Radiological Physics and Technology最新文献_第2页

Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing. 基于散射校正处理的床边胸部x线图像质量评价系统的开发。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-01-11 DOI: 10.1007/s12194-025-00879-1

Kazuya Mori, Toru Negishi

{"title":"Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing.","authors":"Kazuya Mori, Toru Negishi","doi":"10.1007/s12194-025-00879-1","DOIUrl":"https://doi.org/10.1007/s12194-025-00879-1","url":null,"abstract":"In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography. We then evaluated optimal chest phantom VG images as well as those that deviated from the VG setting conditions using BRISQUE. Furthermore, we conducted a subjective evaluation using the mean opinion score (MOS) and established an objective evaluation system for VG images. Finally, the degree of agreement between the MOS subjectively evaluated by 14 radiological technologists and that determined by the objective evaluation system for 100 clinical images obtained by portable chest radiography was calculated using Cohen's kappa coefficient. The correlation coefficient between the BRISQUE score and MOS for chest phantom images was - 0.96 (p < 0.05). The two scores showed a very high linear correlation, indicating the potential of the BRISQUE score as an alternative to MOS. The Cohen's kappa coefficient for the objective evaluation system using the optimal conversion table was 0.42. Conversely, there was a very high detection rate of 82.86% for poor-quality images. An objective evaluation system for bedside chest X-ray images using VG that uses no-reference image quality evaluation helps provide proper image quality. Furthermore, such a system can be constructed with a small amount of training data, which increases the possibility of introducing it to a variety of facilities.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967239","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 gravity effect on liver and spleen volumes using multiposture MRI. 多体位MRI评价重力对肝脾体积的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-01-10 DOI: 10.1007/s12194-024-00870-2

Seiya Nakagawa, Tosiaki Miyati, Naoki Ohno, Yuki Oda, Haruka Kashiwagi, Satoshi Kobayashi

引用次数: 0

IF 1.7

Radiological Physics and Technology Pub Date : 2025-01-07 DOI: 10.1007/s12194-024-00869-9

Kazutaka Hoyoshi, Kazuhiro Sato, Noriyasu Homma, Issei Mori

{"title":"Noise-related inaccuracies in the quantitative evaluation of CT artifacts.","authors":"Kazutaka Hoyoshi, Kazuhiro Sato, Noriyasu Homma, Issei Mori","doi":"10.1007/s12194-024-00869-9","DOIUrl":"https://doi.org/10.1007/s12194-024-00869-9","url":null,"abstract":"Accuracies of measuring the artifact index (AI), a quantitative artifact evaluation index in X-ray CT images, were investigated. The AI is calculated based not only on the standard deviation (SD) of the artifact area in the image, but also on the SD of noise components for considering the noise influence. However, conventional measurement methods may not follow this consideration, for example the non-uniformity of the noise distribution is not taken into account, resulting in reducing the accuracy of AI. To address this problem, this study aims to clarify the impact of noise SD measuring (NSDM) error on AI accuracy and improve the accuracy by reducing the NSDM error. Experimental results demonstrated that the conventional noise measurement methods reduced the accuracy of the AI. Specifically, AI inaccuracy due to the NSDM error is severe in the case of weak artifacts and under high noise conditions. Furthermore, the AI accuracy can be improved by reducing the influence of the NSDM error through image smoothing or by correcting NSDM through noise distribution estimation. These results showed that AI can be affected by NSDM errors practically even though it is robust against noise in principle. The impact of NSDM errors must be avoided for reliable artifact evaluation.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956534","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

Water/fat separate reconstruction for body quantitative susceptibility mapping in MRI. 水/脂肪分离重建在MRI中用于身体定量敏感性制图。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-01-06 DOI: 10.1007/s12194-024-00878-8

Hirohito Kan, Masahiro Nakashima, Takahiro Tsuchiya, Masato Yamada, Akio Hiwatashi

