Journal of X-Ray Science and Technology - Volume 28, issue 6
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Journal of X-Ray Science and Technology is an international journal designed for the diverse community (biomedical, industrial and academic) of users and developers of novel x-ray imaging techniques. The purpose of the journal is to provide clear and full coverage of new developments and applications in the field.
Areas such as x-ray microlithography, x-ray astronomy and medical x-ray imaging as well as new technologies arising from fields traditionally considered unrelated to x rays (semiconductor processing, accelerator technology, ionizing and non-ionizing medical diagnostic and therapeutic modalities, etc.) present opportunities for research that can meet new challenges as they arise.
Abstract: OBJECTIVE: To study the diagnostic value of real-time ultrasound shear wave elastography (US-SWE) in evaluating the histological stages of nonalcoholic fatty liver disease (NAFLD) in a rabbit model. MATERIALS AND METHODS: Twenty-one 8-week-old rabbits were fed a high-fat, high-cholesterol diet (experimental groups), and seven rabbits were fed a standard diet (control group). All rabbits underwent real-time US-SWE at various time points to document the histological stages of NAFLD. We categorized the histological stages as normal, NAFL, borderline nonalcoholic steatohepatitis (NASH), and NASH. We measured the elastic modulus of the liver parenchyma and analyzed the diagnostic efficacy of real-time…US-SWE using the area under receiver operating characteristic curve (AUC) for the four histological stages. RESULTS: The mean, minimum, and maximum elastic modulus increase for NAFL, borderline NASH, and NASH. For the mean, minimum, and maximum elastic modulus, AUCs are 0.891 (95% confidence interval [CI]: 0.716–0.977), 0.867 (95% CI: 0.686–0.965), and 0.789 (95% CI:0.594–0.919) for differentiating normal liver from liver with NAFLD, respectively; AUCs are 0.846 (95% CI: 0.660–0.954), 0.818 (95% CI: 0.627–0.937), and 0.797 (95% CI:0.627–0.913) for differentiating normal liver or liver with NAFL from liver with borderline NASH or NASH, respectively; AUCs are 0.889 (95% CI: 0.713–0.976), 0.787 (95% CI: 0.591–0.918), and 0.895 (95% CI:0.720–0.978) for differentiating liver with NASH from liver with lower severity NAFLD or normal liver, respectively. CONCLUSIONS: Real-time US-SWE is an accurate, noninvasive technique for evaluating the histological stages of NAFLD by measuring liver stiffness. We recommend using the mean elastic modulus to differentiate the histological stages, with the minimum and maximum elastic modulus as valuable complements.
Abstract: BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE: In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a…picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS: We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS: The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS: Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients.
Keywords: Rheumatoid arthritis, deep learning, image classification, convolutional neural network
Abstract: OBJECTIVE: To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS: Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the…value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS: Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9–10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9–10). The entropy and energy values correlate with GS (r = 0.767, r = –0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8. CONCLUSION: Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.
Keywords: Prostate cancer, magnetic resonance imaging, texture analysis, Gleason score