Journal of X-Ray Science and Technology - Volume 29, issue 5

Authors: Huang, Deyang | Miao, Hengyuan | Zhang, Ziqi | Yang, Yanhong | Zhang, Lieguang | Lure, Fleming Y.M. | Wang, Zixian | Jaeger, Stefan | Guo, Lin | Xu, Tao | Liu, Jinxin

Article Type: Research Article

Abstract: BACKGROUND AND OBJECTIVE: Monitoring recovery process of coronavirus disease 2019 (COVID-19) patients released from hospital is crucial for exploring residual effects of COVID-19 and beneficial for clinical care. In this study, a comprehensive analysis was carried out to clarify residual effects of COVID-19 on hospital discharged patients. METHODS: Two hundred sixty-eight cases with laboratory measured data at hospital discharge record and five follow-up visits were retrospectively collected to carry out statistical data analysis comprehensively, which includes multiple statistical methods (e.g., chi-square, T-test and regression) used in this study. RESULTS: Study found that 13 of 21 hematologic …parameters in laboratory measured dataset and volume ratio of right lung lesions on CT images highly associated with COVID-19. Moderate patients had statistically significant lower neutrophils than mild and severe patients after hospital discharge, which is probably caused by more efforts on severe patients and slightly neglection of moderate patients. COVID-19 has residual effects on neutrophil-to-lymphocyte ratio (NLR) of patients who have hypertension or chronic obstructive pulmonary disease (COPD). After released from hospital, female showed better performance in T lymphocytes subset cells, especially T helper lymphocyte% (16% higher than male). According to this sex-based differentiation of COVID-19, male should be recommended to take clinical test more frequently to monitor recovery of immune system. Patients over 60 years old showed unstable recovery process of immune cells (e.g., CD45 + lymphocyte) within 75 days after discharge requiring longer clinical care. Additionally, right lung was vulnerable to COVID-19 and required more time to recover than left lung. CONCLUSIONS: Criterion of hospital discharge and strategy of clinical care should be flexible in different cases due to residual effects of COVID-19, which depend on several impact factors. Revealing remaining effects of COVID-19 is an effective way to eliminate disorder of mental health caused by COVID-19 infection. Show more

Keywords: COVID-19 pneumonia, hospital discharge, dynamic changes analysis, computed tomography (CT) lung image, routine blood test parameters

DOI: 10.3233/XST-210920

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 741-762, 2021

Authors: Mao, Ning | Jiao, Zimei | Duan, Shaofeng | Xu, Cong | Xie, Haizhu

Article Type: Research Article

Abstract: OBJECTIVE: To develop and validate a radiomics model based on contrast-enhanced spectral mammography (CESM), and preoperatively discriminate low-grade (grade I/II) and high-grade (grade III) invasive breast cancer. METHOD: A total of 205 patients with CESM examination and pathologically confirmed invasive breast cancer were retrospectively enrolled. We randomly divided patients into two independent sets namely, training set (164 patients) and test set (41 patients) with a ratio of 8:2. Radiomics features were extracted from the low-energy and subtracted images. The least absolute shrinkage and selection operator (LASSO) logistic regression were established for feature selection, which were then utilized to …construct three classification models namely, low energy, subtracted images and their combined model to discriminate high- and low-grade invasive breast cancer. Receiver operator characteristic (ROC) curves were used to confirm performance of three models in training set. The clinical usefulness was evaluated by using decision curve analysis (DCA). An independent test set was used to confirm the discriminatory power of the models. To test robustness of the result, we used 100 times LGOCV (leave group out cross validation) to validate three models. RESULTS: From initial radiomics feature pool, 17 and 11 features were selected for low-energy image and subtracted image, respectively. The combined model using 28 features showed the best performance for preoperatively evaluating the histologic grade of invasive breast cancer, with an area under the curve, AUC = 0.88, and 95%confidence interval [CI] 0.85 to 0.92 in the training set and AUC = 0.80 (95%CI 0.67 to 0.92) in the test set. The mean AUC of LGOCV is 0.82. CONCLUSIONS: CESM-based radiomics model is a non-invasive predictive tool that demonstrates good application prospects in preoperatively predicting histological grade of invasive breast cancer. Show more

Keywords: Breast cancer, histologic grade, contrast-enhanced spectral mammography, radiomics, preoperative prediction

