Radiological Physics and Technology最新文献_第9页

Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy. 开发基于深度卷积神经网络过渡学习的个体显示优化系统，用于体生长抑素受体闪烁成像。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-01-02 DOI: 10.1007/s12194-023-00766-7

Shun Matsumoto, Yuki Nakahara, Teppei Yonezawa, Yuto Nakamura, Masahiro Tanabe, Mayumi Higashi, Junji Shiraishi

{"title":"Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.","authors":"Shun Matsumoto, Yuki Nakahara, Teppei Yonezawa, Yuto Nakamura, Masahiro Tanabe, Mayumi Higashi, Junji Shiraishi","doi":"10.1007/s12194-023-00766-7","DOIUrl":"10.1007/s12194-023-00766-7","url":null,"abstract":"Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural network (DCNN) reconstructed by transfer learning of a DCNN constructed using Gallium-67 (67Ga) images. The initial DCNN was constructed using U-Net to optimize the display of 67Ga images (493 cases/986 images), and a DCNN with transposed weight coefficients was reconstructed for the optimization of whole-body SRS images (133 cases/266 images). A DCNN was constructed for each observer using reference display conditions estimated in advance. Furthermore, to eliminate information loss in the original image, a grayscale linear process is performed based on the DCNN output image to obtain the final linearly corrected DCNN (LcDCNN) image. To verify the usefulness of the proposed method, an observer study using a paired-comparison method was conducted on the original, reference, and LcDCNN images of 15 cases with 30 images. The paired comparison method showed that in most cases (29/30), the LcDCNN images were significantly superior to the original images in terms of display conditions. When comparing the LcDCNN and reference images, the number of LcDCNN and reference images that were superior to each other in the display condition was 17 and 13, respectively, and in both cases, 6 of these images showed statistically significant differences. The optimized SRS images obtained using the proposed method, while reflecting the observer's preference, were superior to the conventional manually adjusted images.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080962","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

Verifying institutionally developed hybrid 3D-printed coaxial cylindrical phantom for patient-specific quality assurance in stereotactic body radiation therapy of hepatocellular carcinoma. 验证机构开发的混合三维打印同轴圆柱模型，用于肝细胞癌立体定向体放射治疗的患者特异性质量保证。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-01-03 DOI: 10.1007/s12194-023-00769-4

M P Arun Krishnan, M Ummal Momeen

{"title":"Verifying institutionally developed hybrid 3D-printed coaxial cylindrical phantom for patient-specific quality assurance in stereotactic body radiation therapy of hepatocellular carcinoma.","authors":"M P Arun Krishnan, M Ummal Momeen","doi":"10.1007/s12194-023-00769-4","DOIUrl":"10.1007/s12194-023-00769-4","url":null,"abstract":"An accurate and reliable patient-specific quality assurance (PSQA) is crucial to ensure the safety and precision of Stereotactic body radiation therapy (SBRT) in treating Hepatocellular carcinoma (HCC). This study examines the effectiveness of a novel hybrid 3D-printed hybrid coaxial cylindrical phantom for PSQA in the SBRT of HCC. The study compared three different point dose verification techniques for PSQA: a traditional solid water phantom, two dimensional detector array I'MatriXX, and a newly developed hybrid 3D-printed phantom. Thirty SBRT HCC liver cases were examined using these techniques, and point doses were measured and compared to planned doses using the perpendicular composite method with solid water and I'MatriXX phantoms. Unlike the other two methods, the point dose was compared in true composite geometry using the hybrid 3D-printed phantom, which enhanced the accuracy and consistency of PSQA. The study aims to assess the statistical significance and accuracy of the hybrid 3D-printed phantom compared to other methods. The results showed all techniques complied with the institutional threshold criteria of within ± 3% for point-dose measurement discrepancies. The hybrid 3D-printed phantom was found to have better consistency with a lower standard deviation than traditional methods. Statistical analysis using Student's t-test revealed the statistical significance of the hybrid 3D-printed phantom technique in patient-specific point-dose assessments with a p-value < 0.01. The hybrid 3D-printed phantom developed institutionally is cost-effective and easy to handle. It has been proven to be a valuable tool for PSQA in SBRT for the treatment of HCC and has demonstrated its practicality and reliability.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139088967","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 of a pre-reconstruction process in a filtered back projection reconstruction on an image quality of a low tube voltage computed tomography. 滤波背投重建中的预重建过程对低管电压计算机断层扫描图像质量的影响。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-15 DOI: 10.1007/s12194-023-00764-9

