Radiological Physics and Technology最新文献_第2页

A novel internal target volume definition based on velocity and time of respiratory target motion for external beam radiotherapy. 基于呼吸靶运动速度和时间的新型外照射放射治疗内靶体积定义。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-13 DOI: 10.1007/s12194-024-00837-3

Masashi Yamanaka, Teiji Nishio, Kohei Iwabuchi, Hironori Nagata

{"title":"A novel internal target volume definition based on velocity and time of respiratory target motion for external beam radiotherapy.","authors":"Masashi Yamanaka, Teiji Nishio, Kohei Iwabuchi, Hironori Nagata","doi":"10.1007/s12194-024-00837-3","DOIUrl":"https://doi.org/10.1007/s12194-024-00837-3","url":null,"abstract":"This study aimed to develop a novel internal target volume (ITV) definition for respiratory motion targets, considering target motion velocity and time. The proposed ITV was evaluated in respiratory-gated radiotherapy. An ITV modified with target motion velocity and time (ITVvt) was defined as an ITV that includes a target motion based on target motion velocity and time. The target motion velocity was calculated using four-dimensional computed tomography (4DCT) images. The ITVvts were created from phantom and clinical 4DCT images. The phantom 4DCT images were acquired using a solid phantom that moved with a sinusoidal waveform (peak-to-peak amplitudes of 10 and 20 mm and cycles of 2-6 s). The clinical 4DCT images were obtained from eight lung cancer cases. In respiratory-gated radiotherapy, the ITVvt was compared with conventional ITVs for beam times of 0.5-2 s within the gating window. The conventional ITV was created by adding a uniform margin as the maximum motion within the gating window. In the phantom images, the maximum volume difference between the ITVvt and conventional ITV was -81.9%. In the clinical images, the maximum volume difference was -53.6%. Shorter respiratory cycles and longer BTs resulted in smaller ITVvt compared with the conventional ITV. Therefore, the proposed ITVvt plan could be used to reduce treatment volumes and doses to normal tissues.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298275","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}

引用次数: 0

Assessing knowledge about medical physics in language-generative AI with large language model: using the medical physicist exam. 利用大型语言模型评估语言生成人工智能中的医学物理知识：使用医学物理学家考试。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-10 DOI: 10.1007/s12194-024-00838-2

Noriyuki Kadoya, Kazuhiro Arai, Shohei Tanaka, Yuto Kimura, Ryota Tozuka, Keisuke Yasui, Naoki Hayashi, Yoshiyuki Katsuta, Haruna Takahashi, Koki Inoue, Keiichi Jingu

{"title":"Assessing knowledge about medical physics in language-generative AI with large language model: using the medical physicist exam.","authors":"Noriyuki Kadoya, Kazuhiro Arai, Shohei Tanaka, Yuto Kimura, Ryota Tozuka, Keisuke Yasui, Naoki Hayashi, Yoshiyuki Katsuta, Haruna Takahashi, Koki Inoue, Keiichi Jingu","doi":"10.1007/s12194-024-00838-2","DOIUrl":"https://doi.org/10.1007/s12194-024-00838-2","url":null,"abstract":"This study aimed to evaluate the performance for answering the Japanese medical physicist examination and providing the benchmark of knowledge about medical physics in language-generative AI with large language model. We used questions from Japan's 2018, 2019, 2020, 2021 and 2022 medical physicist board examinations, which covered various question types, including multiple-choice questions, and mainly focused on general medicine and medical physics. ChatGPT-3.5 and ChatGPT-4.0 (OpenAI) were used. We compared the AI-based answers with the correct ones. The average accuracy rates were 42.2 ± 2.5% (ChatGPT-3.5) and 72.7 ± 2.6% (ChatGPT-4), showing that ChatGPT-4 was more accurate than ChatGPT-3.5 [all categories (except for radiation-related laws and recommendations/medical ethics): p value < 0.05]. Even with the ChatGPT model with higher accuracy, the accuracy rates were less than 60% in two categories; radiation metrology (55.6%), and radiation-related laws and recommendations/medical ethics (40.0%). These data provide the benchmark for knowledge about medical physics in ChatGPT and can be utilized as basic data for the development of various medical physics tools using ChatGPT (e.g., radiation therapy support tools with Japanese input).","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298277","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}

