Journal of X-Ray Science and Technology最新文献

Background: Computed Tomography (CT) technology is commonly used to realize non-destructive testing of Printed Circuit Board (PCB), and element segmentation is the key link in the process. Although the pretraining and finetuning paradigm alleviates the problem of labeling, PCB CT images are easily affected by uneven grayscale and layer penetration. This leads to difficult segmentation of boundaries and affect semantic understanding, resulting in jagged boundaries and even missing elements.

Objective: This paper aims to solve the problem of poor boundary segmentation in PCB CT image element segmentation.

Methods: To this end, we propose PCB CT image element segmentation model based on boundary-attention-guided finetuning. An improved boundary detection algorithm is proposed to enhance boundary sensing ability. In order to achieve non-fixed weight feature fusion, the Attention Feature Fusion module is designed to help boundary features better assist segmentation through attention mechanism.

Results: Experiments show that BAG-FTseg can achieve 89.5% mIoU on our PCB CT dataset, exceeding the baseline model by 0.9%, and the boundary-mIoU reaches 69.5%, 5.3% higher than the baseline model.

Conclusion: This method improves the accuracy of boundary segmentation of PCB elements and the efficiency of feature fusion through attention mechanism, which has practical significance.

Background: The annotation of the regions of interest (ROI) of lumbar vertebrae by radiologists for bone density assessment is a tedious and time-intensive task. However, deep learning (DL) methods for image segmentation has the potential to substitute manual annotations which can significantly improve the efficiency of clinical diagnostics.

Objective: The paper proposes a semi-supervised three-dimensional (3D) segmentation method for the ROI of lumbar vertebrae by integrating the tube masking masked autoencoder (MAE) pre-training.

Methods: The paper proposes a method that modifies the masking strategy of the original MAE pre-training network. And the pre-training network is only trained by images without segmentation labels, when the training is finished, the weights will be saved for segmentation tasks. In downstream tasks, a semi-supervised approach utilizing pseudo-label generation is employed for training. This method leverages a small amount of labeled data to achieve the segmentation of ROI of the lumbar vertebrae.

Results: The experimental results demonstrate that under the condition of limited annotated data, the proposed network improves the dice coefficient by 5-7% and reduces the hausdorff distance by 0.2∼0.6 mm compared to using the UNetr network alone for segmentation. When compared to the conventional MAE, the tube masking MAE presented in this paper assists effectively in segmentation, resulting in a 2% increase in the dice coefficient and a 0.24 mm reduction in the hausdorff distance.

Conclusion: Automatic segmentation of the ROI of the lumbar vertebrae helps to shorten the time for doctors to annotate vertebrae during clinical bone density examinations. The paper employs the tube masking MAE pre-trained model to effectively extract contextual information of the 3D lumbar vertebrae, combining it with a semi-supervised network leveraging pseudo-label generation for fine-tuning, which leads to effective 3D segmentation of the lumbar vertebrae.

Background: Although computed tomography (CT) is widely employed in disease detection, X-ray radiation may pose a risk to the health of patients. Reducing the projection views is a common method, however, the reconstructed images often suffer from streak artifacts.

Purpose: In previous related works, it can be found that the convolutional neural network (CNN) is proficient in extracting local features, while the Transformer is adept at capturing global information. To suppress streak artifacts for sparse-view CT, this study aims to develop a method that combines the advantages of CNN and Transformer.

Methods: In this paper, we propose a Multi-Attention and Dual-Branch Feature Aggregation U-shaped Transformer network (MAFA-Uformer), which consists of two branches: CNN and Transformer. Firstly, with a coordinate attention mechanism, the Transformer branch can capture the overall structure and orientation information to provide a global context understanding of the image under reconstruction. Secondly, the CNN branch focuses on extracting crucial local features of images through channel spatial attention, thus enhancing detail recognition capabilities. Finally, through a feature fusion module, the global information from the Transformer and the local features from the CNN are integrated effectively.

Results: Experimental results demonstrate that our method achieves outstanding performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE). Compared with Restormer, our model achieves significant improvements: PSNR increases by 0.76 dB, SSIM improves by 0.44%, and RMSE decreases by 8.55%.

Conclusion: Our method not only effectively suppresses artifacts but also better preserves details and features, thereby providing robust support for accurate diagnosis of CT images.

