Journal of X-Ray Science and Technology - Volume 25, issue 2
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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: Real-time monitoring and amendment of patient position is important for the radiotherapy. However, using electronic portal imaging device (EPID) and cone beam computer tomography (CBCT) in the clinical practice generate different degrees of delay, so that they cannot achieve the purpose of real-time application. Meanwhile, a few products come with the function of the real-time monitoring and amendment, such as CyberKnife, which is too expensive for the common people. The objective of this study is to develop and test a novel independent system to monitor treatment center and amend the position of patient, which is applicable to most accelerators, based…on binocular location. The system monitors the treatment center by tracking the markers attached to the patient. Once the treatment center shifts, the system uses the magic finger, which is developed to control the treatment bet automatically to adjust the treatment bed position. To improve the monitoring accuracy, we trained the data collected from the clinic based on SVM (Support Vector Machine). Thus, the training results assist users to adjust the feasible degree of the monitoring. The experiment results showed that using this new monitoring system, the monitoring resolution reached 0.5 mm, and the error ratio of the judgment was less than 1.5%.
Keywords: Real-time monitoring, binocular system, magic finger, Support Vector Machine
Abstract: BACKGROUND AND OBJECTIVE: Effective treatment of Uterus Cervical Carcinoma (UCC) rely heavily on the precise pre-surgical staging. The conventional International Federation of Gynecology and Obstetrics (FIGO) system based on clinical examination is being applied worldwide for UCC staging. Yet its performance just appears passable. Thus, this study aims to investigate the value of applying Magnetic Resonance Imaging (MRI) with clinical examination in staging of UCC. MATERIALS AND METHODS: A retrospective dataset involving 164 patients diagnosed with UCC was enrolled in this study. The mean age of this study population was 46.1 years (range, 28–#x2013;75 years). All patients…underwent operations and UCC types were confirmed by pathological examinations. The tumor stages were determined by two experienced Gynecologist independently based on FIGO examinations and MRI. The diagnostic results were also compared with the post-operative pathologic reports. Statistical data analysis on diagnostic performance was then done and reported. RESULTS: The study results showed that the overall accuracy of applying MRI in UCC staging was 82.32%, while using FIGO staging method, the staging accuracy was 59.15%. CONCLUSIONS: MRI is suitable to evaluate tumor extent with high accuracy, and it can offer more objective information for the diagnosis and staging of UCC. Compared with clinical examinations based on FIGO, MRI illustrated relatively high accuracy in evaluating UCC staging, and is worthwhile to be recommended in future clinical practice.
Keywords: Magnetic Resonance Imaging, diagnosis, Uterus Cervical Carcinoma
Abstract: Simulation of blood flow in a stenosed artery using Smoothed Particle Hydrodynamics (SPH) is a new research field, which is a particle-based method and different from the traditional continuum modelling technique such as Computational Fluid Dynamics (CFD). Both techniques harness parallel computing to process hemodynamics of cardiovascular structures. The objective of this study is to develop and test a new robust method for comparison of arterial flow velocity contours by SPH with the well-established CFD technique, and the implementation of SPH in computed tomography (CT) reconstructed arteries. The new method was developed based on three-dimensional (3D) straight and curved arterial…models of millimeter range with a 25% stenosis in the middle section. In this study, we employed 1,000 to 13,000 particles to study how the number of particles influences SPH versus CFD deviation for blood-flow velocity distribution. Because further increasing the particle density has a diminishing effect on this deviation, we have determined a critical particle density of 1.45 particles/mm2 based on Reynolds number (Re = 200) at the inlet for an arterial flow simulation. Using this critical value of particle density can avoid unnecessarily big computational expenses that have no further effect on simulation accuracy. We have particularly shown that the SPH method has a big potential to be used in the virtual surgery system, such as to simulate the interaction between blood flow and the CT reconstructed vessels, especially those with stenosis or plaque when encountering vasculopathy, and for employing the simulation results output in clinical surgical procedures.
Abstract: PURPOSE: Thoracic aortic dissection (TAD) is considered one of the most catastrophic and non-traumatic cardiovascular diseases associated with high morbidity and mortality rates in clinical treatment. The purpose of this paper is to investigate the pulsatile hemodynamics changes throughout a cardiac cycle in a Stanford Type B TAD model with the aid of computational fluid dynamics (CFD) method. METHODS: A patient-specific dissected aorta geometry was reconstructed from the three-dimensional (3D) computed tomography angiography (CTA) scanning. The realistic time-dependent pulsatile boundary conditions were prescribed for our 3D patient-specific TAD model. Blood was considered to be an incompressible, Newtonian…fluid. The aortic wall was assumed to be rigid, and a no-slip boundary condition was applied at the wall. CFD simulations were processed using the finite volume (FV) method to investigate the pulsatile hemodynamics in terms of blood flow velocity, aortic wall pressure, wall shear stress and flow vorticity. In the experiments, blood velocity, pressure, wall shear stress and vorticity distributions were analyzed qualitatively and quantitatively. RESULTS: The experimental results demonstrated a high wall shear stress and strong vertical flow at dissection initiation. The results also indicated that wall shear progressed along the false lumen, which is a possible cause of blood flow between aortic wall layers.
