Journal of X-Ray Science and Technology最新文献

{"title":"Proximal femur segmentation and quantification in dual-energy subtraction tomosynthesis: A novel approach to fracture risk assessment.","authors":"Akari Matsushima, Tai-Been Chen, Koharu Kimura, Mizuki Sato, Shih-Yen Hsu, Takahide Okamoto","doi":"10.1177/08953996241312594","DOIUrl":"10.1177/08953996241312594","url":null,"abstract":"<p><p>BackgroundOsteoporosis is a major public health concern, especially among older adults, due to its association with an increased risk of fractures, particularly in the proximal femur. These fractures severely impact mobility and quality of life, leading to significant economic and health burdens.ObjectiveThis study aims to enhance bone density assessment in the proximal femur by addressing the limitations of conventional dual-energy X-ray absorptiometry through the integration of tomosynthesis with dual-energy applications and advanced segmentation models.Methods and MaterialsThe imaging capability of a radiography/fluoroscopy system with dual-energy subtraction was evaluated. Two phantoms were included in this study: a tomosynthesis phantom (PH-56) was used to measure the quality of the tomosynthesis images, and a torso phantom (PH-4) was used to obtain proximal femur images. Quantification of bone images was achieved by optimizing the energy subtraction (ene-sub) and scale factors to isolate bone pixel values while nullifying soft tissue pixel values. Both the faster region-based convolutional neural network (Faster R-CNN) and U-Net were used to segment the proximal femoral region. The performance of these models was then evaluated using the intersection-over-union (IoU) metric with a torso phantom to ensure controlled conditions.ResultsThe optimal ene-sub-factor ranged between 1.19 and 1.20, and a scale factor of around 0.1 was found to be suitable for detailed bone image observation. Regarding segmentation performance, a VGG19-based Faster R-CNN model achieved the highest mean IoU, outperforming the U-Net model (0.865 vs. 0.515, respectively).ConclusionsThese findings suggest that the integration of tomosynthesis with dual-energy applications significantly enhances the accuracy of bone density measurements in the proximal femur, and that the Faster R-CNN model provides superior segmentation performance, thereby offering a promising tool for bone density and osteoporosis management. Future research should focus on refining these models and validating their clinical applicability to improve patient outcomes.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"405-419"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DR-ConvNeXt: DR classification method for reconstructing ConvNeXt model structure.","authors":"Pengfei Song, Yun Wu","doi":"10.1177/08953996241311190","DOIUrl":"10.1177/08953996241311190","url":null,"abstract":"<p><p>BackgroundDiabetic retinopathy (DR) is a major complication of diabetes and a leading cause of blindness among the working-age population. However, the complex distribution and variability of lesion characteristics within the dataset present significant challenges for achieving high-precision classification of DR images.ObjectiveWe propose an automatic classification method for DR images, named DR-ConvNeXt, which aims to achieve accurate diagnosis of lesion types.MethodsThe method involves designing a dual-branch addition convolution structure and appropriately increasing the number of stacked ConvNeXt Block convolution layers. Additionally, a unique primary-auxiliary loss function is introduced, contributing to a significant enhancement in DR classification accuracy within the DR-ConvNeXt model.ResultsThe model achieved an accuracy of 91.8%,sensitivity of 81.6%, and specificity of 97.9% on the APTOS dataset. On the Messidor-2 dataset, the model achieved an accuracy of 83.6%, sensitivity of 74.0%, and specificity of 94.6%.ConclusionsThe DR-ConvNeXt model's classification results on the two publicly available datasets illustrate the significant advantages in all evaluation indexes for DR classification.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"448-460"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel detail-enhanced wavelet domain feature compensation network for sparse-view X-ray computed laminography.","authors":"Yawu Long, Qianglong Zhong, Jin Lu, Chengke Xiong","doi":"10.1177/08953996251319183","DOIUrl":"10.1177/08953996251319183","url":null,"abstract":"<p><p>BackgroundX-ray Computed Laminography (CL) is a popular industrial tool for non-destructive visualization of flat objects. However, high-quality CL imaging requires a large number of projections, resulting in a long imaging time. Reducing the number of projections allows acceleration of the imaging process, but decreases the quality of reconstructed images.ObjectiveOur objective is to