Journal of X-Ray Science and Technology - Volume 30, issue 3

Authors: Bie, Yifan | Yang, Shuo | Li, Xingchao | Zhao, Kun | Zhang, Changlei | Zhong, Hai

Article Type: Research Article

Abstract: OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V). METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and …medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable. RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively. CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V. Show more

Keywords: Deep learning image reconstruction, computed tomography (CT), adaptive statistical iterative reconstruction-Veo, image quality, renal CT, adrenal CT

DOI: 10.3233/XST-211105

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 409-418, 2022

Authors: Park, Hyemin | Yoon, Yongsu | Kim, Eunhye | Jeong, Hoiwoun | Kim, Jungsu

Article Type: Research Article

Abstract: BACKGROUND: The International Electrotechnical Commission established the concept of the exposure index (EI), target exposure index (EIT ) and deviation index (DI). Some studies have conducted to utilize the EI as a patient dose monitoring tool in the digital radiography (DR) system. OBJECTIVE: To establish the appropriate clinical EIT , this study aims to introduce the diagnostic reference level (DRL) for general radiography and confirm the usefulness of clinical EI and DI. METHODS: The relationship between entrance surface dose (ESD) and clinical EI is obtained by exposure under the national radiography conditions of …Korea for 7 extremity examinations. The EI value when the ESD is the DRL is set as the clinical EIT , and the change of DI is then checked. RESULTS: The clinical EI has proportional relationship with ESD and is affected by the beam quality. When the clinical EIT is not adjusted according to the revision of DRLs, there is a difference of up to 2.03 in the DI value and may cause an evaluation error of up to 1.6 times for patient dose. CONCLUSIONS: If the clinical EIT is periodically managed according to the environment of medical institution, the appropriate patient dose and image exposure can be managed based on the clinical EI, EIT , and DI. Show more

Keywords: Digital radiography, dose optimization, extremity radiography, exposure index, flat panel detector

DOI: 10.3233/XST-211084

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 419-432, 2022

Authors: Xu, Yongshun | Sushmit, Asif | Lyu, Qing | Li, Ying | Cao, Ximiao | Maltz, Jonathan S. | Wang, Ge | Yu, Hengyong

Article Type: Research Article

Abstract: Cardiac CT provides critical information for the evaluation of cardiovascular diseases. However, involuntary patient motion and physiological movement of the organs during CT scanning cause motion blur in the reconstructed CT images, degrading both cardiac CT image quality and its diagnostic value. In this paper, we propose and demonstrate an effective and efficient method for CT coronary angiography image quality grading via semi-automatic labeling and vessel tracking. These algorithms produce scores that accord with those of expert readers to within 0.85 points on a 5-point scale. We also train a neural network model to perform fully-automatic motion artifact grading. We …demonstrate, using XCAT simulation tools to generate realistic phantom CT data, that supplementing clinical data with synthetic data improves the scoring performance of this network. With respect to ground truth scores assigned by expert operators, the mean square error of grading motion of the right coronary artery is reduced by 36% by synthetic data supplementation. This demonstrates that augmentation of clinical training data with realistically synthesized images can potentially reduce the number of clinical studies needed to train the network. Show more

Keywords: Cardiac CT, motion artifacts, semi-automatic labeling, vessel tracking, deep learning

DOI: 10.3233/XST-211109

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 433-445, 2022

Authors: Lian, Kai-Mei | Lin, Teng

Article Type: Research Article

Abstract: OBJECTIVE: To investigate the importance of color-map virtual touch tissue imaging (CMV) in assisting Breast Imaging Reporting and Data Systems (BI-RADS) in diagnosing malignant breast lesions. METHODS: A dataset included 134 patients and 146 breast lesions was assembled. All patients underwent biopsy or surgical excision of breast lesions, and pathological results were obtained. All patients with breast lesions also underwent conventional ultrasound (US) and CMV. Each lesion was assigned a CMV score based on the color pattern of the lesion and surrounding breast tissue and a BI-RADS classification rating based on US characteristics. We compared the diagnostic performance …of using BI-RADS and CMV separately and their combination. RESULTS: BI-RADS (odds ratio [OR]: 3.665; 95% confidence interval [CI]: 2.147, 6.258) and CMV (OR: 6.616; 95% CI: 2.272, 19.270) were independent predictors of breast malignancy (all P  < 0.05). The area under the receiver operating characteristic curves (AUC) for either CMV or BI-RADS alone was inferior to that of the combination (0.877 vs. 0.962; 0.938 vs. 0.962; all P  < 0.05). CONCLUSIONS: The performance of BI-RADS in diagnosing breast lesions is significantly improved by combining CMV. Therefore, we recommend CMV as an adjunct to BI-RADS. Show more

