Journal of X-Ray Science and Technology - Volume Preprint, issue Preprint
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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: OBJECTIVES: This work aims to explore more accurate pixel-driven projection methods for iterative image reconstructions in order to reduce high-frequency artifacts in the generated projection image. METHODS: Three new pixel-driven projection methods namely, small-pixel-large-detector (SPLD), linear interpolation based (LIB) and distance anterpolation based (DAB), were proposed and applied to reconstruct images. The performance of these methods was evaluated in both two-dimensional (2D) computed tomography (CT) images via the modified FORBILD phantom and three-dimensional (3D) electron paramagnetic resonance (EPR) images via the 6-spheres phantom. Specifically, two evaluations based on projection generation and image reconstruction were performed. For projection generation,…evaluation was using a 2D disc phantom, the modified FORBILD phantom and the 6-spheres phantom. For image reconstruction, evaluations were performed using the FORBILD and 6-spheres phantom. During evaluation, 2 quantitative indices of root-mean-square-error (RMSE) and contrast-to-noise-ratio (CNR) were used. RESULTS: Comparing to the use of ordinary pixel-driven projection method, RMSE of the SPLD based least-square algorithm was reduced from 0.0701 to 0.0384 and CNR was increased from 5.6 to 19.47 for 2D FORBILD phantom reconstruction. For 3D EPRI, RMSE of SPLD was also reduced from 0.0594 to 0.0498 and CNR was increased from 3.88 to 11.58. In addition, visual evaluation showed that images reconstructed in both 2D and 3D images suffered from high-frequency line-shape artifacts when using the ordinary pixel-driven projection method. However, using 3 new methods all suppressed the artifacts significantly and yielded more accurate reconstructions. CONCLUSIONS: Three proposed pixel-driven projection methods achieved more accurate iterative image reconstruction results. These new and more accurate methods can also be easily extended to other imaging modalities. Among them, SPLD method should be recommended to 3D and four dimensional (4D) EPR imaging.
Keywords: Accurate pixel-driven projection, iterative image reconstruction, computed tomography, electron paramagnetic resonance imaging
Abstract: The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features,…we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.
Abstract: This study aims to investigate and test a new image reconstruction algorithm applying to the low-signal projections to generate high quality images by reducing the artifacts and noise in the cone-beam computed tomography (CBCT). For the low-signal and noisy projections, a multiple sampling method is first utilized in projection domain to suppress environmental noise, which guarantees the accuracy of the data for reconstruction, simultaneously. Next, a fuzzy entropy based method with block matching 3D (BM3D) filtering algorithm is employed to improve the image quality to reduce artifacts and noise in image domain. Then, simulation studies on polychromatic spectrum were performed…to evaluate the performance of the proposed new algorithm. Study results demonstrated significant improvement in the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images reconstructed using the new algorithm. SNRs and CNRs of the new images were averagely 40% and 20% higher than those of the previous images reconstructed using the traditional algorithms, respectively. As a result, since the new image reconstruction algorithm effectively reduced the artifacts and noise, and produced images with better contour and grayscale distribution, it has the potential to improve image quality using the original CBCT data with the low and missing signals.
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