{"title":"Water/fat separate reconstruction for body quantitative susceptibility mapping in MRI.","authors":"Hirohito Kan, Masahiro Nakashima, Takahiro Tsuchiya, Masato Yamada, Akio Hiwatashi","doi":"10.1007/s12194-024-00878-8","DOIUrl":"https://doi.org/10.1007/s12194-024-00878-8","url":null,"abstract":"This study aimed to investigate the cause of susceptibility underestimation in body quantitative susceptibility mapping (QSM) and propose a water/fat separate reconstruction to address this issue. A numerical simulation was conducted using conventional QSM with/without body masking. The conventional method with body masking underestimated the susceptibility across all regions, whereas the method without body masking estimated an equivalent value to the ground truth. Additional numerical simulations and human experiments were conducted to compare the water/fat separate reconstruction, which separately reconstructs water and fat susceptibility maps based on the water/fat separation, with conventional QSM with body masking. The proposed method improved susceptibility estimation specifically in only the water tissue. The results of the human experiments were consistent with those of the numerical simulations. The lack of phase information outside the body contributed to susceptibility underestimation in conventional QSM. The developed method addressed susceptibility underestimation only in water tissue in body QSM.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933066","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

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning. 自我监督学习中基于乳腺组织结构的拼图任务的乳腺癌分类。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-01-06 DOI: 10.1007/s12194-024-00874-y

Keisuke Sugawara, Eichi Takaya, Ryusei Inamori, Yuma Konaka, Jumpei Sato, Yuta Shiratori, Fumihito Hario, Tomoya Kobayashi, Takuya Ueda, Yoshikazu Okamoto

{"title":"Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.","authors":"Keisuke Sugawara, Eichi Takaya, Ryusei Inamori, Yuma Konaka, Jumpei Sato, Yuta Shiratori, Fumihito Hario, Tomoya Kobayashi, Takuya Ueda, Yoshikazu Okamoto","doi":"10.1007/s12194-024-00874-y","DOIUrl":"https://doi.org/10.1007/s12194-024-00874-y","url":null,"abstract":"Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures. However, deep learning models that adopt a diagnostic approach similar to human radiologists are still limited. This study aims to evaluate the effectiveness of the Jigsaw puzzle task in characterizing breast tissue structures for breast cancer classification on mammographic images. Using the Chinese Mammography Database (CMMD), we compared four pre-training pipelines: (1) IN-Jig, pre-trained with both the ImageNet classification task and the Jigsaw puzzle task, (2) Scratch-Jig, pre-trained only with the Jigsaw puzzle task, (3) IN, pre-trained only with the ImageNet classification task, and (4) Scratch, that is trained from random initialization without any pre-training tasks. All pipelines were fine-tuned using binary classification to distinguish between the presence or absence of breast cancer. Performance was evaluated based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Additionally, detailed analysis was conducted for performance across different radiological findings, breast density, and regions of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). The AUC for the four models were 0.925, 0.921, 0.918, 0.909, respectively. Our results suggest the Jigsaw puzzle task is an effective pre-training method for breast cancer classification, with the potential to enhance diagnostic accuracy with limited data.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933063","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

Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms. 基于三维卷积神经网络的全心冠状动脉MRA显著性冠状动脉狭窄计算机分类方法。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-31 DOI: 10.1007/s12194-024-00875-x

Takuma Shiomi, Ryohei Nakayama, Akiyoshi Hizukuri, Masafumi Takafuji, Masaki Ishida, Hajime Sakuma

