Journal of X-Ray Science and Technology - Volume 25, issue 6
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Impact Factor 2018: 1.381
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: Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether the image quality acquired by the digital radiography system can be improved with a modified convolutional neural network to generate high-resolution images with reduced noise from the original low-quality images. The experiment evaluated on a test dataset, which contains 5 X-ray images, showed that the proposed method outperformed the traditional methods (i.e., bicubic interpolation and 3D block-matching approach) as measured by peak…signal to noise ratio (PSNR) about 1.3 dB while kept highly efficient processing time within one second. Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise. Thus, this study indicated that applying this RTR convolutional neural network system was useful to improve image quality acquired by the digital radiography system.
Keywords: Digital radiography, enhancing image quality, convolutional neural network
Abstract: Estimation of the pleural effusion’s volume is an important clinical issue. The existing methods cannot assess it accurately when there is large volume of liquid in the pleural cavity and/or the patient has some other disease (e.g. pneumonia). In order to help solve this issue, the objective of this study is to develop and test a novel algorithm using B-spline and local clustering level set method jointly, namely BLL. The BLL algorithm was applied to a dataset involving 27 pleural effusions detected on chest CT examination of 18 adult patients with the presence of free pleural effusion. Study results showed…that average volumes of pleural effusion computed using the BLL algorithm and assessed manually by the physicians were 586 ml±339 ml and 604±352 ml, respectively. For the same patient, the volume of the pleural effusion, segmented semi-automatically, was 101.8% ±4.6% of that was segmented manually. Dice similarity was found to be 0.917±0.031. The study demonstrated feasibility of applying the new BLL algorithm to accurately measure the volume of pleural effusion.
Keywords: CT, pleural effusion, volume, B-spline, local clustering level set
Abstract: BACKGROUND: In regularized iterative reconstruction algorithms, the selection of regularization parameter depends on the noise level of cone beam projection data. OBJECTIVE: Our aim is to propose an algorithm to estimate the noise level of cone beam projection data. METHODS: We first derived the data correlation of cone beam projection data in the Fourier domain, based on which, the signal and the noise were decoupled. Then the noise was extracted and averaged for estimation. An adaptive regularization parameter selection strategy was introduced based on the estimated noise level. Simulation and real data studies were conducted for…performance validation. RESULTS: There exists an approximately zero-energy double-wedge area in the 3D Fourier domain of cone beam projection data. As for the noise level estimation results, the averaged relative errors of the proposed algorithm in the analytical/MC/spotlight-mode simulation experiments were 0.8%, 0.14% and 0.24%, respectively, and outperformed the homogeneous area based as well as the transformation based algorithms. Real studies indicated that the estimated noise levels were inversely proportional to the exposure levels, i.e., the slopes in the log-log plot were -1.0197 and -1.049 with respect to the short-scan and half-fan modes. The introduced regularization parameter selection strategy could deliver promising reconstructed image qualities. CONCLUSIONS: Based on the data correlation of cone beam projection data in Fourier domain, the proposed algorithm could estimate the noise level of cone beam projection data accurately and robustly. The estimated noise level could be used to adaptively select the regularization parameter.
Abstract: BACKGROUND: Cone-beam computed tomography (CBCT) is widely used in various medical imaging applications, including dental examinations. Dental CBCT images often suffer from motion artifacts caused by involuntary rigid motion of patients. However, earlier motion compensation studies are not applicable for dental CBCT systems using truncated detectors. OBJECTIVE: This study proposes a novel motion correction algorithm that can be applied for truncated dental CBCT images. METHODS: We propose a two-step method for motion correction. First, we estimate the relative displacement of each pair of opposite projections by finding the motion vector that maximizes the two-dimensional correlation coefficients…of the opposite projections. Second, we convert the relative displacement into the absolute coordinate motion that yields the highest image sharpness of the reconstruction image. Using the motion vectors in the absolute coordinate system, motion artifacts are then compensated by modifying the trajectory of the source and detector during the back-projection step of the image reconstruction process. RESULTS: In simulation, the proposed method successfully estimated the true relative displacement. After converting to the absolute coordinate motions, the motion-compensated image was close to the ground-truth image and exhibited a lower mean-square-error than that of the uncompensated image. The results from the real data experiment also confirmed that the proposed method successfully compensated for the motion artifacts. CONCLUSIONS: The experimental results confirmed that the proposed method was applicable to most dental CBCT systems using a truncated detector without any use of an additional motion tracking system nor prior knowledge.
Abstract: X-ray luminescence computed tomography (XLCT) is a hybrid imaging modality with the potential to achieve a spatial resolution up to several hundred micrometers for targets embedded in turbid media with a depth larger than several millimeters. In this paper, we report a high spatial resolution XLCT imaging system with a collimated superfine x-ray beam in imaging the deeply embedded targets. A collimator with a 100 micrometer pinhole was mounted in the front of a powerful x-ray tube to generate a superfine x-ray pencil beam with a beam diameter of 0.175 mm. For the phantom experiment of four capillary targets with an…edge-to-edge distance of 400 micrometers, we were able to reconstruct the targets in a depth of 5 mm successfully, which were validated with microCT images. We have further investigated the effect of different x-ray beam diameters on the reconstructed XLCT images with numerical simulations. Our results indicate that XLCT has the ability to image successfully multiple deeply embedded targets when the collimated x-ray beam diameter is less than or equal to the target edge-to-edge distance. Our numerical simulations also demonstrate that XLCT can achieve a spatial resolution of 200 micrometers for targets embedded at a depth of 5 mm if the scanning beam has a diameter of 100 micrometers.