Keywords: Ultrasound, virtual tissue imaging, BI-RADS, regression analysis

DOI: 10.3233/XST-211110

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 447-457, 2022

Authors: Danala, Gopichandh | Ray, Bappaditya | Desai, Masoom | Heidari, Morteza | Mirniaharikandehei, Seyedehnafiseh | Maryada, Sai Kiran R. | Zheng, Bin

Article Type: Research Article

Abstract: BACKGROUND: Endovascular mechanical thrombectomy (EMT) is an effective method to treat acute ischemic stroke (AIS) patients due to large vessel occlusion (LVO). However, stratifying AIS patients who can and cannot benefit from EMT remains a clinical challenge. OBJECTIVE: To develop a new quantitative image marker computed from pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among AIS patients undergoing EMT after diagnosis of LVO. METHODS: A retrospective dataset of 31 AIS patients with pre-intervention CTP images is assembled. A computer-aided detection (CAD) scheme is developed to pre-process CTP images of …different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and machine learning (ML) models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. Performance of image markers is evaluated using the area under the ROC curve (AUC) and accuracy computed from the confusion matrix. RESULTS: The ML model using the neuroimaging features computed from the slopes of the subtracted cumulative blood flow curves between two cerebral hemispheres yields classification performance of AUC = 0.878±0.077 with an overall accuracy of 90.3%. CONCLUSIONS: This study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients’ prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients. Show more

Keywords: Acute ischemic stroke (AIS), quantitative image markers, prediction of AIS prognosis, computer-aided detection and diagnosis (CAD)

DOI: 10.3233/XST-221138

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 459-475, 2022

Authors: Luo, Yuan | Li, Yifan | Zhang, Yuwei | Zhang, Jianwei | Liang, Meng | Jiang, Lin | Guo, Li

Article Type: Research Article

Abstract: BACKGROUND: Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate. OBJECTIVE: To improve predictive performance of radiomics features for lung cancer by tuning the machine learning model parameters. METHODS: Using a dataset involving 263 cases (125 benign and 138 malignant) acquired from our hospital, each classifier model is trained and tested using 237 and 26 cases, respectively. We initially extract 867 radiomics features of CT images for model development and then test 10 feature selections and 7 models to determine the best method. We further tune …the parameter of the final model to reach the best performance. The adjusted final model is then validated using 224 cases acquired from Lung Image Database Consortium (LIDC) dataset (64 benign and 160 malignant) with the same set of selected radiomics features. RESULTS: During model development, the feature selection via concave minimization method show the best performance of area under ROC curve (AUC = 0.765), followed by l0-norm regularization (AUC = 0.741) and Fisher discrimination criterion (AUC = 0.734). Support vector machine (SVM) and random forest (RF) are the top two machine learning algorithms showing the best performance (AUC = 0.765 and 0.734, respectively), using by the default parameter. After parameter tuning, SVM with linear kernel achieves the best performance (AUC = 0.837), whereas the best tuned RF with the number of trees is 510 and yields a slightly lower performance (AUC = 0.775) in 26 test samples data. During model validation, the SVM and RF models yield AUC = 0.78 and 0.77, respectively. CONCLUSION: Appropriate quantitative radiomics features and accurate parameters can improve the model’s performance to predict lung cancer. Show more

Keywords: Lung neoplasms, machine learning, radiomics, parameter analysis, lung nodule classification

DOI: 10.3233/XST-211096

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 477-490, 2022

Authors: Gopatoti, Anandbabu | Vijayalakshmi, P.

