Journal of X-Ray Science and Technology - Volume Pre-press, issue Pre-press
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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: Intraoperative computed tomography (iCT) system has been developed focusing on combining the advanced imaging techniques for the best imaging modality. However, the use of iCT system in the operating rooms is limited due to the lack of flexible mobility. OBJECTIVE: This study aims to develop a mobile iCT imaging system and assess its imaging performance in a phantom study. METHODS: The mobile iCT system with mecanum omni-directional wheels has three major components namely, a rotating gantry, a slip-ring and a stationary gantry. Performance of mecanum iCT system was evaluated using the…indices of signal-to-noise (SNR), contrast-to noise (CNR), and spatial resolution (MTF). Anatomical landmarks on phantom images were assessed using a 5-point scale (5 = definitely seen; 4 = probably seen; 3 = equivocal; 2 = probably not seen; and 1 = definitely not seen). RESULTS: The mecanum iCT system can be conveniently used for a whole-body scan under intraoperative conditions even in narrow operating rooms due to a smaller turning radius. The image quality of the mecanum iCT system was found to be acceptable for clinical applications (with SNR = 162.72, CNR = 134.29 and MTF = 694μ m). The diagnostic scores on the phantom images were ‘definitely seen’ value. CONCLUSIONS: The proposed mecanum iCT system achieved the improved flexible mobility and has potential to better serve as a useful imaging tool in the clinical intraoperative setting.
Abstract: BACKGROUND: Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) image is still a challenging work in computer-aided diagnosis (CAD). OBJECTIVE: This study aims to develop a novel automated algorithm for breast tumor detection based on deep learning. METHODS: We proposed a new deep learning network named One-step model which have one input and two outputs, the first one was the segmentation result and the other one was used for false-positive…reduction. The proposed One-step model includes three key components: Base-net, Seg-net, and Cls-net based on Anchor Box. The model chose DenseNet to construct Base-net, the decoder part of RefineNet as Seg-net, and connected several middle layers of Base-net and Seg-net to Cls-net. From the first output acquired by Base-net and Seg-net, the model detected a series of suspicious lesion regions. Then the second output from the Cls-net was used to recognize and reduce the false-positive regions. RESULTS: Experimental results showed that the new model achieved competitive detection result with 90.78% F1 score, which was 8.55% higher than Single Shot MultiBox Detector (SSD) method. In addition, running new model is also computational efficient and has comparative cost effect as SSD. CONCLUSIONS: We established a novel One-step model which improves location accuracy by generating more precise bounding box via Seg-net and removing false targets by another object detection network (Cls-net). On the other hand, a real-time detection of tumor is achieved by sharing the common Base-net. The experimental results showed that the new model performed well on various irregular and blurred ultrasound images. As a result, this study demonstrated feasibility of applying deep learning scheme to detect breast lesions depicting on US image.
Keywords: Automatic location breast tumor, deep learning, fully connected convolutional networks, segmentation, ultrasound image, Anchor Box
Abstract: BACKGROUND: Arterial embolism is a major cause of ischemic stroke. Currently, digital subtraction angiography (DSA) is the gold standard in clinical arterial embolization examinations. However, it is invasive and risky. OBJECTIVE: This study aims to longitudinally assess the progression of carotid artery embolism in middle cerebral artery occlusion animal model (MCAO) using magnetic resonance imaging (MRI) techniques. METHODS: Turbo spin echo (TSE), time of flight magnetic resonance angiography (TOF-MRA) and diffusion weighted magnetic resonance imaging (DWI) were used to evaluate the image characteristics of cerebral tissues at 1, 2, 3, 7, 14, 21 and 28 days…after MCAO microsurgery on Sprague-Dawley (SD) rats. Quantitative analysis was performed and compared in MCAO hemisphere and contralateral normal hemisphere. Furthermore, pathologic section using triphenyl tetrazolium chloride (TTC) stain was performed as well. RESULTS: TOF-MRA showed carotid signal void in the embolism side, which is evidence of artery occlusion. The used MRI techniques showed that edema gradually dissipated within one week, but there was no significant change afterwards. The time-varying signal intensity of MRI techniques in MCAO hemisphere changed significantly, but there were no significant changes in contralateral normal hemisphere. Cerebral injury was also confirmed by analysis of pathology images. CONCLUSIONS: The MCAO animal model was successfully established on SD rats using the microsurgery to assess arterial embolization of intracranial tissue injury.
