Radiological Physics and Technology最新文献

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 : 2024-12-01 Epub 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":"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":" ","pages":"929-937"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","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

Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. 评估基于加速器的硼中子俘获疗法商业治疗计划系统的计算精度和计算时间。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-08-14 DOI: 10.1007/s12194-024-00833-7

Akihiko Takeuchi, Katsumi Hirose, Ryohei Kato, Shinya Komori, Mariko Sato, Tomoaki Motoyanagi, Yuhei Yamazaki, Yuki Narita, Yoshihiro Takai, Takahiro Kato

{"title":"Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy.","authors":"Akihiko Takeuchi, Katsumi Hirose, Ryohei Kato, Shinya Komori, Mariko Sato, Tomoaki Motoyanagi, Yuhei Yamazaki, Yuki Narita, Yoshihiro Takai, Takahiro Kato","doi":"10.1007/s12194-024-00833-7","DOIUrl":"10.1007/s12194-024-00833-7","url":null,"abstract":"This study aims to evaluate the feasibility of using a commercially available boron neutron capture therapy (BNCT) dose calculation program (NeuCure® Dose Engine) in terms of calculation accuracy and computation time. Treatment planning was simulated under the following calculation parameters: 1.5-5.0 mm grid sizes and 1-10% statistical uncertainties. The calculated monitor units (MUs) and computation times were evaluated. The MUs calculated on grid sizes larger than 2 mm were overestimated by 2% compared with the result of 1.5 mm grid. We established the two-step method for the routine administration of BNCT: multiple calculations involved in beam optimization should be done at a 5 mm grid and a 10% statistical uncertainty (the shortest computation time: 10.3 ± 2.1 min) in the first-step, and final dose calculations should be performed at a 2 mm grid and a 10% statistical uncertainty (satisfied clinical accuracy: 6.9 ± 0.3 h) in the second-step.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"907-917"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976920","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

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. 深度学习重建对使用计算机断层扫描评估胰腺囊性病变的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-08-15 DOI: 10.1007/s12194-024-00834-6

Jun Kanzawa, Koichiro Yasaka, Yuji Ohizumi, Yuichi Morita, Mariko Kurokawa, Osamu Abe

{"title":"Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.","authors":"Jun Kanzawa, Koichiro Yasaka, Yuji Ohizumi, Yuichi Morita, Mariko Kurokawa, Osamu Abe","doi":"10.1007/s12194-024-00834-6","DOIUrl":"10.1007/s12194-024-00834-6","url":null,"abstract":"This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"827-833"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989183","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

LIT-Unet: a lightweight and effective model for medical image segmentation. LIT-Unet：用于医学图像分割的轻量级有效模型。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-09-20 DOI: 10.1007/s12194-024-00844-4

Ru Wang, Qiqi Kou, Lina Dou

{"title":"LIT-Unet: a lightweight and effective model for medical image segmentation.","authors":"Ru Wang, Qiqi Kou, Lina Dou","doi":"10.1007/s12194-024-00844-4","DOIUrl":"10.1007/s12194-024-00844-4","url":null,"abstract":"This study aimed to design a simple and efficient automatic segmentation model for medical images, so as to facilitate doctors to make more accurate diagnosis and treatment plan. A hybrid lightweight network LIT-Unet with symmetric encoder-decoder U-shaped architecture is proposed. Synapse multi-organ segmentation dataset and automated cardiac diagnosis challenge (ACDC) dataset were used to test the segmentation performance of the method. Two indexes, Dice similarity coefficient (DSC ↑) and 95% Hausdorff distance (HD95 ↓), were used to evaluate and compare the segmentation ability with the current advanced methods. Ablation experiments were conducted to demonstrate the lightweight nature and effectiveness of our model. For Synapse dataset, our model achieves a higher DSC score (80.40%), an improvement of 3.8% over the typical hybrid model (TransUnet). The 95 HD value is low at 20.67%. For ACDC dataset, LIT-Unet achieves the optimal average DSC (%) of 91.84 compared with other networks listed. Compared to patch expanding, the DSC of our model is intuitively improved by 1.62% with the help of deformable token merging (DTM). These results show that the proposed hierarchical LIT-Unet can achieve significant accuracy and is expected to provide a reliable basis for clinical diagnosis and treatment.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"878-887"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298279","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

The effect on gastrointestinal peristalsis for magnetic resonance cholangiopancreatography during breath-holding methods. 憋气法对磁共振胰胆管造影胃肠道蠕动的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-09-27 DOI: 10.1007/s12194-024-00846-2

Yuhei Otsuka, Tomoya Nakamura, Nao Kajihara, Takao Tashiro

{"title":"The effect on gastrointestinal peristalsis for magnetic resonance cholangiopancreatography during breath-holding methods.","authors":"Yuhei Otsuka, Tomoya Nakamura, Nao Kajihara, Takao Tashiro","doi":"10.1007/s12194-024-00846-2","DOIUrl":"10.1007/s12194-024-00846-2","url":null,"abstract":"The breath-hold (BH) 3D magnetic resonance cholangiopancreatography method has been reported to suppress \"respiratory artifacts\"; however, the influence of gastrointestinal peristalsis around the target organs has not been discussed. In contrast, the autonomic nervous system has been reported to affect gastrointestinal peristalsis and BH imaging has been reported to influence venous blood flow signal (BFS) through its involvement with the autonomic nervous system. We examined the impact of BH imaging on gastrointestinal peristalsis. Seven healthy volunteers participated. Three respiratory patterns-free breathing (FB), BH at maximum inspiration (Insp-BH), and BH at maximum expiration (Exp-BH)-were used. Gastrointestinal peristalsis was measured using cine MRI. Cine MRI data were analyzed using the normalized interframe difference method, focusing on the duodenum and gastric body. Hemodynamic changes resulting from BH methods were evaluated using 2D phase contrast, targeting the inferior vena cava (IVC). The BFS was examined for all phases of each respiratory pattern. Peristalsis variation in the duodenum showed no significant differences among FB, Exp-BH, and Insp-BH. In the gastric body, no significant differences were observed between FB and Exp-BH or between Exp-BH and Insp-BH. However, a significant difference emerged between FB and Insp-BH. Regarding BFS, in the IVC, significant differences were observed between Exp-BH and Insp-BH and between FB and Insp-BH (both, p < 0.01), with no significant difference between FB and Exp-BH. Insp-BH reduces venous blood flow and suppresses the influence of peristalsis variation.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"888-895"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356119","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 : 2024-12-01 Epub 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":"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":" ","pages":"834-842"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","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

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 : 2024-12-01 Epub 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 : 2024-12-01 Epub 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":"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":" ","pages":"869-877"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","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

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis. 基于放射组学的人工智能（AI）方法在乳腺癌筛查和诊断中的最新概述。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-09-16 DOI: 10.1007/s12194-024-00842-6

Reza Elahi, Mahdis Nazari

{"title":"An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.","authors":"Reza Elahi, Mahdis Nazari","doi":"10.1007/s12194-024-00842-6","DOIUrl":"10.1007/s12194-024-00842-6","url":null,"abstract":"Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"795-818"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298276","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

Effect of frame rate on image quality in cardiology evaluated using an indirect conversion dynamic flat-panel detector. 使用间接转换动态平板探测器评估帧频对心脏病学图像质量的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-09-18 DOI: 10.1007/s12194-024-00845-3

Akira Hasegawa, Yohan Kondo

引用次数: 0