DOI: 10.3233/XST-210886

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 763-772, 2021

Authors: Romero, Ignacio O. | Li, Changqing

Article Type: Research Article

Abstract: BACKGROUND: Pencil beam X-ray luminescence computed tomography (XLCT) imaging provides superior spatial resolution than other imaging geometries like sheet beam and cone beam geometries. However, the pencil beam geometry suffers from long scan times, resulting in concerns overdose which discourages the use of pencil beam XLCT. OBJECTIVE: The dose deposited in pencil beam XLCT imaging was investigated to estimate the dose from one angular projection scan with three different X-ray sources. The dose deposited in a typical small animal XLCT imaging was investigated. METHODS: A Monte Carlo simulation platform, GATE (Geant4 Application for Tomographic Emission) was …used to estimate the dose from one angular projection scan of a mouse leg model with three different X-ray sources. Dose estimations from a six angular projection scan by three different X-ray source energies were performed in GATE on a mouse trunk model composed of muscle, spine bone, and a tumor. RESULTS: With the Sigray source, the bone marrow of mouse leg was estimated to have a radiation dose of 44 mGy for a typical XLCT imaging with six angular projections, a scan step size of 100 micrometers, and 106 X-ray photons per linear scan. With the Sigray X-ray source and the typical XLCT scanning parameters, we estimated the dose of spine bone, muscle tissues, and tumor structures of the mouse trunk were 38.49 mGy, 15.07 mGy, and 16.87 mGy, respectively. CONCLUSION: Our results indicate that an X-ray benchtop source (like the X-ray source from Sigray Inc.) with high brilliance and quasi-monochromatic properties can reduce dose concerns with the pencil beam geometry. Findings of this work can be applicable to other imaging modalities like X-ray fluorescence computed tomography if the imaging protocol consists of the pencil beam geometry. Show more

Keywords: X-ray luminescence computed tomography (XLCT), X-ray imaging, radiation dose, Geant4 application for tomographic emission (GATE), Geant4

DOI: 10.3233/XST-210904

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 773-784, 2021

Authors: Nijiati, Mayidili | Zhang, Ziqi | Abulizi, Abudoukeyoumujiang | Miao, Hengyuan | Tuluhong, Aikebaierjiang | Quan, Shenwen | Guo, Lin | Xu, Tao | Zou, Xiaoguang

Article Type: Research Article

Abstract: Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of applying AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including …2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with more scant doctors and higher rates of the infected population. Show more

Keywords: Artificial intelligence (AI), tuberculosis (TB) diagnosis, low-resource settings, radiologists, chest X-rays (CXRs), assistance, convolutional neural network

DOI: 10.3233/XST-210894

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 785-796, 2021

Authors: Huang, Ying | Wan, Qian | Chen, Zixiang | Hu, Zhanli | Cheng, Guanxun | Qi, Yulong

Article Type: Research Article

Abstract: Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses …the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses. Show more

Keywords: X-ray computed tomography (CT), image reconstruction, total variation (TV), reduction of X-ray dose, reduction of image noise

DOI: 10.3233/XST-210906

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 797-812, 2021

Authors: Sun, Jihang | Li, Haoyan | Yang, Lixin | Zhou, Zuofu | Li, Michelle | Peng, Yun

Article Type: Research Article

Abstract: BACKGROUND: Low-tube voltage scanning improves CT attenuation value of contrast medium (CM). Thus, we hypothesized that 70 kVp in pediatric abdominal CT angiography (CTA) could be used to reduce both radiation and CM dose and improve patient comfort at the same time. OBJECTIVE: To evaluate the feasibility of using 70 kVp in pediatric abdominal CTA to reduce radiation dose and CM dose and improve patient care for children. MATERIALS AND METHODS: Forty-six children needing abdominal CTA were enrolled in the study group using low-dose scanning protocol with 70 kVp and 0.7–1.1 ml/kg contrast dose, and reconstructed with 50%ASIR-V. They were …compared with other 46 children in control group with matching body weight and underwent conventional CT scans with 100 kVp, 1.2–1.8 ml/kg contrast dose and reconstructed using 50%ASIR. Image quality of large vessels was evaluated using a 5-point scale. CT value and standard deviation of descending aorta (Ao) was measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Radiation dose, contrast dose, the maximum injection pressure between the two groups were also compared. RESULTS: Score for displaying large vessels by 70 kVp images was 3.91±0.28, lower than that (4.17±0.38) of the control group (p  < 0.05), but fully met the diagnostic requirements. CT value of Ao was 390.87±86.79HU in study group, which is higher than 343.93±49.94HU in control group, while there was no difference in SNR and CNR between two groups; the radiation dose, contrast dosage and injection pressure of the study group were 1.23±0.39mGy, 12.67±7.27 ml and 43.83±17.16psi, respectively, which are significantly lower than the 1.95±0.37mGy, 22.67±7.39 ml, and 77.59±19.68psi of control group. CONCLUSION: Use of 70 kVp in pediatric abdominal CTA provides diagnostic quality images while significantly reduce radiation and contrast dose, as well as injection pressure to improve patient comfort for children. Show more

