Radiological Physics and Technology最新文献_第8页

Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom. 利用冰水模型评估深度学习重建对扩散加权成像质量和表观扩散系数的影响。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-28 DOI: 10.1007/s12194-023-00765-8

Tatsuya Hayashi, Shinya Kojima, Toshimune Ito, Norio Hayashi, Hiroshi Kondo, Asako Yamamoto, Hiroshi Oba

{"title":"Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.","authors":"Tatsuya Hayashi, Shinya Kojima, Toshimune Ito, Norio Hayashi, Hiroshi Kondo, Asako Yamamoto, Hiroshi Oba","doi":"10.1007/s12194-023-00765-8","DOIUrl":"10.1007/s12194-023-00765-8","url":null,"abstract":"This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10-3 mm2/s at 0 °C) was imaged at various b-values (0, 1000, 2000, and 4000 s/mm2) using a 3 T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0 mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000 s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000 s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5 mm and combined b-values of 0 and 4000 s/mm2, the ADC values were 0.97 × 10-3and 0.98 × 10-3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"186-194"},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049528","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

Validating computer applications for calculating spatial resolution and noise property in CT using simulated images with known properties. 利用已知属性的模拟图像验证计算 CT 空间分辨率和噪声属性的计算机应用程序。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-01-10 DOI: 10.1007/s12194-023-00771-w

Takeshi Inoue, Katsuhiro Ichikawa, Takanori Hara, Kazuya Ohashi, Kazuhiro Sato, Hiroki Kawashima

{"title":"Validating computer applications for calculating spatial resolution and noise property in CT using simulated images with known properties.","authors":"Takeshi Inoue, Katsuhiro Ichikawa, Takanori Hara, Kazuya Ohashi, Kazuhiro Sato, Hiroki Kawashima","doi":"10.1007/s12194-023-00771-w","DOIUrl":"10.1007/s12194-023-00771-w","url":null,"abstract":"The purpose of this study was to evaluate, using simulated images with known property values, how accurately some computer applications for calculating modulation transfer function (MTF), task transfer function (TTF), or noise power spectrum (NPS) in computed tomography (CT) based on widely known techniques produce their results. Specifically, they were three applications applicable to the wire method for MTF calculation, two applications corresponding to the circular edge (CE) and linear edge (LE) methods for TTF, and one application using a two-dimensional Fourier transform for NPS, which are collectively integrated with the software 'CTmeasure' provided by the Japanese Society of CT Technology. Images for the calculation with radial symmetry were generated based on a roll-off type filter function. The accuracy of each application was evaluated by comparing the calculated property with the true one. The calculated MTFs for the wire method accurately matched the true ones with percentage errors of smaller than 1.0%. In contrast, the CE and LE methods presented relatively large errors of up to 50% at high frequencies, whereas the NPS's errors were up to 30%. A closer investigation revealed, however, that these errors were attributable not to the applications but to the insufficiencies in the measurement techniques commonly employed. By improving the measurement conditions to minimize the effects of the insufficiencies, the errors notably decreased, whichvalidated the calculation techniques in the applications we used.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"238-247"},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139404735","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

Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network. 使用深度卷积神经网络使用新指数对CT图像的分辨率特性进行基于任务的评估。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-06 DOI: 10.1007/s12194-023-00751-0

Aiko Hayashi, Ryohei Fukui, Shogo Kamioka, Kazushi Yokomachi, Chikako Fujioka, Eiji Nishimaru, Masao Kiguchi, Junji Shiraishi

{"title":"Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network.","authors":"Aiko Hayashi, Ryohei Fukui, Shogo Kamioka, Kazushi Yokomachi, Chikako Fujioka, Eiji Nishimaru, Masao Kiguchi, Junji Shiraishi","doi":"10.1007/s12194-023-00751-0","DOIUrl":"10.1007/s12194-023-00751-0","url":null,"abstract":"In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"83-92"},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71487242","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

Directional vector visualization of scattered rays in mobile c-arm fluoroscopy. 移动式 C 臂透视中散射射线的定向矢量可视化。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-02-05 DOI: 10.1007/s12194-024-00779-w