Abstract: OBJECTIVE: To retrospectively reappraise characteristics of the electro-clinical seizure semiology of the bilateral asymmetric tonic seizure (BATS) in the patients with supplementary sensorimotor area (SSMA) epilepsy. METHODS: From the retrospective analysis of the pre- and post-operative Magnetic Resonance Imaging (MRI) data involving 386 patients who received epilepsy surgery, 123 BATS were identified meeting the clinical criteria and included in the study. For comparison in four extremities involvement, limbs were paired and comparatively evaluated between the contralateral and ipsilateral sides, proximal and distal segments, and upper and lower limbs. For evaluation of sequential events, each tonic phase of…the BATS was chronologically divided into 10 equal epochs. In each epoch, distribution of tonic events in 4 extremities and axes was visually evaluated and comparatively analyzed. RESULTS: Asymmetric tonic posturing was the most constant findings in 6 patients, whose upper limbs contralateral to epileptogenic cortex were kept in abduction in all 123 (100%) seizures and extension in 118 (95.9%) seizures. This type of asymmetry became visible and remained stable in the initial three epochs of the tonic phase in 107 out of 123 (87.0%) seizures. In each epoch, especially the initial one, the contralateral upper limbs were involved more frequently than those ipsilateral to the epileptogenic cortex (p < 0.05). By pairwise comparison, an earlier involvement of the contralateral side to epileptogenic cortex was visually observed in 53 out of 280 (18.9%) limb pairs, in which the ipsilateral limbs were preceded by the contralateral ones 4.6 (0.1–16.0) seconds earlier. Both of the proximal and distal segments were simultaneously involved in 260 out of 298 (87.2%) limb pairs, although the former were 4.3 (0.5–16.0) earlier than the latter in 34 out of 298 (11.4%) limb pairs. CONCLUSIONS: This study demonstrated that by studying the restricted epileptogenic lesion limited to pure SSMA, unilateral extension and abduction posturing in upper limb were the most prominent and valuable sign for the lesion lateralization in SSMA neurosurgery decision-making.
Keywords: Electro-clinical semiology, bilateral asymmetric tonic seizure, supplementary sensorimotor area epilepsy (SSMA), image guidance diagnosis, Magnetic Resonance Imaging (MRI)
Abstract: BACKGROUND: Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE: In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and…support vector machine (SVM). METHODS: New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. RESULTS: Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. CONCLUSIONS: MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.
Abstract: BACKGROUND: Surface electromyography (sEMG) signal is the combined effect of superficial muscle EMG and neural electrical activity. In recent years, researchers did large amount of human-machine system studies by using the physiological signals as control signals. OBJECTIVE: To develop and test a new multi-classification method to improve performance of analyzing sEMG signals based on public sEMG dataset. METHODS: First, ten features were selected as candidate features. Second, a genetic algorithm (GA) was applied to select representative features from the initial ten candidates. Third, a multi-layer perceptron (MLP) classifier was trained by the selected optimal features.…Last, the trained classifier was used to predict the classes of sEMG signals. A special graphics processing unit (GPU) was used to speed up the learning process. RESULTS: Experimental results show that the classification accuracy of the new method reached higher than 90%. Comparing to other previously reported results, using the new method yielded higher performance. CONCLUSIONS: The proposed features selection method is effective and the classification result is accurate. In addition, our method could have practical application value in medical prosthetics and the potential to improve robustness of myoelectric pattern recognition.
Keywords: Surface electromyography signal, features selection, genetic algorithm, multi-layer perception, biological signal processing, GPU acceleration
Abstract: BACKGROUND: The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. OBJECTIVE: To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. METHODS: A window-based data acquisition method was presented to…extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. RESULTS: The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. CONCLUSIONS: The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals’ energy appropriately. The classical machine learning classifiers all performed well by using these features.
Abstract: BACKGROUND: Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. OBJECTIVE: To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. METHODS: In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By…introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. RESULTS: Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. CONCLUSION: The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.
Keywords: Brain tumor segmentation, evaluation of tumor segmentation accuracy, Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS), Singed Pressure Force (SPF)