0000fundamental physics.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated segmentation of cancerous lesions in PET/CT scans is a crucial first step in quantitative image analysis. However, training deep learning models for segmentation with high accuracy is particularly challenging due to the variations in lesion size, shape, and radiotracer uptake. These lesions can appear in different parts of the body, often near healthy organs that also exhibit considerable uptake, making the task even more complex. As a result, creating an effective segmentation model for routine PET/CT image analysis is challenging. In this study, we utilized a 3D Residual UNet model and employed the Generalized Dice Focal Loss function to train the model on the AutoPET Challenge 2024 dataset. We conducted a 5-fold cross-validation and used an average ensembling technique using the models from the five folds. In the preliminary test phase for Task-1, the average ensemble achieved a mean Dice Similarity Coefficient (DSC) of 0.6687, mean false negative volume (FNV) of 10.9522 ml and mean false positive volume (FPV) 2.9684 ml. More details about the algorithm can be found on our GitHub repository: https://github.com/ahxmeds/autosegnet2024.git. The training code has been shared via the repository: https://github.com/ahxmeds/autopet2024.git.
{"title":"AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images","authors":"Shadab Ahamed","doi":"arxiv-2409.10151","DOIUrl":"https://doi.org/arxiv-2409.10151","url":null,"abstract":"Automated segmentation of cancerous lesions in PET/CT scans is a crucial\u0000first step in quantitative image analysis. However, training deep learning\u0000models for segmentation with high accuracy is particularly challenging due to\u0000the variations in lesion size, shape, and radiotracer uptake. These lesions can\u0000appear in different parts of the body, often near healthy organs that also\u0000exhibit considerable uptake, making the task even more complex. As a result,\u0000creating an effective segmentation model for routine PET/CT image analysis is\u0000challenging. In this study, we utilized a 3D Residual UNet model and employed\u0000the Generalized Dice Focal Loss function to train the model on the AutoPET\u0000Challenge 2024 dataset. We conducted a 5-fold cross-validation and used an\u0000average ensembling technique using the models from the five folds. In the\u0000preliminary test phase for Task-1, the average ensemble achieved a mean Dice\u0000Similarity Coefficient (DSC) of 0.6687, mean false negative volume (FNV) of\u000010.9522 ml and mean false positive volume (FPV) 2.9684 ml. More details about\u0000the algorithm can be found on our GitHub repository:\u0000https://github.com/ahxmeds/autosegnet2024.git. The training code has been\u0000shared via the repository: https://github.com/ahxmeds/autopet2024.git.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aditya A BhosaleDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, Komlan PayneDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, Xiaoliang ZhangDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United StatesDepartment of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
This study incorporates electromagnetic simulations to assess the performance of multi-turn solenoid coils for ultra-low field MR imaging with various conductor materials (superconducting material, low-temperature copper, and room-temperature copper) across different human samples (elbow, knee, and brain). At 70 mT, superconducting materials performed significantly better than both room-temperature and low-temperature copper. The high Q-factor of the superconducting material indicates lower energy loss, which is useful for MR imaging. Furthermore, B1+ field efficiency increased significantly with superconducting materials, indicating superior performance. SNR evaluations revealed that materials with higher conductivity significantly improve SNR, which is critical for producing high-quality MR images. These results show that superconducting and low-temperature copper materials can significantly improve MR imaging quality at ultra-low fields, which has important implications for coil design and optimization.