Keywords: Computed tomography angiography (CTA), X-ray imaging, low voltage of X-ray tube, contrast medium, children

DOI: 10.3233/XST-210896

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 813-821, 2021

Authors: Pérez-Pelegrí, Manuel | Biarnés, Carles | Thió-Henestrosa, Santiago | Remollo, Sebastià | Gimeno, Alfredo | Cuba, Víctor | Teceño, Mikel | Martí-Navas, Marian | Serena, Joaquín | Pedraza, Salvador | Ruiz-Constantino, Juan Sebastián | Puig, Josep

Article Type: Research Article

Abstract: BACKGROUND AND OBJECTIVE: Estimates of parameters used to select patients for endovascular thrombectomy (EVT) for acute ischemic stroke differ among software packages for automated computed tomography (CT) perfusion analysis. To determine impact of these differences in decision making, we analyzed intra-observer and inter-observer agreement in recommendations about whether to perform EVT based on perfusion maps from 4 packages. METHODS: Perfusion CT datasets from 63 consecutive patients with suspected acute ischemic stroke were retrospectively postprocessed with 4 packages of Minerva, RAPID, Olea, and IntelliSpace Portal (ISP). We used Pearson correlation coefficients and Bland-Altman analysis to compare volumes of infarct …core, penumbra, and mismatch calculated by Minerva and RAPID. We used kappa analysis to assess agreement among decisions of 3 radiologists about whether to recommend EVT based on maps generated by 4 packages. RESULTS: We found significant differences between using Minerva and RAPID to estimate penumbra (67.39±41.37mL vs. 78.35±45.38 mL, p  < 0.001) and mismatch (48.41±32.03 vs. 61.27±32.73mL, p  < 0.001), but not of infarct core (p  = 0.230). Pearson correlation coefficients were 0.94 (95%CI:0.90–0.96) for infarct core, 0.87 (95%CI:0.79–0.91) for penumbra, and 0.72 (95%CI:0.57–0.83) for mismatch volumes (p  < 0.001). Limits of agreements were (–21.22–25.02) for infarct core volumes, (–54.79–32.88) for penumbra volumes, and (–60.16–34.45) for mismatch volumes. Final agreement for EVT decision-making was substantial between Minerva vs. RAPID (k  = 0.722), Minerva vs. Olea (k  = 0.761), and RAPID vs. Olea (k  = 0.782), but moderate for ISP vs. the other three. CONCLUSIONS: Despite quantitative differences in estimates of infarct core, penumbra, and mismatch using 4 software packages, their impact on radiologists’ decisions about EVT is relatively small. Show more

Keywords: Acute ischemic stroke, computed tomography perfusion (CTP), endovascular thrombectomy (EVT), estimates of infarct core, penumbra and mismatch

DOI: 10.3233/XST-210898

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 823-834, 2021

Authors: Rezaeijo, Seyed Masoud | Ghorvei, Mohammadreza | Mofid, Bahram

Article Type: Research Article

Abstract: OBJECTIVE: To develop an ensemble a deep transfer learning model of CT images for predicting pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). METHODS: The data were obtained from the public dataset ‘QIN-Breast’ from The Cancer Imaging Archive (TCIA). CT images were gathered before and after the first cycle of NAC. CT images of 121 breast cancer patients were used to train and test the model. Among these patients, 58 achieved a pCR and 63 showed a non-pCR based pathology examination of surgical results after NAC. The dataset was split into training and testing …subsets with a ratio of 7:3. In addition, the number of training samples in the dataset was increased from 656 to 1,968 by performing an image augmentation method. Two deep transfer learning models namely, DenseNet201 and ResNet152V2, and the ensemble model with a concatenation of two models, were trained and tested using CT images. RESULTS: The ensemble model obtained the highest accuracy of 100% on the testing dataset. Furthermore, we received the best performance of 100% in recall, precision and f1-score value for the ensemble model. This supports the fact that the ensemble model results in better-generalized model and leads to efficient framework. Although a 0.004 and 0.003 difference were seen between the AUC of two base models (DenseNet201 and ResNet152V2) and the proposed ensemble, this increase in the model quality is critical in medical research. T-SNE revealed that in the proposed ensemble, no points were clustered into the wrong class. These results expose the strong performance of the proposed ensemble. CONCLUSION: The study concluded that the ensemble model can increase the ability to predict breast cancer response to first-cycle NAC than two DenseNet201 and ResNet152V2 models. Show more

