Journal of X-Ray Science and Technology - Volume 30, issue 3
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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: BACKGROUND: Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process. OBJECTIVE: To improve the quality of low-intensity images and identify the leukemia classification by utilizing CNN-based Deep Learning (DCNN) strategy. METHODS: The strategies employed for the recognition of leukemia classifications in the advised strategy are DCNN (ResNet-34 & DenseNet-121). The histogram equalization-based adaptive gamma correction followed by guided filtering applies to study the improvement in intensity and preserve the essential details of the image. The DCNN is used as a feature extractor…to help classify leukemia types. Two datasets of ASH with 520 images and ALL-IDB with 559 images are used in this study. In 1,079 images, 779 are positive cases depicting leukemia and 300 images are negative (normal) cases. Thus, to validate performance of this DCNN strategy, ASH and ALL-IDB datasets are promoted in the investigation process to classify between positive and negative images. RESULTS: The DCNN classifier yieldes the overall classification accuracy of 99.2% and 98.4%, respectively. In addition, the achieved classification specificity, sensitivity, and precision are 99.3%, 98.7%, 98.4%, and 98.9%, 98.4%,98.6% applying to two datasets, respectively, which are higher than the performance using other machine learning classifiers including support vector machine, decision tree, naive bayes, random forest and VGG-16. CONCLUSION: Ths study demonstrates that the proposed DCNN enables to improve low-intensity images and accuracry of leukemia classification, which is superior to many of other machine leaning classifiers used in this research field.
Abstract: OBJECTIVES: To evaluate the feasibility of using coronary computed tomography angiography (CCTA)-derived strain to detect regional myocardial dysfunction in coronary artery disease (CAD) patients with normal left ventricular ejection fraction (LVEF). METHODS: A total of 1,580 segments from 101 patients who underwent stressed CT myocardial perfusion imaging (CT-MPI) and CCTA were retrospectively enrolled in this study. The CT-derived global and segmental strain values were evaluated using the feature tracking technique. Segments with myocardial blood flow (MBF) < 125 ml/min/100 ml and 95 ml/min/100 ml were categorized as ischemic and infarcted, respectively. RESULTS: Segmental radial strain (SRS) and segmental circumferential strain (SCS)…in the abnormal segments (including all segments with MBF < 125 ml/min/100 ml) were significantly lower than those in the normal segments (14.81±8.65% vs 17.17±9.13%, p < 0.001; –10.21±5.79% vs –11.86±4.52%, p < 0.001, respectively). SRS and SCS values in infarcted segments were significantly impaired compared with the ischemic segments (12.43±8.03% vs. 15.32±8.71%, p = 0.038; –7.72±5.91% vs. –10.67±5.66%, p = 0.010, respectively). The AUCs for SRS and SCS in detecting infarcted segments were 0.622 and 0.698, respectively (p < 0.05). CONCLUSIONS: It is feasible for using CCTA-derived strain parameters to detect regional myocardial dysfunction in CAD patients with preserved LVEF. Segmental radial and circumferential strain have the potential ability to distinguish myocardial ischemia from infarction, and normal from ischemic myocardium.
Abstract: OBJECTIVE: To establish and validate a model capable of predicting lymph node metastasis (LNM) of non-small cell lung cancer (NSCLC) patients. METHODS: Preoperative clinical and CT imaging data on patients with NSCLC undergoing surgery were retrospectively analyzed. A model was developed using a training cohort of 290 patients. The univariate analysis followed by dichotomous logistic regression was performed to estimate different risk factors of lymph node metastasis, and a nomogram was constructed. Using another testing cohort of 120 patients, the performance of the nomogram was validated using several evaluation methods and indices and evaluated including via the area…under the curve (AUC), calibration curve, Hosmer-Lemeshow test and decision curve analysis (DCA). RESULTS: CT-based imaging signs were important independent risk factors for lymph node metastasis in NSCLC patients. The possible risk factors also included four other independent risk factors through dichotomous logistic regression, i.e., age, SIRI, PNI and CEA, which were filtered and included in the nomogram. Nomogram yields AUC values of 0.828 [95% confidence interval (CI): 0.778–0.877] in the training cohort and 0.816 (95% CI: 0.737–0.895) in the validation cohort, respectively. The calibration curves showed high agreement in both the training and validation cohorts. At the threshold probability of 0–0.8, the nomogram increases the net outcomes compared to the treat-none and treat-all lines in the decision curve. CONCLUSIONS: The nomogram based on the PNI and CT images signs holds promise as a novel and accurate tool for predicting the LNM in NSCLC patients and guiding intraoperative lymph node dissection.
Abstract: BACKGROUND: Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images. OBJECTIVE: This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations. METHODS: The total variation combining the nonlocal means filtration is proposed and the alternating direction method of multipliers is utilized to develop an efficient algorithm. The first order approximation of…linear expansion at intermediate point is applied to overcome the computation of the huge CT system matrix. RESULTS: The proposed method improves root mean squared error by 25.6% compared to the recent block-matching sparsity regularization (BMSR) on simulation dataset of 19 views. The structure similarities of image of the new method is higher than 0.95, while that of BMSR is about 0.92. Moreover, on real rabbit dataset of 20 views, the peak signal-to-noise ratio (PSNR) of the new method is 36.84, while using other methods PSNR are lower than 35.81. CONCLUSIONS: The proposed method shows advantages on noise suppression and detail preservations over the competing algorithms used in CT image reconstruction.
Keywords: Computed tomography, Image reconstruction, nonlocal means filtration, total variation, alternating direction method of multipliers, first order approximation
Abstract: BACKGROUND: In vertebrae, the amount of cortical bone has been estimated at 30–60%, but 45–75% of axial load on a vertebral body is borne by cortical bone. OBJECTIVE: To compare the role of L1 CT-attenuation and cortical thickness in predicting osteoporosis by opportunistic CT and explore cortical thickness value in osteoporosis. METHODS: We collected data of 94 patients who underwent DXA and thoracic and/or abdominal CT to demonstrate an entire L1 for other indications in routine practice. Patients were divided into three groups according to T-score: osteoporosis, osteopenia, or normal. CT-attenuation value and cortical thickness of…L1 were measured. ANOVA analysis was utilized to analyze CT-attenuation and cortical thickness among the three groups. Sensitivity, specificity, and area under the curve (AUC) predicting low BMD were determined using ROC. Pearson correlations were employed to describe relationship between L1 BMD and CT-attenuation value, BMD, as well as cortical thickness. RESULTS: The mean cortical thickness was 0.83±0.11, 0.72±0.10, and 0.64±0.09 mm for normal, osteopenia, and osteoporotic subgroups, respectively. A statistically significant difference was observed in cortical thickness and CT-attenuation value among these three subgroups. A mean CT-attenuation value threshold of > 148.7 yielded 73.0% sensitivity and 86.0% specificity for distinguishing low BMD from normal with an AUC = 0.83. Pearson correlation analysis indicated that BMD was positively correlated with CT-attenuation (r = 0.666, P < 0.001) and cortical thickness (r = 0.604, P < 0.001). CONCLUSIONS: L1 CT-attenuation and cortical thickness measured on opportunistic CT can help predict osteoporosis. Compared with cortical thickness, CT-attenuation is a more sensitive and accurate index for distinguishing low BMD from normal.
Keywords: Osteoporosis, cortical thickness, bone mineral density, dual x-ray absorptiometry, Computed tomography (CT)