Keywords: Time of flight magnetic resonance angiography, diffusion weighted magnetic resonance imaging, apparent diffusion coefficient, Sprague-Dawley rat, middle cerebral artery occlusion animal model, ischemic stroke
Abstract: BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE: To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS: We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity.…To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS: Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS: Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models.
Abstract: Anomalously high x-ray scattering at a wavelength of 0.154 nm by super-polished substrates of fused silica, which were etched by the argon ions with the energy of 300 eV, is detected. The scattering intensity increases monotonically with increasing of the etching depth. The effect is explained by the scattering on the volume inhomogeneities with the lateral size greater than 0.5μ m of the subsurface “damaged” layer. The concentration of volume inhomogeneities increases with the increase of the fluence of argon ions, but the concentration of implanted argon atoms in the layer quickly reaches the maximum value and then begins a trend of…going down. The thickness of the “damaged” layer is approximately equal to the penetration depth of the Ar atoms and can be directly determined from the x-ray specular reflection. It is shown that the presence of volume inhomogeneities of the subsurface “damaged” layer does not affect the geometric roughness of the surface. The observed effect imposes limitations on the usage of grazing incidence x-ray optics without reflective coatings and of the diffuse x-ray scattering (DXRS) method for studying the substrate roughness. A new method that potentially enables to evaluate the applicability of the DXRS method in practice is proposed.
Keywords: X-ray optics, X-Ray diffuse scattering, supersmooth surface, Ion
polishing, Roughness, Ion implantation
Abstract: BACKGROUND: Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images. OBJECTIVE: To propose and test a salient object detection method for BUS images. METHODS: BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in…the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula. RESULTS: Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33% ) using a dataset of 1000 BUS images. CONCLUSIONS: After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.
Abstract: OBJECTIVE: To explore the difference of 18F-FDG PET/CT images between the symptomatic and asymptomatic pulmonary tuberculosis, as well as the correlation between the standard uptake value (SUV) and the symptomatic/asymptomatic pulmonary tuberculosis. METHODS: A study dataset of 57 pulmonary tuberculosis cases was retrospectively assembled and analyzed. Among these cases, 30 were diagnosed having symptomatic pulmonary tuberculosis and 27 were asymptomatic pulmonary tuberculosis. PET/CT was performed in all 57 cases. The clinical data, CT images and PET/CT radioactive uptake data were analyzed using statistical data analysis software. RESULTS: All 57 cases showed radioactively high uptake, with the…maximum standard uptake value (SUVmax) of the lesion ranging from 1.60 to 27.30 and a mean value of 6.63±4.82. The symptomatic cases had an SUVmax of 8.76±4.97 and the asymptomatic cases had an SUVmax of 4.27±3.39. The SUVmax as well as singular or multiple lesions showed statistical differences between symptomatic and asymptomatic cases. CONCLUSION: The symptomatic pulmonary tuberculosis cases show significantly higher SUVmax than the asymptomatic cases. Based on the criteria of SUVmax greater than 2.0 to define active lesions, 100% of symptomatic cases might have active lesions while 70.4% of asymptomatic cases might have active lesions. Therefore, focused attention should be clinically paid on the asymptomatic cases of pulmonary tuberculosis to avoid miss diagnosis and delayed treatment.
Abstract: Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering…techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).
Abstract: OBJECTIVE: This study aims to assess the value of ultrasound real-time shear wave elastography (US-SWE) for evaluation of nonalcoholic fatty liver disease (NAFLD) in a rabbit model compared with multislice computed tomography (MSCT). MATERIAL AND METHODS: Twenty-six rabbits were fed with high-fat, high-cholesterol diet and six rabbits were fed with a standard diet. All rabbits were performed with MSCT and US-SWE at various time points to measure changes in liver parenchyma. The diagnostic efficiency of US-SWE was analyzed using receiver operating characteristics (ROC) curves compared with MSCT based on the liver pathology. RESULTS: The statistically significant…differences in the areas under the ROC curves between using MSCT and US-SWE modalities were detected to discriminate between normal vs. NAFLD or higher severity pathology. Similarly, for normal or NAFLD vs. borderline or NASH livers, statistically significant differences between using US-SWE and MSCT modalities were also detected for nonalcoholic steatohepatitis (NASH) vs. lower severity pathology. CONCLUSIONS: MSCT, but not US-SWE, had a better ability to differentiate normal or NAFLD livers from higher severity NAFLD livers. However, the diagnostic efficiency of US-SWE was superior to that of MSCT for differentiating NASH from normal or lower severity NAFLD.