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to misdiagnosis and delays in treatment progress but also contributes to the continued transmission of tuberculosis bacteria, posing a risk to other individuals. Currently, CT imaging is extensively utilized in computer-aided diagnosis (CAD). The main features of ST on CT images include bone destruction, osteosclerosis, sequestration formation, and intervertebral disc damage. However, manual diagnosis by doctors may result in subjective judgments and misdiagnosis. Therefore, an accurate and objective method is needed for diagnosing of spinal tuberculosis. In this paper, we put forward an assistive diagnostic approach for spinal tuberculosis that is based on deep learning. The approach uses the Mask R-CNN model. Moreover, we modify the original model network by incorporating the ResPath and cbam* to improve the performance metrics, namely $mA{P}_{\mathrm{small}}$ and F1-score. Meanwhile, other deep learning models such as Faster-RCNN and SSD were also compared. Experimental results demonstrate that the enhanced model can effectively identify spinal tuberculosis lesions, with an $mA{P}_{\mathrm{small}}$ of 0.9175, surpassing the original model's 0.8340, and an F1-score of 0.9335, outperforming the original model's 0.8657.

Background: The dynamic multi-leaf collimator (DMLC) plays a crucial role in shaping X-rays, significantly enhancing the precision, efficiency, and quality of tumor radiotherapy.

Objective: To improve the shaping effect of X-rays by optimizing the end structure of the DMLC leaf, which significantly impacts the collimator's performance.

Methods: This study introduces the innovative application of the multi-objective particle swarm optimization (MOPSO) algorithm to optimize DMLC parameters, including leaf end radius, source-to-leaf distance, leaf height, and tangent angle between the leaf end and the central axis. The main optimization objectives are to minimize the width and variance of the penumbra, defined as the distance between the 80% and 20% dose of X-rays on the isocenter plane, which directly impacts treatment accuracy.

Results: Structural optimization across various scenarios showed significant improvements in the size and uniformity of the penumbra, ensuring a more precise radiation dose. Based on the optimized structure, a three-dimensional model of the MLC was designed and an experimental prototype was fabricated for performance testing. The results indicate that the optimized MLC exhibits a smaller penumbra.

Conclusion: The proposed optimization method significantly enhances the precision of radiotherapy while minimizing radiation exposure to healthy tissue, representing a notable advancement in radiotherapy technology.

Background: We study the reconstruction problem for incomplete view tomography, including sparse view tomography and limited angle tomography, by the Landweber iteration and its accelerated version. Traditional implementations of these Landweber-type iterative methods necessitate multiple large-scale matrix-vector multiplications, which in turn require substantial time and storage resources.

Objective: This paper aims to develop and test a novel and efficient discretization approach for a class of Landweber-type methods that minimizes storage requirements by incorporating the specific structure of the incomplete view Radon transform.

Methods: We prove that the normal operator of incomplete view Radon transform in these methods is a compact convolution operator, and derive the explicit representation of its convolution kernel. Discretized by the pixel basis, these Landweber-type iterative methods can be implemented quickly and accurately by introducing a discretized convolution operation between two small-scale matrices with minimal storage requirements.

Results: For the simulated complete and limited angle data, the reconstruction results using various Landweber-type methods with our proposed discretization scheme achieve a 1-5dB improvement in PSNR and require one-third of computation time compared to the traditional approach. For the simulated sparse view data, our discretization scheme yields a valid image with the highest PSNR.

Conclusions: The Landweber-type iterative methods, when combined with our proposed discretization approach based on the Radon transform, are effective for addressing the incomplete view tomography problem.

Background: Pneumoconiosis staging is challenging due to the low clarity of X-ray images and the small, diffuse nature of the lesions. Additionally, the scarcity of annotated data makes it difficult to develop accurate staging models. Although clinical text reports provide valuable contextual information, existing works primarily focus on designing multimodal image-text contrastive learning tasks, neglecting the high similarity of pneumoconiosis imaging representations. This results in inadequate extraction of fine-grained multimodal information and underutilization of domain knowledge, limiting their application in medical tasks.

Objective: The study aims to address the limitations of current multimodal methods by proposing a new approach that improves the precision of pneumoconiosis diagnosis and staging through enhanced fine-grained learning and better utilization of domain knowledge.

Methods: The proposed Multimodal Similarity-aware and Knowledge-driven Pre-Training (MSK-PT) approach involves two stages. In the first stage, we deeply analyze the similar features of pneumoconiosis images and use a similarity-aware modality alignment strategy to explore the fine-grained representations and associated disturbances of pneumoconiosis lesions between images and texts, guiding the model to match more appropriate feature representations. In the second stage, we utilize data-associated features and pre-stored domain knowledge features as priors and constraints to guide the downstream model in the visual domain without annotations. To address potential erroneous labels generated by model predictions, we further introduce an uncertainty threshold strategy to mitigate the negative impact of imperfect prediction labels and enhance model interpretability.