引用次数: 0

Optimum delineation of skin structure for dose calculation with the linear Boltzmann transport equation algorithm in radiotherapy treatment planning. 在放射治疗规划中使用线性玻尔兹曼传输方程算法计算剂量时的皮肤结构最佳划分。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-09 DOI: 10.1007/s12194-024-00840-8

Keisuke Hamada, Toshioh Fujibuchi, Hiroyuki Arakawa

引用次数: 0

Single-point macromolecular proton fraction mapping using a 0.3 T permanent magnet MRI system: phantom and healthy volunteer study. 使用 0.3 T 永磁 MRI 系统进行单点大分子质子分数绘图：模型和健康志愿者研究。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-09 DOI: 10.1007/s12194-024-00843-5

Yasuhiro Fujiwara, Shoma Eitoku, Nobutaka Sakae, Takahisa Izumi, Hiroyuki Kumazoe, Mika Kitajima

{"title":"Single-point macromolecular proton fraction mapping using a 0.3 T permanent magnet MRI system: phantom and healthy volunteer study.","authors":"Yasuhiro Fujiwara, Shoma Eitoku, Nobutaka Sakae, Takahisa Izumi, Hiroyuki Kumazoe, Mika Kitajima","doi":"10.1007/s12194-024-00843-5","DOIUrl":"https://doi.org/10.1007/s12194-024-00843-5","url":null,"abstract":"In a 0.3 T permanent-magnet magnetic resonance imaging (MRI) system, quantifying myelin content is challenging owing to long imaging times and low signal-to-noise ratio. macromolecular proton fraction (MPF) offers a quantitative assessment of myelin in the nervous system. We aimed to demonstrate the practical feasibility of MPF mapping in the brain using a 0.3 T MRI. Both 0.3 T and 3.0 T MRI systems were used. The MPF-mapping protocol used a standard 3D fast spoiled gradient-echo sequence based on the single-point reference method. Proton density, T1, and magnetization transfer-weighted images were obtained from a protein phantom at 0.3 T and 3.0 T to calculate MPF maps. MPF was measured in all phantom sections to assess its relationship to protein concentration. We acquired MPF maps for 16 and 8 healthy individuals at 0.3 T and 3.0 T, respectively, measuring MPF in nine brain tissues. Differences in MPF between 0.3 T and 3.0 T, and between 0.3 T and previously reported MPF at 0.5 T, were investigated. Pearson's correlation coefficient between protein concentration and MPF at 0.3 T and 3.0 T was 0.92 and 0.90, respectively. The 0.3 T MPF of brain tissue strongly correlated with 3.0 T MPF and literature values measured at 0.5 T. The absolute mean differences in MPF between 0.3 T and 0.5 T were 0.42% and 1.70% in white and gray matter, respectively. Single-point MPF mapping using 0.3 T permanent-magnet MRI can effectively assess myelin content in neural tissue.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298280","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}

引用次数: 0

Parameter optimisation for image acquisition and stacking in carbon dioxide digital subtraction angiography. 二氧化碳数字减影血管造影中图像采集和叠加的参数优化。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-09 DOI: 10.1007/s12194-024-00841-7