Keywords: X-ray imaging, medical optics
instrumentation, tomography, turbid media
Abstract: Sparse-view imaging is a promising scanning approach which has fast scanning rate and low-radiation dose in X-ray computed tomography (CT). Conventional L1-norm based total variation (TV) has been widely used in image reconstruction since the advent of compressive sensing theory. However, with only the first order information of the image used, the TV often generates dissatisfactory image for some applications. As is widely known, image curvature is among the most important second order features of images and can potentially be applied in image reconstruction for quality improvement. This study incorporates the curvature in the optimization model and proposes a new…total absolute curvature (TAC) based reconstruction method. The proposed model contains both total absolute curvature and total variation (TAC-TV), which are intended for better description of the featured complicated image. As for the practical algorithm development, the efficient alternating direction method of multipliers (ADMM) is utilized, which generates a practical and easy-coded algorithm. The TAC-TV iterations mainly contain FFTs, soft-thresholding and projection operations and can be launched on graphics processing unit, which leads to relatively high performance. To evaluate the presented algorithm, both qualitative and quantitative studies were performed using various few view datasets. The results illustrated that the proposed approach yielded better reconstruction quality and satisfied convergence property compared with TV-based methods.
Keywords: Sparse-view image reconstruction, variational method, total absolute curvature, total variation, alternating direction method
Abstract: PURPOSE: To explore the value of low-dose CT perfusion imaging (LDCTPI) technology and its perfusion parameters in assessing response of neoadjuvant chemotherapy (NAC) in patients with advanced gastric cancer (AGC). METHODS: Thirty patients with AGC were studied prospectively by LDCTPI to measure two parameters including blood flow (BF) and blood volume (BV) of tumor area before and after chemotherapy, respectively. All of the patients received two courses of NAC and surgical resection of gastric tumor within one week after chemotherapy, and then obtained the result of postoperative pathology response for chemotherapy. The comparisons of BF and BV values…of AGC before and after chemotherapy were analyzed by paired-samples t -test, respectively; and the correlations between BF as well as BV decrease rates after NAC and the pathology response grade were analyzed by Spearman statistical test. Thirty patients were divided into effective and ineffective groups according to different pathology response grade. Comparisons of BF as well as BV decrease rates between effective and ineffective groups were analyzed by independent-samples t -test, respectively. Receiver operating characteristic (ROC) curves were used to determine the cutoff values of BF and BV decrease rates as evaluation indicators of AGC after NAC and calculate area under the curve (AUC). RESULTS: There were significant differences in BF and BV values of AGC between before and after NAC (p < 0.001), respectively, and there were obvious correlations between BF as well as BV decrease rates and pathology response grade (r = 0.660, p < 0.001; r = 0.706, p < 0.001), respectively. There were also significant differences in BF and BV decrease rates of AGC between effective and ineffective groups (P = 0.001), respectively. If BF decrease rate of 12.1% (AUC was 0.816, P = 0.005) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 84% were achieved, and if BV decrease rate of 32.8% (AUC was 0.844, P = 0.002) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 89% were achieved. CONCLUSIONS: BF and BV decrease rates have potential to be used as effective indicators to assess chemotherapy efficacy of AGC from the hemodynamics.
Abstract: Sparse-view Computed Tomography (CT) plays an important role in industrial inspection and medical diagnosis. However, the established reconstruction equations based on traditional Radon transform are ill-posed and obtain an approximate solution in the case of finite sampling angles. By contrast, Mojette transform is considered as the discrete geometry of the projection and reconstruction lattice. It determines the geometrical conditions for ensuring a unique solution instead of solving an ill-posed problem from the start. Therefore, Mojette transform results in theoretical exact image reconstruction in the discrete domain, and approximately gets the minimum number of projections, as well as their directions. However,…the reconstruction method utilizing Mojette transform is very sensitive to noise. To address the problem, the paper proposes a sparse-view Mojette inversion algorithm based on the minimum noise accumulation by selecting the prioritized projections for an image reconstruction. Experimental results show that the proposed method can effectively suppress the noise accumulation without increasing the number of projections and produce better reconstruction results than traditional corner-based Mojette inversion (CBI).
Keywords: Sparse-view computed tomography, Radon transform, Mojette transform, the priority-based subset of projections, minimum noise accumulation, accurate reconstruction
Abstract: Monochromatic-beam-based dynamic X-ray computed microtomography (CT) was developed to observe evolution of microstructure inside samples. However, the low flux density results in low efficiency in data collection. To increase efficiency, reducing the number of projections should be a practical solution. However, it has disadvantages of low image reconstruction quality using the traditional filtered back projection (FBP) algorithm. In this study, an iterative reconstruction method using an ordered subset expectation maximization-total variation (OSEM-TV) algorithm was employed to address and solve this problem. The simulated results demonstrated that normalized mean square error of the image slices reconstructed by the OSEM-TV algorithm was…about 1/4 of that by FBP. Experimental results also demonstrated that the density resolution of OSEM-TV was high enough to resolve different materials with the number of projections less than 100. As a result, with the introduction of OSEM-TV, the monochromatic-beam-based dynamic X-ray microtomography is potentially practicable for the quantitative and non-destructive analysis to the evolution of microstructure with acceptable efficiency in data collection and reconstructed image quality.