{"title":"Superconducting and low temperature RF Coils for Ultra-Low-Field MRI: A Study on SNR Performance","authors":"Aditya A BhosaleDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, Komlan PayneDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, Xiaoliang ZhangDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United StatesDepartment of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States","doi":"arxiv-2409.09608","DOIUrl":"https://doi.org/arxiv-2409.09608","url":null,"abstract":"This study incorporates electromagnetic simulations to assess the performance\u0000of multi-turn solenoid coils for ultra-low field MR imaging with various\u0000conductor materials (superconducting material, low-temperature copper, and\u0000room-temperature copper) across different human samples (elbow, knee, and\u0000brain). At 70 mT, superconducting materials performed significantly better than\u0000both room-temperature and low-temperature copper. The high Q-factor of the\u0000superconducting material indicates lower energy loss, which is useful for MR\u0000imaging. Furthermore, B1+ field efficiency increased significantly with\u0000superconducting materials, indicating superior performance. SNR evaluations\u0000revealed that materials with higher conductivity significantly improve SNR,\u0000which is critical for producing high-quality MR images. These results show that\u0000superconducting and low-temperature copper materials can significantly improve\u0000MR imaging quality at ultra-low fields, which has important implications for\u0000coil design and optimization.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fada Guan, Dadi Jiang, Xiaochun Wang, Ming Yang, Kiminori Iga, Yuting Li, Lawrence Bronk, Julianna Bronk, Liang Wang, Youming Guo, Narayan Sahoo, David R. Grosshans, Albert C. Koong, Xiaorong R. Zhu, Radhe Mohan
Previously, a synchrotron-based horizontal proton beamline (87.2 MeV) was successfully commissioned to deliver radiation doses in FLASH and conventional dose rate modes to small fields and volumes. In this study, we developed a strategy to increase the effective radiation field size using a custom robotic motion platform to automatically shift the positions of biological samples. The beam was first broadened with a thin tungsten scatterer and shaped by customized brass collimators for irradiating cell/organoid cultures in 96-well plates (a 7-mm-diameter circle) or for irradiating mice (1-cm2 square). Motion patterns of the robotic platform were written in G-code, with 9-mm spot spacing used for the 96-well plates and 10.6-mm spacing for the mice. The accuracy of target positioning was verified with a self-leveling laser system. The dose delivered in the experimental conditions was validated with EBT-XD film attached to the 96-well plate or the back of the mouse. Our film-measured dose profiles matched Monte Carlo calculations well (1D gamma pass rate >95%). The FLASH dose rates were 113.7 Gy/s for cell/organoid irradiation and 191.3 Gy/s for mouse irradiation. These promising results indicate that this robotic platform can be used to effectively increase the field size for preclinical experiments with proton FLASH.
{"title":"Mimicking large spot-scanning radiation fields for proton FLASH preclinical studies with a robotic motion platform","authors":"Fada Guan, Dadi Jiang, Xiaochun Wang, Ming Yang, Kiminori Iga, Yuting Li, Lawrence Bronk, Julianna Bronk, Liang Wang, Youming Guo, Narayan Sahoo, David R. Grosshans, Albert C. Koong, Xiaorong R. Zhu, Radhe Mohan","doi":"arxiv-2409.09518","DOIUrl":"https://doi.org/arxiv-2409.09518","url":null,"abstract":"Previously, a synchrotron-based horizontal proton beamline (87.2 MeV) was\u0000successfully commissioned to deliver radiation doses in FLASH and conventional\u0000dose rate modes to small fields and volumes. In this study, we developed a\u0000strategy to increase the effective radiation field size using a custom robotic\u0000motion platform to automatically shift the positions of biological samples. The\u0000beam was first broadened with a thin tungsten scatterer and shaped by\u0000customized brass collimators for irradiating cell/organoid cultures in 96-well\u0000plates (a 7-mm-diameter circle) or for irradiating mice (1-cm2 square). Motion\u0000patterns of the robotic platform were written in G-code, with 9-mm spot spacing\u0000used for the 96-well plates and 10.6-mm spacing for the mice. The accuracy of\u0000target positioning was verified with a self-leveling laser system. The dose\u0000delivered in the experimental conditions was validated with EBT-XD film\u0000attached to the 96-well plate or the back of the mouse. Our film-measured dose\u0000profiles matched Monte Carlo calculations well (1D gamma pass rate >95%). The\u0000FLASH dose rates were 113.7 Gy/s for cell/organoid irradiation and 191.3 Gy/s\u0000for mouse irradiation. These promising results indicate that this robotic\u0000platform can be used to effectively increase the field size for preclinical\u0000experiments with proton FLASH.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jaime Parra Raad, Daniel Lock, Yi-Yi Liu, Mark Solomon, Laura Peralta, Kirsten Christensen-Jeffries