Kyoko Hizukuri, Toshioh Fujibuchi, Hiroyuki Arakawa

{"title":"Directional vector visualization of scattered rays in mobile c-arm fluoroscopy.","authors":"Kyoko Hizukuri, Toshioh Fujibuchi, Hiroyuki Arakawa","doi":"10.1007/s12194-024-00779-w","DOIUrl":"10.1007/s12194-024-00779-w","url":null,"abstract":"Previous radiation protection-measure studies for medical staff who perform X-ray fluoroscopy have employed simulations to investigate the use of protective plates and their shielding effectiveness. Incorporating directional information enables users to gain a clearer understanding of how to position protective plates effectively. Therefore, in this study, we propose the visualization of the directional vectors of scattered rays. X-ray fluoroscopy was performed; the particle and heavy-ion transport code system was used in Monte Carlo simulations to reproduce the behavior of scattered rays in an X-ray room by reproducing a C-arm X-ray fluoroscopy system. Using the calculated results of the scattered-ray behavior, the vectors of photons scattered from the phantom were visualized in three dimensions. A model of the physician was placed on the directional vectors and dose distribution maps to confirm the direction of the scattered rays toward the physician when the protective plate was in place. Simulation accuracy was confirmed by measuring the ambient dose equivalent and comparing the measured and calculated values (agreed within 10%). The directional vectors of the scattered rays radiated outward from the phantom, confirming a large amount of backscatter radiation. The use of a protective plate between the patient and the physician's head part increased the shielding effect, thereby enhancing radiation protection for the physicians compared to cases without the protective plate. The use of directional vectors and the surrounding dose-equivalent distribution of this method can elucidate the appropriate use of radiation protection plates.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"288-296"},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693201","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

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":" ","pages":"124-134"},"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

Half-value layer measurements using solid-state detectors and single-rotation technique with lead apertures in spiral computed tomography with and without a tin filter. 在螺旋计算机断层扫描中使用固态探测器和带有铅孔的单旋转技术测量半值层，带锡滤波器和不带锡滤波器。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-21 DOI: 10.1007/s12194-023-00767-6

Atsushi Fukuda, Nao Ichikawa, Takuma Hayashi, Ayaka Hirosawa, Kosuke Matsubara

{"title":"Half-value layer measurements using solid-state detectors and single-rotation technique with lead apertures in spiral computed tomography with and without a tin filter.","authors":"Atsushi Fukuda, Nao Ichikawa, Takuma Hayashi, Ayaka Hirosawa, Kosuke Matsubara","doi":"10.1007/s12194-023-00767-6","DOIUrl":"10.1007/s12194-023-00767-6","url":null,"abstract":"Solid-state detectors (SSDs) may be used along with a lead collimator for half-value layer (HVL) measurement using computed tomography (CT) with or without a tin filter. We aimed to compare HVL measurements obtained using three SSDs (AGMS-DM+ , X2 R/F sensor, and Black Piranha) with those obtained using the single-rotation technique with lead apertures (SRTLA). HVL measurements were performed using spiral CT at tube voltages of 70-140 kV without a tin filter and 100-140 kV (Sn 100-140 kV) with a tin filter in increments of 10 kV. For SRTLA, a 0.6-cc ionization chamber was suspended at the isocenter to measure the free-in-air kerma rate ( <math> <msub><mover><mi>K</mi> <mo>˙</mo></mover> <mtext>air</mtext></msub> </math> ) values. Five apertures were made on the gantry cover using lead sheets, and four aluminum plates were placed on these apertures. HVLs in SRTLA were obtained from <math> <msub><mover><mi>K</mi> <mo>˙</mo></mover> <mtext>air</mtext></msub> </math> decline curves. Subsequently, SSDs inserted into the lead collimator were placed on the gantry cover and used to measure HVLs. Maximum HVL differences of AGMS-DM+ , X2 R/F sensor, and Black Piranha with respect to SRTLA without/with a tin filter were - 0.09/0.6 (only two Sn 100-110 kV) mm, - 0.50/ - 0.6 mm, and - 0.17/(no data available) mm, respectively. These values were within the specification limit. SSDs inserted into the lead collimator could be used to measure HVL using spiral CT without a tin filter. HVLs could be measured with a tin filter using only the X2 R/F sensor, and further improvement of its calibration accuracy with respect to other SSDs is warranted.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"207-218"},"PeriodicalIF":1.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138832210","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

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":" ","pages":"219-229"},"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

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":" ","pages":"195-206"},"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":" ","pages":"230-237"},"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