build a deep learning network for sparse-view CL reconstruction.MethodsConsidering complementarities of feature extraction in different domains, we design an encoder-decoder network that enables to compensate the missing information during spatial domain feature extraction in wavelet domain. Also, a detail-enhanced module is developed to highlight details. Additionally, Swin Transformer and convolution operators are combined to better capture features.ResultsA total of 3200 pairs of 16-view and 1024-view CL images (2880 pairs for training, 160 pairs for validation, and 160 pairs for testing) of solder joints have been employed to investigate the performance of the proposed network. It is observed that the proposed network obtains the highest image quality with PSNR and SSIM of 37.875 ± 0.908 dB, 0.992 ± 0.002, respectively. Also, it achieves competitive results on the AAPM dataset.ConclusionsThis study demonstrates the effectiveness and generalization of the proposed network for sparse-view CL reconstruction.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"488-498"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on ring artifact reduction method for CT images of nuclear graphite components.","authors":"Tianchen Zeng, Jintao Fu, Peng Cong, Ximing Liu, Guangduo Xu, Yuewen Sun","doi":"10.1177/08953996241308760","DOIUrl":"10.1177/08953996241308760","url":null,"abstract":"<p><p>BackgroundThe supporting structure of high-temperature gas-cooled reactors (HTGR) comprises over 3000 carbon/graphite components, necessitating computed tomography (CT) non-destructive testing before operational deployment as per reactor technical specifications. However, CT images are frequently marred by severe ring artifacts due to the response non-uniformity and non-linearity of detector units, which diminishes the ability to detect defects effectively.MethodsTo address this issue, we propose a physics-based ring artifacts reduction method for CT that employs pixel response correction. This method physically accounts for the cause of ring artifacts and leverages the prior knowledge of the detected object to enhance the accuracy of the detection process.ResultsOur proposed method achieved a notable reduction in ring artifacts, as evidenced by a 37.7% decrease in ring total variation (RTV) values compared to the originals, significantly enhancing image quality. It also surpassed traditional and machine learning methods in artifact reduction while maintaining image details. The lower RTV scores confirm our method's superior effectiveness in minimizing ring artifacts.ConclusionWe believe that our research contributes to the enhancement of defect inspection performance in detection systems, which is crucial for ensuring the safety of reactors. The proposed method's effectiveness in mitigating ring artifacts while maintaining image quality highlights its potential impact on the reliability of non-destructive testing in the context of HTGR components.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"317-324"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.","authors":"Heng Wang, Tiejun Yang, Jiacheng Fan, Huiyao Zhang, Wenjie Zhang, Mingzhu Ji, Jianyu Miao","doi":"10.1177/08953996241300023","DOIUrl":"10.1177/08953996241300023","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive impairment (MCI) is significant. Structural magnetic resonance imaging (sMRI) is widely used to present structural changes in the subject's brain tissue. The relatively mild structural changes in the brain with MCI have led to ongoing challenges in the task of conversion prediction in MCI. Moreover, many multimodal AD diagnostic models proposed in recent years ignore the potential relationship between multimodal information.</p><p><strong>Objective: </strong>To solve these problems, we propose a multimodal fine-grained classification model based on deep metric learning for AD diagnosis (DML-MFCM), which can fully exploit the fine-grained feature information of sMRI and learn the potential relationships between multimodal feature information.</p><p><strong>Methods: </strong>First, we propose a fine-grained feature extraction module that can effectively capture the fine-grained feature information of the lesion area. Then, we introduce a multimodal cross-attention module to learn the potential relationships between multimodal data. In addition, we design a hybrid loss function based on deep metric learning. It can guide the model to learn the feature representation method between samples, which improves the model's performance in disease diagnosis.</p><p><strong>Results: </strong>We have extensively evaluated the proposed models on the ADNI and AIBL datasets. The ACC of AD vs. NC, MCI vs. NC, and sMCI vs. pMCI tasks in the ADNI dataset are 98.75%, 95.88%, and 88.00%, respectively. The ACC on the AD vs. NC and MCI vs. NC tasks in the AIBL dataset are 94.33% and 91.67%.