{"title":"Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.","authors":"Takuma Shiomi, Ryohei Nakayama, Akiyoshi Hizukuri, Masafumi Takafuji, Masaki Ishida, Hajime Sakuma","doi":"10.1007/s12194-024-00875-x","DOIUrl":"https://doi.org/10.1007/s12194-024-00875-x","url":null,"abstract":"This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms. The dataset included 951 segments from WHCMRA images of 75 patients who underwent both WHCMRA and invasive coronary angiography (ICA). Forty-two segments with significant stenosis (luminal diameter reduction <math><mo>≥</mo></math> 75%) on ICA were annotated on WHCMRA images by an experienced radiologist, whereas 909 segments without it were annotated at representative sites. Volumes of interest (VOIs) of 21 × 21 × 21 voxels centered on annotated points were extracted. The network comprises two feature extractors, two attention mechanisms (for the coronary artery and annotated points), and a classifier. The feature extractors first extracted the feature maps from the VOI. The two attention mechanisms weighted the feature maps of the coronary artery and those the neighborhood of the annotated point, respectively. The classifier finally classified the VOIs into those with and without significant coronary artery stenosis. Using fivefold cross-validation, the classification accuracy, sensitivity, specificity, and AUROC (area under the receiver operating characteristic curve) were 0.875, 0.905, 0.873, and 0.944, respectively. The proposed method showed high classification performance for significant coronary artery stenosis and appears to have a substantial impact on the interpretation of WHCMRA images.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910920","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-based quick evaluation method for protective plate effects in X-ray fluoroscopy (DQPEX). 基于方向矢量的x射线透视保护板效果快速评价方法。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-29 DOI: 10.1007/s12194-024-00873-z

Kyoko Hizukuri, Toshioh Fujibuchi, Donghee Han, Hiroyuki Arakawa, Takuya Furuta

{"title":"Directional vector-based quick evaluation method for protective plate effects in X-ray fluoroscopy (DQPEX).","authors":"Kyoko Hizukuri, Toshioh Fujibuchi, Donghee Han, Hiroyuki Arakawa, Takuya Furuta","doi":"10.1007/s12194-024-00873-z","DOIUrl":"https://doi.org/10.1007/s12194-024-00873-z","url":null,"abstract":"One radiation protection measure for medical personnel in X-ray fluoroscopy is using radiation protective plates. A real-time interactive tool visualizing radiation-dose distribution varied with the protective plate position will help greatly to train medical personnel to protect themselves from unnecessary radiation exposure. Monte Carlo simulation can calculate the individual interactions between radiations and objects in the X-ray room, and reproduce the complex dose distribution inside the room. However, Monte Carlo simulation is computationally time-consuming and not suited for real-time feedback. Therefore, we developed a new method to calculate the dose distribution with the presence of protective plates instantly using pre-computed directional vectors, named Directional vector-based Quick evaluation method for Protective plates Effects in X-ray fluoroscopy (DQPEX). DQPEX uses a database of dose distributions and directional vectors precomputed by Monte Carlo code, Particle and Heavy Ion Transport code System (PHITS). Assuming the dose at each position was all contributed from radiations in the direction indicated by the directional vector, the dose reduction by the protective plates at the position was determined whether the backtrace line of the directional vector has a intersect with the protective plate or not. With DQPEX, the whole dose distribution in X-ray room with the presence of a protective plate can be computed about 13 s, which is approximately 1/6000 of the full PHITS simulation. Sufficient accuracy of DQPEX to visualize the effect of a protective plate was confirmed by comparing the obtained dose distribution with those obtained by the full PHITS simulation and measurements.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903810","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 effective dose and risk of exposure-induced cancer death, and diagnostic reference level for CT scans in Tabriz, Iran. 伊朗大不里士CT扫描的有效剂量和暴露致癌死亡风险的估计及诊断参考水平。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-20 DOI: 10.1007/s12194-024-00872-0

Hamed Zamani, Maedeh Yektamanesh, Fatemeh Shiridokht, Soheila Sharifian Jazi, Reza Javadrashid, Amir Ghasemi Jangjoo, Mikaeil Molazadeh, Alireza Farajollahi, Tohid Mortezazadeh