Kazuya Kakuta, Koichi Chida

{"title":"Parameter optimisation for image acquisition and stacking in carbon dioxide digital subtraction angiography.","authors":"Kazuya Kakuta, Koichi Chida","doi":"10.1007/s12194-024-00841-7","DOIUrl":"https://doi.org/10.1007/s12194-024-00841-7","url":null,"abstract":"The aim of this study was to optimise the vessel angle as well as the stack number from the profiles of carbon dioxide digital subtraction angiography (CO2-DSA) images of a water phantom containing an artificial vessel tilted at different angles which imitate arteries in the body. The artificial vessel was tilted at 0°, 15°, and 30° relative to the horizontal axis with its centre as the pivot point, and CO2-DSA images were acquired at each vessel tilt angle. The maximum opacity method was used to stack up to four images of the next frame one by one. The signal-to-noise ratio (SNR) was determined from the profile curves. The Wilcoxon rank sum test was used to evaluate whether the profile curve and SNR differed depending on the vessel tilt angle or stack number, and a p-value of less than 0.05 was considered statistically significant. Images acquired at 0° had a significantly lower SNR than images acquired at 15° (p = 0.10). When the vessel angle was 30°, the profile curves were significantly improved (p < 0.05) when two or more images were stacked over the original image. Images with a good SNR were acquired at the vessel tilt angle of 15°, and the shape of the profile curve was improved when two or more images were stacked on the original image. This study demonstrates that the quality of images acquired using CO2-DSA can be significantly improved through parameter optimisation for image acquisition and post-processing.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156304","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}

引用次数: 0

Estimation of the lateral variation of photon beam energy spectra using the percentage depth dose reconstruction method. 利用百分比深度剂量重建法估算光子束能量谱的横向变化。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-06 DOI: 10.1007/s12194-024-00835-5

Puspen Chakraborty, Hidetoshi Saitoh, Yuta Miyake, Tenyoh Suzuki, Weishan Chang

{"title":"Estimation of the lateral variation of photon beam energy spectra using the percentage depth dose reconstruction method.","authors":"Puspen Chakraborty, Hidetoshi Saitoh, Yuta Miyake, Tenyoh Suzuki, Weishan Chang","doi":"10.1007/s12194-024-00835-5","DOIUrl":"https://doi.org/10.1007/s12194-024-00835-5","url":null,"abstract":"In photon-collapsed cone convolution (pCCC) algorithm of the Monaco treatment planning system (TPS), the central-axis energy spectrum is assumed constant throughout the entire irradiation area. To consider lateral variations, an off-axis softening factor is applied to attenuation coefficients during the total energy released per unit mass calculation. We evaluated this method through comparison studies of percentage depth doses (PDDs) and off-axis ratios (OARs) calculated by Monaco and measured for a 6 MV photon beam at various off-axis angles and depths. Significant differences were observed, with relative differences exceeding ± 1%. Therefore, this method may not accurately represent lateral variations of energy spectra. We propose directly implementing energy spectra on both central-axis and off-axis to improve dose calculation accuracy for large field. To this end, we introduce reconstruction of PDDs from monoenergetic depth doses (MDDs) along off-axis angles, thereby estimating energy spectra as functions of radial distance. This method derives energy spectra quickly without significantly increasing the beam modeling time. MDDs were computed through Monte Carlo simulations (DOSRZnrc). The variances between reconstructed and measured PDDs were minimized using the generalized-reduced-gradient method to optimize energy spectra. Reconstructed PDDs along off-axis angles of 0°, 1.15°, 2.29°, 3.43°, 4.57°, 5.71°, 6.84°, 7.97°, 9.09°, 10.2° to estimate energy spectra at radial distances of 0-18 cm in 2 cm increments and OARs calculated using estimated energy spectra at 5, 10, and 20 cm depths, well agreed with measurement (relative differences within ± 0.5%). In conclusion, our proposed method accurately estimates lateral energy spectrum variation, thereby improving dose calculation accuracy of pCCC algorithm.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141310","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}

引用次数: 0

Joint segmentation of sternocleidomastoid and skeletal muscles in computed tomography images using a multiclass learning approach. 利用多类学习方法联合分割计算机断层扫描图像中的胸锁乳突肌和骨骼肌

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-06 DOI: 10.1007/s12194-024-00839-1

Kosuke Ashino, Naoki Kamiya, Xiangrong Zhou, Hiroki Kato, Takeshi Hara, Hiroshi Fujita