Keywords: Ensemble, deep transfer learning, breast, neoadjuvant chemotherapy, CT

DOI: 10.3233/XST-210910

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 835-850, 2021

Authors: Qiao, Zhiwei | Lu, Yang

Article Type: Research Article

Abstract: PURPOSE: Total Variation (TV) minimization algorithm is a classical compressed sensing (CS) based iterative image reconstruction algorithm that can accurately reconstruct images from sparse-view projections in computed tomography (CT). However, the system matrix used in the algorithm is often too large to be stored in computer memory. The purpose of this study is to investigate a new TV algorithm based on image rotation and without system matrix to avoid the memory requirement of system matrix. METHODS: Without loss of generality, a rotation-based adaptive steepest descent-projection onto convex sets (R-ASD-POCS) algorithm is proposed and tested to solve the TV …model in parallel beam CT. Specifically, simulation experiments are performed via the Shepp-Logan, FORBILD and real CT image phantoms are used to verify the inverse-crime capability of the algorithm and evaluate the sparse reconstruction capability and the noise suppression performance of the algorithm. RESULTS: Experimental results show that the algorithm can achieve inverse-crime, accurate sparse reconstruction and thus accurately reconstruct images from noisy projections. Compared with the classical ASD-POCS algorithm, the new algorithm may yield the similar image reconstruction accuracy without use of the huge system matrix, which saves the computational memory space significantly. Additionally, the results also show that R-ASD-POCS algorithm is faster than ASD-POCS. CONCLUSIONS: The proposed new algorithm can effectively solve the problem of using huge memory in large scale and iterative image reconstruction. Integrating with ASD-POCS frame, this no-system-matrix based scheme may be readily extended and applied to any iterative image reconstructions. Show more

Keywords: Rotation-based reconstruction, system matrix, TV minimization, iterative algorithm, computed tomography

DOI: 10.3233/XST-210929

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 851-865, 2021

Authors: Romero, Ignacio O. | Li, Changqing

Article Type: Research Article

Abstract: BACKGROUND: The time of flight (TOF) cone beam computed tomography (CBCT) was recently shown to reduce the X-ray scattering effects by 95% and improve the image CNR by 110% for large volume objects. The advancements in X-ray sources like in compact Free Electron Lasers (FEL) and advancements in detector technology show potential for the TOF method to be feasible in CBCT when imaging large objects. OBJECTIVE: To investigate the feasibility and efficacy of TOF CBCT in imaging smaller objects with different targets such as bones and tumors embedded inside the background. METHODS: The TOF method used …in this work was verified using a 24 cm phantom. Then, the GATE software was used to simulate the CBCT imaging of an 8 cm diameter cylindrical water phantom with two bone targets using a modeled 20 keV quasi-energetic FEL source and various TOF resolutions ranging from 1 to 1000 ps. An inhomogeneous breast phantom of similar size with tumor targets was also imaged using the same system setup. RESULTS: The same results were obtained in the 24 cm phantom, which validated the applied CBCT simulation approach. For the case of 8 cm cylindrical phantom and bone target, a TOF resolution of 10 ps improved the image contrast-to-noise ratio (CNR) by 57% and reduced the scatter-to-primary ratio (SPR) by 8.63. For the case of breast phantom and tumor target, image CNR was enhanced by 12% and SPR was reduced by 1.35 at 5 ps temporal resolution. CONCLUSIONS: This study indicates that a TOF resolution below 10 ps is required to observe notable enhancements in the image quality and scatter reduction for small objects around 8 cm in diameter. The strong scattering targets such as bone can result in substantial improvements by using TOF CBCT. Show more

Keywords: Time of flight (TOF), cone beam computed tomography (CBCT), Monte-Carlo Simulation, GATE software

DOI: 10.3233/XST-210918

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 867-880, 2021