Masaki Takemitsu, Shohei Kudomi, Kazuki Takegami, Takuya Uehara

引用次数: 0

Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning. 利用机器学习为立体定向体放射治疗中的等中心稳定性提供高效质量保证。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-31 DOI: 10.1007/s12194-023-00768-5

Sana Salahuddin, Saeed Ahmad Buzdar, Khalid Iqbal, Muhammad Adeel Azam, Lidia Strigari

{"title":"Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning.","authors":"Sana Salahuddin, Saeed Ahmad Buzdar, Khalid Iqbal, Muhammad Adeel Azam, Lidia Strigari","doi":"10.1007/s12194-023-00768-5","DOIUrl":"10.1007/s12194-023-00768-5","url":null,"abstract":"This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075450","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

Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier. 基于YOLO模型和三维神经网络分类器的CT肺结节分层识别方法。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-18 DOI: 10.1007/s12194-023-00756-9

Yashar Ahmadyar, Alireza Kamali-Asl, Hossein Arabi, Rezvan Samimi, Habib Zaidi

{"title":"Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.","authors":"Yashar Ahmadyar, Alireza Kamali-Asl, Hossein Arabi, Rezvan Samimi, Habib Zaidi","doi":"10.1007/s12194-023-00756-9","DOIUrl":"10.1007/s12194-023-00756-9","url":null,"abstract":"This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048170","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 attenuation correction method in ^99mTc-GSA SPECT/CT hepatic imaging: a phantom study. 基于深度学习的99mTc-GSA SPECT/CT肝脏成像衰减校正方法:一项幻象研究。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-30 DOI: 10.1007/s12194-023-00762-x

Masahiro Miyai, Ryohei Fukui, Masahiro Nakashima, Sachiko Goto

{"title":"Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study.","authors":"Masahiro Miyai, Ryohei Fukui, Masahiro Nakashima, Sachiko Goto","doi":"10.1007/s12194-023-00762-x","DOIUrl":"10.1007/s12194-023-00762-x","url":null,"abstract":"This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463411","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

A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan. CT扫描检测到的不确定肺结节生长的时空图像分析。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-10-27 DOI: 10.1007/s12194-023-00750-1

Takaomi Hanaoka, Hisanori Matoba, Jun Nakayama, Shotaro Ono, Kayoko Ikegawa, Mitsuyo Okada

{"title":"A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.","authors":"Takaomi Hanaoka, Hisanori Matoba, Jun Nakayama, Shotaro Ono, Kayoko Ikegawa, Mitsuyo Okada","doi":"10.1007/s12194-023-00750-1","DOIUrl":"10.1007/s12194-023-00750-1","url":null,"abstract":"The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231549","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 patient radiation dose and risk of cancer from CT examinations. CT检查对患者放射剂量和癌症风险的评估。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-04 DOI: 10.1007/s12194-023-00763-w