Super-resolution ultrasound (SRUS) visualises microvasculature beyond the ultrasound diffraction limit (wavelength($lambda$)/2) by localising and tracking spatially isolated microbubble contrast agents. SRUS phantoms typically consist of simple tube structures, where diameter channels below 100 $mu$m are not available. Furthermore, these phantoms are generally fragile and unstable, have limited ground truth validation, and their simple structure limits the evaluation of SRUS algorithms. To aid SRUS development, robust and durable phantoms with known and physiologically relevant microvasculature are needed for repeatable SRUS testing. This work proposes a method to fabricate durable microvascular phantoms that allow optical gauging for SRUS validation. The methodology used a microvasculature negative print embedded in a Polydimethylsiloxane to fabricate a microvascular phantom. Branching microvascular phantoms with variable microvascular density were demonstrated with optically validated vessel diameters down to $sim$ 60 $mu$m ($lambda$/5.8; $lambda$ =$sim$ 350 $mu$m). SRUS imaging was performed and validated with optical measurements. The average SRUS error was 15.61 $mu$m ($lambda$/22) with a standard deviation error of 11.44 $mu$m. The average error decreased to 7.93 $mu$m ($lambda$/44) once the number of localised microbubbles surpassed 1000 per estimated diameter. In addition, the less than 10$%$ variance of acoustic and optical properties and the mechanical toughness of the phantoms measured a year after fabrication demonstrated their long-term durability. This work presents a method to fabricate durable and optically validated complex microvascular phantoms which can be used to quantify SRUS performance and facilitate its further development.
{"title":"Optically-Validated Microvascular Phantom for Super-Resolution Ultrasound Imaging","authors":"Jaime Parra Raad, Daniel Lock, Yi-Yi Liu, Mark Solomon, Laura Peralta, Kirsten Christensen-Jeffries","doi":"arxiv-2409.09031","DOIUrl":"https://doi.org/arxiv-2409.09031","url":null,"abstract":"Super-resolution ultrasound (SRUS) visualises microvasculature beyond the\u0000ultrasound diffraction limit (wavelength($lambda$)/2) by localising and\u0000tracking spatially isolated microbubble contrast agents. SRUS phantoms\u0000typically consist of simple tube structures, where diameter channels below 100\u0000$mu$m are not available. Furthermore, these phantoms are generally fragile and\u0000unstable, have limited ground truth validation, and their simple structure\u0000limits the evaluation of SRUS algorithms. To aid SRUS development, robust and\u0000durable phantoms with known and physiologically relevant microvasculature are\u0000needed for repeatable SRUS testing. This work proposes a method to fabricate\u0000durable microvascular phantoms that allow optical gauging for SRUS validation.\u0000The methodology used a microvasculature negative print embedded in a\u0000Polydimethylsiloxane to fabricate a microvascular phantom. Branching\u0000microvascular phantoms with variable microvascular density were demonstrated\u0000with optically validated vessel diameters down to $sim$ 60 $mu$m\u0000($lambda$/5.8; $lambda$ =$sim$ 350 $mu$m). SRUS imaging was performed and\u0000validated with optical measurements. The average SRUS error was 15.61 $mu$m\u0000($lambda$/22) with a standard deviation error of 11.44 $mu$m. The average\u0000error decreased to 7.93 $mu$m ($lambda$/44) once the number of localised\u0000microbubbles surpassed 1000 per estimated diameter. In addition, the less than\u000010$%$ variance of acoustic and optical properties and the mechanical toughness\u0000of the phantoms measured a year after fabrication demonstrated their long-term\u0000durability. This work presents a method to fabricate durable and optically\u0000validated complex microvascular phantoms which can be used to quantify SRUS\u0000performance and facilitate its further development.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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