Results: We collected and created the pneumoconiosis chest X-ray (PneumoCXR) dataset to evaluate our proposed MSK-PT method. The experimental results show that our method achieved a classification accuracy of 81.73%, outperforming the state-of-the-art algorithms by 2.53%.

Conclusions: MSK-PT showed diagnostic performance that matches or exceeds the average radiologist's level, even with limited labeled data, highlighting the method's effectiveness and robustness.

Background: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is crucial for proper treatment and controlling the spread of Tuberculosis disease. Chest X-ray imaging is one of the most widely used diagnostic tools for detecting the Tuberculosis, which is time-consuming, and prone to errors. Nowadays, deep learning model provides the automated classification of medical images with promising outcome.

Objective: Thus, this research introduced a deep learning based segmentation and classification model. Initially, the Adaptive Gaussian Filtering based pre-processing and data augmentation is performed to remove artefacts and biased outcome. Then, Attention UNet (A_UNet) based segmentation is proposed for segmenting the required region of Chest X-ray.

Methods: Using the segmented outcome, Enhanced Swin Transformer (EnSTrans) model based Tuberculosis classification model is designed with Residual Pyramid Network based Multi-layer perceptron (MLP) layer for enhancing the classification accuracy.

Results: Enhanced Lotus Effect Optimization (EnLeO) Algorithm is employed for the loss function optimization of the EnSTrans model.

Conclusions: The proposed methods acquired the Accuracy, Recall, Precision, F-score, and Specificity of 99.0576%, 98.9459%, 99.145%, 98.96%, and 99.152% respectively.

Background: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular events. However, there is no study combining radiomics with deep learning techniques to predict non-calcified plaques (NCP) in coronary artery at present.

Objective: To investigate the value of combination of radiomics and deep learning features based on non-contrast computerized tomography (CT) scans of pericoronary adipose tissue (PCAT), integrating with clinical risk factors of patients, in identifying coronary inflammation and predicting the presence of NCP.

Methods: The clinical and imaging data of 353 patients were analyzed. The region of interest (ROI) of PCAT was manually outlined on non-contrast CT scan images, like coronary CT calcium score sequential images, then the radiomics and deep learning features in ROIs were extracted respectively. In training set (Center 1), after performing feature selection, radiomics and deep learning feature models were established, meanwhile, clinical models were built. Finally, combined models were developed out via integrating clinical, radiomics, and deep learning features. The predictive performance of the four feature model groups (clinical, radiomics, deep learning, and three combination) was assessed by seven different machine learning models through generation of receiver operating characteristic curves (ROC) and the calculation of area under the curve (AUC), sensitivity, specificity, and accuracy. Furthermore, the predictive performance of each model was validated in an external validation set (Center 2).

Results: For the single model comparation, eXtreme Gradient Boosting (XGBoost) showed the best performance among the clinical model group in the validation set. And Random Forest (RF) exhibited the best indicative performance not only among the radiomics feature group but also in the deep learning feature model group. What's more, among the combined model group, RF still displayed the best predictive performance, with the value of AUC, sensitivity, specificity, and accuracy in the validation set are 0.963, 0.857, 0.929, and 0.905.

Conclusion: The RF model in the combined model group based on non-contrast CT scan PCAT can predict the presence of NCP more accurately and has the potential for preliminary screening of the NCP.

Backgroud: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging characteristics.

Objective: The objective of this study is to facilitate early diagnosis of patients and reduce clinician stress by constructing a deep learning-based classification model for automatic diagnosis of schwannoma and meningiomas using magnetic resonance images (MRI).

Methods: We retrospectively collected MRI images of 74 patients with pathologically confirmed schwannoma and meningiomas from 2015 to 2020 at a local hosipital, and constructed a CNN model based on the PyTorch's deep learning framework for the discrimination between the two. First, a modified feature fusion CNN model (ResNet34-SKConv) was trained by introducing a selective convolutional kernel module into the original CNN model. The introduction of the selective convolutional kernel module enhances the network's focus on tumor features and effectively improves the network's performance. Finally, the trained model was used to process all the MRI image slices to achieve the classification of SCH and MEN patients by the voting prediction method.

Results: Using the 5-fold cross-validation method, this new ResNet34-SKConv model achieves a classification accuracy of 92.32%, a specificity of 95.87%, and a F1-score of 93.54, respectively.

Conclusion: This study demonstrated that a classification model using a deep learning network can be effective in achieving differential diagnosis of SCH and MEN. Thus, the new method has great potential for developing new computer-aided diagnosis and applications with future clinical practice.