Article Type: Research Article

Abstract: BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models. OBJECTIVE: This study aims to classify COVID-19 and normal patients from CXR images using semantic segmentation networks for detecting and labeling COVID-19 infected lung lobes in CXR images. METHODS: For semantically segmenting infected lung lobes in CXR images for COVID-19 early detection, three structurally different deep learning (DL) networks such as SegNet, U-Net and hybrid CNN with SegNet plus U-Net, are proposed and …investigated. Further, the optimized CXR image semantic segmentation networks such as GWO SegNet, GWO U-Net, and GWO hybrid CNN are developed with the grey wolf optimization (GWO) algorithm. The proposed DL networks are trained, tested, and validated without and with optimization on the openly available dataset that contains 2,572 COVID-19 CXR images including 2,174 training images and 398 testing images. The DL networks and their GWO optimized networks are also compared with other state-of-the-art models used to detect COVID-19 CXR images. RESULTS: All optimized CXR image semantic segmentation networks for COVID-19 image detection developed in this study achieved detection accuracy higher than 92%. The result shows the superiority of optimized SegNet in segmenting COVID-19 infected lung lobes and classifying with an accuracy of 98.08% compared to optimized U-Net and hybrid CNN. CONCLUSION: The optimized DL networks has potential to be utilised to more objectively and accurately identify COVID-19 disease using semantic segmentation of COVID-19 CXR images of the lungs. Show more

Keywords: X-ray image, Coronavirus disease, detection of COVID-19, deep learning, semantic segmentation, Grey wolf optimization (GWO)

DOI: 10.3233/XST-211113

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 491-512, 2022

Authors: Indumathy, D. | Ramesh, K. | Senthilkumar, G. | Sudha, S.

Article Type: Research Article

Abstract: Coronary artery diseases are one of the high-risk diseases, which occur due to the insufficient blood supply to the heart. The different types of plaques formed inside the artery leads to the blockage of the blood stream. Understanding the type of plaques along with the detection and classification of plaques supports in reducing the mortality of patients. The objective of this study is to present a novel clustering method of plaque segmentation followed by wavelet transform based feature extraction. The extracted features of all different kinds of calcified and sub calcified plaques are applied to first train and test three …machine learning classifiers including support vector machine, random forest and decision tree classifiers. The bootstrap ensemble classifier then decides the best classification result through a voting method of three classifiers. A training dataset including 64 normal CTA images and 73 abnormal CTA images is used, while a testing dataset consists of 111 normal CTA images and 103 abnormal CTA images. The evaluation metrics shows better classification rate and accuracy of 97.7%. The Sensitivity and Specificity rates are 97.8% and 97.5%, respectively. As a result, our study results demonstrate the feasibility and advantages of developing and applying this new image processing and machine learning scheme to assist coronary artery plaque detection and classification. Show more

Keywords: Coronary artery disease, coronary computed tomography angiography (CCTA) stationary wavelet transform (SWT), decision tree, FCM (Fuzzy C means)

DOI: 10.3233/XST-211077

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 513-529, 2022

Authors: Fu, Bo | Zhang, Xiangyi | Wang, Liyan | Ren, Yonggong | Thanh, Dang N.H.

Article Type: Research Article

Abstract: BACKGROUND: In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution. OBJECTIVE: In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet). METHODS: Firstly, the output results of L 0 Gradient Minimization are used as the labels of a dental CT image dataset to form a pseudo-image pair with the real dental CT images, which are used to …train the noise generation network to estimate real noise distribution. Then, for the lung CT images of the LIDC/IDRI database, we migrate the real noise to the noise-free lung CT images, to construct a new almost-real noisy images dataset. Since dental images and lung images are all CT images, this migration can be achieved. The denoising network is trained to realize the denoising of real LDCT for dental images by using this dataset but can extend for any low-dose CT images. RESULTS: To prove the effectiveness of our NGRNet, we conduct experiments on lung CT images with synthetic noise and tooth CT images with real noise. For synthetic noise image datasets, experimental results show that NGRNet is superior to existing denoising methods in terms of visual effect and exceeds 0.13dB in the peak signal-to-noise ratio (PSNR). For real noisy image datasets, the proposed method can achieve the best visual denoising effect. CONCLUSIONS: The proposed method can retain more details and achieve impressive denoising performance. Show more

Keywords: Low-dose CT images, image denoising, noise generation networks, real noise, deep learning

DOI: 10.3233/XST-211098

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 531-547, 2022

Authors: Lee, Donghyeon | Yun, Sungho | Soh, Jeongtae | Lim, Sunho | Kim, Hyoyi | Cho, Seungryong

Article Type: Research Article

Abstract: BACKGROUND: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation. OBJECTIVE: In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material …maps and is less sensitive to the accuracy of the spectral coefficients. METHODS: By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm. RESULTS: In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition. CONCLUSIONS: The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field. Show more

Keywords: X-ray imaging, computed tomography (CT), Dual-energy CT, material decomposition, iterative reconstruction, one-step method

DOI: 10.3233/XST-211054

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 3, pp. 549-566, 2022