{"title":"Joint segmentation of sternocleidomastoid and skeletal muscles in computed tomography images using a multiclass learning approach.","authors":"Kosuke Ashino, Naoki Kamiya, Xiangrong Zhou, Hiroki Kato, Takeshi Hara, Hiroshi Fujita","doi":"10.1007/s12194-024-00839-1","DOIUrl":"https://doi.org/10.1007/s12194-024-00839-1","url":null,"abstract":"Deep-learning-based methods can improve robustness against individual variations in computed tomography (CT) images of the sternocleidomastoid muscle, which is a challenge when using conventional methods based on probabilistic atlases are used for automatic segmentation. Thus, this study proposes a novel multiclass learning approach for the joint segmentation of the sternocleidomastoid and skeletal muscles in CT images, and it employs a two-dimensional U-Net architecture. The proposed method concurrently learns and segmented segments the sternocleidomastoid muscle and the entire skeletal musculature. Consequently, three-dimensional segmentation results are generated for both muscle groups. Experiments conducted on a dataset of 30 body CT images demonstrated segmentation accuracies of 82.94% and 92.73% for the sternocleidomastoid muscle and entire skeletal muscle compartment, respectively. These results outperformed those of conventional methods, such as the single-region learning of a target muscle and multiclass learning of specific muscle pairs. Moreover, the multiclass learning paradigm facilitated a robust segmentation performance regardless of the input image range. This highlights the method's potential for cases that present muscle atrophy or reduced muscle strength. The proposed method exhibits promising capabilities for the high-accuracy joint segmentation of the sternocleidomastoid and skeletal muscles and is effective in recognizing skeletal muscles, thus, it holds promise for integration into computer-aided diagnostic systems for comprehensive musculoskeletal analysis. These findings are expected to enhance medical image analysis techniques and their applications in clinical decision support systems.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146574","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}

引用次数: 0

Analytical parameterization of Bragg curves for proton beams in muscle, bone, and polymethylmethacrylate. 质子束在肌肉、骨骼和聚甲基丙烯酸甲酯中的布拉格曲线分析参数化。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-01 Epub Date: 2024-06-01 DOI: 10.1007/s12194-024-00816-8

Behzad Aminafshar, Hamid Reza Baghani, Ali Asghar Mowlavi

引用次数: 0

Dosimetric effects of small field size, dose grid size, and variable split-arc methods on gamma pass rates in radiation therapy. 小场尺寸、剂量网格尺寸和可变分弧法对放射治疗中伽马通过率的剂量学影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-01 Epub Date: 2024-05-20 DOI: 10.1007/s12194-024-00809-7

Tsunekazu Kuwae, Takuro Ariga, Takeaki Kusada, Akihiro Nishie

{"title":"Dosimetric effects of small field size, dose grid size, and variable split-arc methods on gamma pass rates in radiation therapy.","authors":"Tsunekazu Kuwae, Takuro Ariga, Takeaki Kusada, Akihiro Nishie","doi":"10.1007/s12194-024-00809-7","DOIUrl":"10.1007/s12194-024-00809-7","url":null,"abstract":"This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065569","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}

引用次数: 0

Deep learning-based correction for time truncation in cerebral computed tomography perfusion. 基于深度学习的脑计算机断层扫描灌注时间截断校正。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-09-01 Epub Date: 2024-06-11 DOI: 10.1007/s12194-024-00818-6

Shota Ichikawa, Makoto Ozaki, Hideki Itadani, Hiroyuki Sugimori, Yohan Kondo

{"title":"Deep learning-based correction for time truncation in cerebral computed tomography perfusion.","authors":"Shota Ichikawa, Makoto Ozaki, Hideki Itadani, Hiroyuki Sugimori, Yohan Kondo","doi":"10.1007/s12194-024-00818-6","DOIUrl":"10.1007/s12194-024-00818-6","url":null,"abstract":"Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301853","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}

引用次数: 0