Saowapark Poosiri, Anchali Krisanachinda, Kitiwat Khamwan

{"title":"Evaluation of patient radiation dose and risk of cancer from CT examinations.","authors":"Saowapark Poosiri, Anchali Krisanachinda, Kitiwat Khamwan","doi":"10.1007/s12194-023-00763-w","DOIUrl":"10.1007/s12194-023-00763-w","url":null,"abstract":"Computed tomography (CT) examinations have been increasingly requested and become the major sources of patient exposure. The cancer risk from CT scans is contingent upon the amount of absorbed dose of organs. This study aims to determine the organ doses and risk of cancer incidence and mortality from CT examinations at high dose (cumulative effective dose, CED ≥ 100 mSv) in a single day to low dose (CED < 100 mSv) from common CT procedures. Data were gathered from two academic centers of patients aged 15 to 75 years old performed CT examinations during the period of 5 years. CED and organ dose were calculated using Monte Carlo simulation software. Lifetime attributable risk (LAR) was determined following Biological Effects of Ionizing Radiation (BEIR) VII report based on life table and baseline cancer rates of Thai population. At high dose, the highest LAR for breast cancer incidence in young female was 82 per 100,000 exposed patients with breast dose of 148 mGy (CT whole abdomen). The highest LAR for liver cancer incidence in male patient was 72 per 100,000 with liver dose of 133 mGy (multiple CT scans). At low dose, the highest average LAR for breast cancer incidence in young female was 23 per 100,000 while for liver cancer incidence in male patients was 22 per 100,000 (CTA whole aorta). Even though the LAR of cancer incidence and mortality was less than 100 per 100,000, they should not be neglected. The risk of cancer incidence may be increased in later life, particularly in young patients.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478916","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

Assessment of dosimetric approaches in evaluating radiation exposure for interventional cardiologists in Sri Lanka. 评估斯里兰卡介入心脏病学家辐射暴露的剂量测量方法。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-01-19 DOI: 10.1007/s12194-023-00774-7

Sachini Udara Wickramasinghe, Vijitha Ramanathan, Sivananthan Sarasanandarajah

{"title":"Assessment of dosimetric approaches in evaluating radiation exposure for interventional cardiologists in Sri Lanka.","authors":"Sachini Udara Wickramasinghe, Vijitha Ramanathan, Sivananthan Sarasanandarajah","doi":"10.1007/s12194-023-00774-7","DOIUrl":"10.1007/s12194-023-00774-7","url":null,"abstract":"Interventional cardiologists face significant radiation exposure during interventional cardiology procedures. Therefore, this study focuses on assessing radiation exposure among interventional cardiologists during their procedures. Specifically, it aims to determine the effectiveness of both single and double dosimeter methods in estimating annual occupational radiation doses. This research holds pioneering significance as it represents the very first study undertaken in Sri Lanka. Thirteen interventional cardiologists performed 486 interventional cardiology procedures over three months in three different healthcare institutes. Active Hp(10) dosimeters were placed to measure radiation exposure. Effective doses were calculated using single and double dosimetric algorithms. Annual occupational doses were assessed on an operator basis. Statistical analyses were conducted to assess algorithmic differences and dose variations using the Kruskal-Wallis test and linear regression. The highest annual occupational dose for each dosimetric algorithm received as 2.00 ± 0.24 mSv, 2.29 ± 0.48 mSv, 3.35 ± 0.71 mSv, and 2.64 ± 0.42 mSv, respectively, and remained below the recommended safety limit of 20 mSv/year. The Kruskal-Wallis test revealed no significant differences in the effective doses among double dosimetric algorithms, as well as between single and double dosimetric algorithms (p > 0.05). Linear regression showed strong correlations among various algorithms, demonstrating consistency. The findings of this study hold significant effects on interventional cardiology practice in Sri Lanka, enhancing radiation safety and monitoring.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139492524","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

Selection of Radiological Physics and Technology Awards 2023. 评选 2023 年放射物理与技术奖。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-02-03 DOI: 10.1007/s12194-024-00781-2

Nobuyuki Kanematsu, Fujio Araki, Katsuhiro Ichikawa, Tosiaki Miyati, Takeji Sakae, Junji Shiraishi, Yoshikazu Uchiyama, Taiga Yamaya

引用次数: 0