Authors: Hu, Ying | Yang, Yifeng | Hou, Xuewen | Zhou, Yan | Nie, Shengdong
Article Type: Research Article
Abstract: OBJECTIVE: To investigate relationships between the severity of white matter hyperintensities (WMH), functional brain activity, and cognition in cerebral small vessel disease (CSVD) based on resting-state functional magnetic resonance imaging (rs-fMRI) data. METHODS: A total of 103 subjects with CSVD were included. The amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC) and their graph properties were applied to explore the influence of WMH burden on functional brain activity. We also investigated whether there are correlations between different functional brain characteristics and cognitive assessments. Finally, we selected disease-related rs-fMRI features in combination with ensemble learning to classify …CSVD patients with low WMH load and with high WMH load. RESULTS: The high WMH load group demonstrated significantly abnormal functional brain activity based on rs-MRI data, relative to the low WMH load group. ALFF and graph properties in specific brain regions were significantly correlated with patients’ cognitive assessments in CSVD. Moreover, altered rs-fMRI signal can help predict the severity of WMH in CSVD patients with an overall accuracy of 92.23%. CONCLUSIONS: This study provided a comprehensive analysis and evidence for a pattern of altered functional brain activity under different WMH load in CSVD based on rs-fMRI data, enabling accurately individual prediction of status of WMH. Show more
Keywords: Cerebral small vessel disease, white matter hyperintensities, rs-fMRI, cognition, aging
DOI: 10.3233/XST-221218
Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1213-1227, 2022
Authors: Chen, Yang | He, Yiwen | Jiang, Zhuoyun | Xie, Yuanzhong | Nie, Shengdong
Article Type: Research Article
Abstract: BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the …last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS: The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People’s Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients. Show more
Keywords: Ischemic stroke, computed tomography angiography, radiomics, convolutional neural networks, subtyping model
DOI: 10.3233/XST-221284
Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 223-235, 2023
Authors: Fang, Ting | Liu, Naijia | Nie, Shengdong | Jia, Shouqiang | Ye, Xiaodan
Article Type: Research Article
Abstract: BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. OBJECTIVE: We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS. METHODS: Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, …we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region. RESULTS: The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96). CONCLUSIONS: This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS. Show more
Keywords: Alberta stroke program early CT score, Acute ischemic stroke, Deep learning, Computer-aided method, CT angiography
DOI: 10.3233/XST-230119
Citation: Journal of X-Ray Science and Technology, vol. 32, no. 1, pp. 17-30, 2024
Authors: Chen, Yang | Dai, Xiaoting | Duan, Huihong | Gao, Lei | Sun, Xiwen | Nie, Shengdong
Article Type: Research Article
Abstract: BACKGROUND: Low dose computed tomography (LDCT) reduces radiation damage to patients. However, with the decrease of radiation dose, LDCT images of the lung often appear some serious problems such as poor contrast, noise and streak artifacts. OBJECTIVE: To improve the quality of lung LDCT images, this study proposed and investigated a new denoising method based on classification training structure combined dictionary for lung LDCT images. METHODS: First, top-hat transform and anisotropic diffusion with a shock filter (ADSF) algorithm are used to enhance the image contrast and image details. Second, an adaptive dictionary is trained and used for noise reduction. Third, …more image details are extracted from the residual image by using the atom activity measurement. The final result is obtained by combining the dictionary denoising result with the extracted detail information. The proposed method is then validated by both simulated and clinical lung LDCT images. Four metrics including Contrast-to-Noise Ratio (CNR), Noise Suppression Index (NSI), Edge Preserving Index (EPI), and Blurring Index (BI) are computed to quantitatively evaluate image quality. RESULTS: The results showed that the CNR, NSI, EPI, and BI of our method reached 8.953, 0.9500, 0.7230 and 0.0170, respectively. Noise and streak artifacts can be removed from lung LDCT images while keeping and retaining more details. CONCLUSIONS: Comparing with the results of other methods tested using the same dataset, this study demonstrated that our new method significantly improved quality of the lung LDCT images. Show more
Keywords: Low-dose computed tomography (LDCT), top-hat transform, anisotropic diffusion with shock filter (ADSF), spare representation, image quality improvement
DOI: 10.3233/XST-190605
Citation: Journal of X-Ray Science and Technology, vol. 28, no. 2, pp. 255-270, 2020
Authors: Cai, Xin | Hou, Xuewen | Sun, Rong | Chang, Xiao | Zhu, Honglin | Jia, Shouqiang | Nie, Shengdong
Article Type: Research Article
Abstract: BACKGROUND: As one of the significant preoperative imaging modalities in medical diagnosis, Magnetic resonance imaging (MRI) takes a long scanning time due to its special imaging principle. OBJECTIVE: We propose an innovative MRI reconstruction strategy and data consistency method based on deep learning to reconstruct high-quality brain MRIs from down-sampled data and accelerate the MR imaging process. METHODS: Sixteen healthy subjects undergoing T1-weighted spin-echo (SE) and T2-weighted fast spin-echo (FSE) sequences by a 1.5T MRI scanner were recruited. A Y-Net3+ network was used to facilitate the high-quality MRI reconstruction through context information. In addition, the existing data consistency fidelity method …was improved. The difference between the reconstructed K-space and the original K-space was shorten by the linear regression algorithm. Therefore, the redundant artifacts derived from under-sampling were avoided. The Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) were applied to quantitatively evaluate image reconstruction performance of different down-sampling patterns. RESULTS: Compared with the classical Y-Net, Y-Net3+ network improved SSIM and PSNR of MRI images from 0.9164±0.0178 and 33.2216±3.2919 to 0.9387±0.0363 and 35.1785±3.3105, respectively, under compressed sensing reconstruction with acceleration factor of 4. The improved network increases signal-to-noise ratio and adds more image texture information in the reconstructed images. Furthermore, in the process of data consistency, linear regression analysis was used to reduce the difference between the reconstructed K-space and the original K-space, so that the SSIM and PSNR were increased to 0.9808±0.0081 and 40.9254±1.1911, respectively. CONCLUSIONS: The improved Y-Net combined with data consistency fidelity method elucidates its potential in reconstructing high-quality T2-weighted images from the down-sampled data by fully exploring the T1-weighted information. With the advantage of avoiding down-sampled artifacts, the improved network exhibits remarkable clinical promise for fast MRI applications. Show more
Keywords: Magnetic resonance imaging, Deep learning, Multi-contrast MRI, Image reconstruction, Data consistency
DOI: 10.3233/XST-230012
Citation: Journal of X-Ray Science and Technology, vol. 31, no. 4, pp. 797-810, 2023