</p><p><strong>Conclusions: </strong>The results demonstrate that our method has excellent performance in AD diagnosis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"211-228"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative elemental sensitive imaging based on K-edge subtraction tomography.","authors":"Yichi Zhang, Fen Tao, Ruoyang Gao, Ling Zhang, Jun Wang, Guohao Du, Tiqiao Xiao, Biao Deng","doi":"10.1177/08953996241290323","DOIUrl":"10.1177/08953996241290323","url":null,"abstract":"<p><strong>Background: </strong>K-edge subtraction (KES) tomography has been extensively utilized in the field of elemental sensitive imaging due to its high spatial resolution, rapid acquisition, and three-dimensional (3D) imaging capabilities. However, previous studies have primarily focused on the qualitative analysis of element contents, rather than quantitative assessment.</p><p><strong>Objective: </strong>The current study proposes a novel method for quantitative elemental analysis based on K-edge subtraction tomography.</p><p><strong>Methods: </strong>The linear correlation between the slice grayscale of standard samples and the difference in their linear absorption coefficients is confirmed. This finding suggests that the grayscale data from slices may be employed to perform quantitative estimations of elemental compositions.</p><p><strong>Results: </strong>In order to verify the accuracy and validity of this method, the target element contents in standard and actual samples are quantitatively analyzed, respectively. The results demonstrate that the method is capable of achieving nanometer-resolved quantitative elemental sensitive imaging with a relative error of less than 3% in the target elemental content.</p><p><strong>Conclusions: </strong>The method described in this paper is expected to expand the scope of applications for K-edge subtraction tomography and provide a novel approach to achieve more precise and convenient quantitative elemental analysis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"37-46"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT image super-resolution under the guidance of deep gradient information.","authors":"Ye Shen, Ningning Liang, Xinyi Zhong, Junru Ren, Zhizhong Zheng, Lei Li, Bin Yan","doi":"10.1177/08953996241289225","DOIUrl":"10.1177/08953996241289225","url":null,"abstract":"<p><p>Due to the hardware constraints of Computed Tomography (CT) imaging, acquiring high-resolution (HR) CT images in clinical settings poses a significant challenge. In recent years, convolutional neural networks have shown great potential in CT super-resolution (SR) problems. However, the reconstruction results of many deep learning-based SR methods have structural distortion and detail ambiguity. In this paper, a new SR network based on generative adversarial learning is proposed. The network consists of gradient branch and SR branch. Gradient branch is used to recover HR gradient maps. The network merges gradient image features of the gradient branch into the SR branch, offering gradient information guidance for super-resolution (SR) reconstruction. Further, the loss function of the network combines the image space loss function with the gradient loss and the gradient variance loss to further generate a more realistic detail texture. Compared to other comparison algorithms, the structural similarity index of the SR results obtained by the proposed method on simulation and experimental data has increased by 1.8% and 1.4%, respectively. The experimental results demonstrate that the proposed CT SR network exhibits superior performance in terms of structure preservation and detail restoration.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"58-71"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks.","authors":"Dhamodharan Srinivasan, Ajmeera Kiran, S Parameswari, Jeevanantham Vellaichamy","doi":"10.1177/08953996241289314","DOIUrl":"10.1177/08953996241289314","url":null,"abstract":"<p><strong>Background: </strong>Osteoporosis (OP) is a condition that significantly decreases bone density and strength, often remaining undetected until the occurrence of a fracture. Timely identification of OP is essential for preventing fractures, reducing morbidity, and enhancing the quality of life.</p><p><strong>Objective: </strong>This study aims to improve the accuracy, speed, and reliability of early-stage osteoporosis detection by integrating the compact architecture of Cascaded ShuffleNet with the pattern recognition prowess of Artificial Neural Networks (ANNs).</p><p><strong>Methods: </strong>BoneVoyage leverages the efficiency of ShuffleNet and the analytical capabilities of ANNs to extract and analyze complex features from bone density scans. The framework was trained and validated on a comprehensive dataset containing thousands of bone density images, ensuring robustness across diverse cases.