{"title":"Estimation of effective dose and risk of exposure-induced cancer death, and diagnostic reference level for CT scans in Tabriz, Iran.","authors":"Hamed Zamani, Maedeh Yektamanesh, Fatemeh Shiridokht, Soheila Sharifian Jazi, Reza Javadrashid, Amir Ghasemi Jangjoo, Mikaeil Molazadeh, Alireza Farajollahi, Tohid Mortezazadeh","doi":"10.1007/s12194-024-00872-0","DOIUrl":"10.1007/s12194-024-00872-0","url":null,"abstract":"This study aimed to estimate the effective dose and the risk of exposure-induced cancer death (REID), as well as to establish diagnostic reference levels (DRLs) for common CT examinations conducted in Tabriz, Iran. The investigation included adult patients undergoing abdomen-pelvis, brain, neck, sinus, and chest CT scans. Patient data, exposure parameters, and radiation dose metrics, such as volume CT dose index (CTDIvol) and dose length product (DLP), were collected and analyzed. The results showed significant variations in radiation dose across different centers for the CT scans. The average effective doses for the different CT scans were 5.65, 1.08, 1.40, 0.46, and 3.68 mSv for abdomen-pelvis, brain, neck, sinus, and chest scans, respectively. The REID values ranged from 14 per million (for sinus scans) to 196 per million (for abdomen-pelvis scans). Additionally, the DRL values for CTDIvol were 11.03 (for abdomen-pelvis), 59.52 (for brain), 8.33 (for neck), 17.05 (for sinus), and 7.83 mGy (for chest). Our results showed that most of the investigated CT scans had lower effective doses compared to the literature and the REIDs were estimated to be low. Minimizing radiation risk can be achieved by reducing CT exams and keeping doses as low as reasonably achievable. The local DRLs from this study were comparable to previous reports and can serve as benchmarks for setting national and international DRLs, helping healthcare facilities optimize radiation practices and improve patient safety in diagnostic imaging.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865781","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

Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images. 在 T1 加权脑磁共振图像的超分辨率处理中使用多重对比图像进行数据集扩增。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-16 DOI: 10.1007/s12194-024-00871-1

Hajime Kageyama, Nobukiyo Yoshida, Keisuke Kondo, Hiroyuki Akai

{"title":"Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.","authors":"Hajime Kageyama, Nobukiyo Yoshida, Keisuke Kondo, Hiroyuki Akai","doi":"10.1007/s12194-024-00871-1","DOIUrl":"https://doi.org/10.1007/s12194-024-00871-1","url":null,"abstract":"This study investigated the effectiveness of augmenting datasets for super-resolution processing of brain Magnetic Resonance Images (MRI) T1-weighted images (T1WIs) using deep learning. By incorporating images with different contrasts from the same subject, this study sought to improve network performance and assess its impact on image quality metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). This retrospective study included 240 patients who underwent brain MRI. Two types of datasets were created: the Pure-Dataset group comprising T1WIs and the Mixed-Dataset group comprising T1WIs, T2-weighted images, and fluid-attenuated inversion recovery images. A U-Net-based network and an Enhanced Deep Super-Resolution network (EDSR) were trained on these datasets. Objective image quality analysis was performed using PSNR and SSIM. Statistical analyses, including paired t test and Pearson's correlation coefficient, were conducted to evaluate the results. Augmenting datasets with images of different contrasts significantly improved training accuracy as the dataset size increased. PSNR values ranged 29.84-30.26 dB for U-Net trained on mixed datasets, and SSIM values ranged 0.9858-0.9868. Similarly, PSNR values ranged 32.34-32.64 dB for EDSR trained on mixed datasets, and SSIM values ranged 0.9941-0.9945. Significant differences in PSNR and SSIM were observed between models trained on pure and mixed datasets. Pearson's correlation coefficient indicated a strong positive correlation between dataset size and image quality metrics. Using diverse image data obtained from the same subject can improve the performance of deep-learning models in medical image super-resolution tasks.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830376","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

Correction: Visualization of X-ray fields, overlaps, and over-beaming on surface of the head in spiral computed tomography using computer-aided design-based X-ray beam modeling. 更正：使用基于计算机辅助设计的x射线束建模，在螺旋计算机断层扫描中显示头部表面的x射线场、重叠和过度光束。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-12-11 DOI: 10.1007/s12194-024-00863-1

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

引用次数: 0