Journal of X-Ray Science and Technology - Volume 30, issue 4
Purchase individual online access for 1 year to this journal.
Price: EUR 160.00
Impact Factor 2022: 2.442
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: PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive…performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.
Abstract: BACKGROUND: Head computed tomography (CT) is a commonly used imaging modality in radiology facilities. Since multiplanar reconstruction (MPR) processing can produce different results depending on the medical staff in charge, there is a possibility that the antemortem and postmortem images of the same person could be assessed and identified differently. OBJECTIVE: To propose and test a new automatic MPR method in order to address and overcome this limitation. METHODS: Head CT images of 108 cases are used. We employ the standardized transformation of statistical parametric mapping 8. The affine transformation parameters are obtained by standardizing the…captured CT images. Automatic MPR processing is performed by using this parameter. The sphenoidal sinus of the orbitomeatal cross section of the automatic MPR processing of this study and the conventional manual MPR processing are cropped with a matrix size of 128×128, and the value of zero mean normalized correlation coefficient is calculated. RESULTS: The computed zero mean normalized cross-correlation coefficient (Rzncc) of≥0.9, 0.8≤Rzncc < 0.9 and 0.7≤Rzncc < 0.8 are achieved in 105 cases (97.2%), 2 cases (1.9%), and 1 case (0.9%), respectively. The average Rzncc was 0.96±0.03. CONCLUSION: Using the proposed new method in this study, MPR processing with guaranteed accuracy is efficiently achieved.
Keywords: Automatic multi planar reconstruction, radiological identification, head computed tomography, forensic radiology, orbitomeatal base line, brain template.
Abstract: BACKGROUND: The detectors of existing large object radiation imaging systems generally work under current-integration mode and cannot distinguish effective signals of unreacted photons from interfering signals of electronic noise and scattered photons, therefore, resulting in image quality deterioration. OBJECTIVE: This study aims to design a new photon-counting mode γ -ray large object radiation imaging system. Therefore, interfering signals with lower energy than effective signals can be eliminated by energy analysis. In addition, the system enables to work properly even under 30∼300Ci Co-60 intensity. METHODS: Based on the physical analysis of the system, the design requirements are…listed. Following the requirements, the best-performing photon-counting detector based on LYSO and SiPM is used in the system. ZP-SK and (ZP)2 -SK filter circuits are designed for Co-60 radiation imaging system with the highest intensity of 100Ci and 300Ci, respectively. Then, a voltage comparator and an FPGA are followed to realize the function of energy analysis and photon counting. RESULTS: The proposed technical solution can improve the Steel Penetration (SP) by at least 60∼70 mmFe compared with the existing current-integration system, which is equivalent to the improvement obtained by increasing the intensity of the radioactive source more than 13 to 20 times. CONCLUSIONS: This study demonstrates the advantages of applying the new photon-counting mode γ -ray large object radiation imaging system to improve the radiation image quality and the penetration ability, which will have enormous potential for future applications.
Abstract: Tube of X-ray computed tomography (CT) system emitting a polychromatic spectrum of photons leads to beam hardening artifacts such as cupping and streaks, while the metal implants in the imaged object results in metal artifacts in the reconstructed images. The simultaneous emergence of various beam-hardening artifacts degrades the diagnostic accuracy of CT images in clinics. Thus, it should be deeply investigated for suppressing such artifacts. In this study, data consistency condition is exploited to construct an objective function. Non-convex optimization algorithm is employed to solve the optimal scaling factors. Finally, an optimal bone correction is acquired to simultaneously correct for…cupping, streaks and metal artifacts. Experimental result acquired by a realistic computer simulation demonstrates that the proposed method can adaptively determine the optimal scaling factors, and then correct for various beam-hardening artifacts in the reconstructed CT images. Especially, as compared to the nonlinear least squares before variable substitution, the running time of the new CT image reconstruction algorithm decreases 82.36% and residual error reduces 55.95%. As compared to the nonlinear least squares after variable substitution, the running time of the new algorithm decreases 67.54% with the same residual error.
Abstract: BACKGROUND: Characterization of normal and malignant breast tissues using X-ray scattering techniques has shown promising results and applications. OBJECTIVE: To examine possibility of characterizing normal and malignant breast tissues using the scattered photon distribution of polyenergetic beams of 30 kV X-rays. METHODS: A Monte Carlo simulation is upgraded so that it is capable of simulating input mammographic X-ray spectra from different target-filter combinations, tracing photon transport, and producing the distribution of scattered photons. The target-filter combinations include Mo-Mo, Mo-Al, Mo-Rh, Rh-Rh, Rh-Al, W-Rh, and W-Al. Analysis of obtained scattered photon distribution is carried out by comparing the…ratio of count under the peak in the momentum transfer region from 0 to 1.55 nm–1 , to that in the region from 1.6 to 9.1 nm–1 (covering the regions of scattering from fat and soft tissue, respectively) for breast samples with different percentages of normal tissue (0–100%). RESULTS: Mo-Mo target-filter combination shows a high linear dependence of the count under peak ratio on the percentage of normal tissue in breast samples (R2 = 0.9513). Despite slightly less linear than Mo-Mo, target-filter combinations other than Rh-Rh, W-Rh, and W-Al produce high linear responses (R2 > 0.9) CONCLUSION: Mo-Mo target-filter combination would probably be the most relevant in characterizing normal and malignant breast tissues from their scattered photon distribution.
Keywords: Monte Carlo simulation, breast tissue, X-ray scattering, tissue characterization, mammography
Abstract: BACKGROUND: Blending technology is usually used to improve quality of dual-energy computed (DECT) images. OBJECTIVES: To evaluate the blended DECT image qualities by employing the Blending-Property-Map (BP-Map) and elucidating the optimal parameters with the highest signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). METHODS: Sixty pairs of 80 kV and 140 kV CT images are blended non-linearly by four methods. Protocol A uses the fixed values of blending width (BW) and blending center (BC); Protocol B uses the values of BW = (CThepatic portal vein – CThepatic parenchymal) / 2 and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol C…uses a BW ranging from 10 to 100 HU at an interval of 10 HU and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol D uses the BP-Map that covers all possible values of BW and BC. RESULTS: When using CT value of adipose tissue as noise, the calculated SNR and CNR of optimal blending width and blending center were 123.22±41.73 and 9.00±3.52, respectively, by the BP-Map in the protocol D. By employing the CT value of back muscle as noise, the SNR and CNR of the best-blended images were 75.90±14.52 and 6.39±2.37, respectively. The subjective score of protocol D was 4.88±0.12. CONCLUSIONS: Compared to traditional blending methods, the BP-Map technique can determine the optimal blending parameter and provide the best-blended images with the highest SNR and CNR.