</p><p><strong>Results: </strong>This model achieving an accuracy of 97.2%, with high sensitivity and specificity. These results significantly surpass those of existing OP detection methods, highlighting the effectiveness of the BoneVoyage framework in identifying subtle changes in bone density indicative of early-stage osteoporosis.</p><p><strong>Conclusions: </strong>BoneVoyage represents a significant advancement in the early detection of osteoporosis, offering a reliable tool for healthcare providers to identify at-risk individuals prematurely. The early detection facilitated by BoneVoyage allows for the implementation of preventive measures and targeted treatments.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"3-25"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spine X-ray image segmentation based on deep learning and marker controlled watershed.","authors":"Yating Xiao, Yan Chen, Yong Zhang, Runjie Zhang, Guangyu Cui, Yufeng Song, Quan Zhang","doi":"10.1177/08953996241299998","DOIUrl":"10.1177/08953996241299998","url":null,"abstract":"<p><strong>Background: </strong>The development of automatic methods for vertebral segmentation provides the objective analysis of each vertebra in the spine image, which is important for the diagnosis of various spinal diseases. However, vertebrae have inter-class similarity and intra-class variability, and some adjacent vertebrae exhibit adhesion.</p><p><strong>Objective: </strong>To solve the adhesion problem of adjacent vertebrae and ensure that the boundary between adjacent vertebrae can be accurately demarcated, we propose an image segmentation method based on deep learning and marker controlled watershed.</p><p><strong>Methods: </strong>This method consists of a dual-path model of localization path and segmentation path to achieve automatic vertebral segmentation. For the vertebral localization path, a high-resolution network (HRNet) is used to locate vertebral center. Moreover, based on spine posture, a new bone direction loss (BD-Loss) is designed to constrain HRNet. For the vertebral segmentation path, we proposed a VU-Net network to achieve vertebral preliminary segmentation. Additionally, a position information perception module (PIPM) is introduced to realize the guidance of HRNet to VU-Net. Finally, we novelly use the outputs of HR-Net and VU-Net deep learning networks to initialize the marker controlled watershed algorithm to suppress the adhesion of adjacent vertebrae and achieve vertebral fine segmentation.</p><p><strong>Results: </strong>The proposed method was evaluated on two spine X-ray datasets using four metrics. The first dataset contains sagittal images of the cervical spine, while the second dataset contains coronal images of the whole spine, both with different health conditions. Our method achieved Recall of 96.82% and 94.38%, Precision of 97.24% and 98.14%, Dice coefficient of 97.03% and 96.22%, Intersection over Union of 94.24% and 92.72% on the cervical spine and whole spine datasets respectively, outperforming current state-of-the-art techniques.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"109-119"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The usefulness of X-ray output management in general radiography systems using exposure index.","authors":"Kazuhiro Ogasawara, Shinya Ohwada, Rie Tachibana, Katsuhiko Ogasawara","doi":"10.1177/08953996241299994","DOIUrl":"10.1177/08953996241299994","url":null,"abstract":"<p><strong>Purpose: </strong>The periodic quality control of X-ray devices is important for obtaining optical medical images and determining the appropriate X-ray exposure dose. Additionally, the measurement of the X-ray output is constrained by time, technical aspects, and expenses. Therefore, we investigated the usefulness of a simple method for managing X-ray output using an Exposure Index (EI).</p><p><strong>Methods: </strong>The entire surface of the flat panel detector was X-ray-irradiated every Friday at the time of end-of-work inspection under the condition that the recorded EI was approximately 1000. The EI and exposure dose were measured, and the linearity and accuracy were evaluated.</p><p><strong>Results: </strong>The output gradually decreased from the start of the measurements in Room 1 and stabilized after the output was adjusted. The relationship between exposure dose and EI showed high linearity, with R² > 0.99, and the CV of EI was less than 2.41%, indicating high reproducibility.</p><p><strong>Conclusions: </strong>We demonstrated that the results of constancy tests can be easily quantified using EI. The EI method can manage the X-ray output with good reproducibility.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"204-210"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}