Journal of X-Ray Science and Technology - Volume 24, issue 3
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Impact Factor 2020: 1.342
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: The purpose of this study was to compare the dosimetric characteristics for protection of the hippocampus between dual arc VMAT (volumetric modulated arc therapy) and 7 fields intensity-modulated radiation therapy (7F-IMRT) for patients with brain metastases from lung cancer under the whole brain radiotherapy. Methods: Based on ten cases with brain metastases from lung cancer, two types of radiotherapy plans were designed, namely, dual arc VMAT and 7F-IMRT. Provided that the clinical requirements were satisfied, the comparisons of target dose distribution, conformity index (CI), homogeneity index (HI), dose of organs at risk (OARs), monitor units (MU)…and treatment time between dual arc VMAT and 7F-IMRT were investigated for their dosimetric difference. Results: Both treatment plans met the requirements of clinical treatments. However, the PTV-HA conformity and homogeneity of dual arc VMAT were superior to those of 7F-IMRT (P < 0.05). As to OARs, the mean maximum doses (Dmax ) of hippocampus, eyes and optic nerves in the dual arc VMAT plan were all lower than those in 7F-IMRT plan (P < 0.05), but the result had no statistical significance (P < 0.05) for the maximum dose of lens. Compared with 7F-IMRT, dual arc VMAT reduced the average number of MU by 67% and the average treatment time by 74%. Therefore, treatment time was shortened by dual arc VMAT. Conclusion: With regards to the patients with brain metastases from lung cancer under the whole brain radiotherapy, the PTV-HA conformity and homogeneity of dual arc VMAT were superior to those of 7F-IMRT under the precise of meeting the clinical requirements. In addition, dual arc VMAT remarkably reduced the irradiation dose to OARs (hippocampus, eyes and optic nerves), MU and treatment time, as well, guaranteed patients with better protection.
Abstract: Pattern classification has been increasingly used in functional magnetic resonance imaging (fMRI) data analysis. However, the classification performance is restricted by the high dimensional property and noises of the fMRI data. In this paper, a new feature selection method (named as “NMI-F”) was proposed by sequentially combining the normalized mutual information (NMI) and fisher discriminant ratio. In NMI-F, the normalized mutual information was firstly used to evaluate the relationships between features, and fisher discriminant ratio was then applied to calculate the importance of each feature involved. Two fMRI datasets (task-related and resting state) were used to test the proposed method.…It was found that classification base on the NMI-F method could differentiate the brain cognitive and disease states effectively, and the proposed NMI-F method was prior to the other related methods. The current results also have implications to the future studies.
Keywords: Pattern classification, feature selection, functional magnetic resonance imaging (fMRI), normalized mutual information (NMI), fisher discriminant ratio
Abstract: Non-local means algorithm can remove image noise in a unique way that is contrary to traditional techniques. This is because it not only smooths the image but it also preserves the information details of the image. However, this method suffers from high computational complexity. We propose a multi-scale non-local means method in which adaptive multi-scale technique is implemented. In practice, based on each selected scale, the input image is divided into small blocks. Then, we remove the noise in the given pixel by using only one block. This can overcome the low efficiency problem caused by the original non-local means…method. Our proposed method also benefits from the local average gradient orientation. In order to perform evaluation, we compared the processed images based on our technique with the ones by the original and the improved non-local means denoising method. Extensive experiments are conducted and results shows that our method is faster than the original and the improved non-local means method. It is also proven that our implemented method is robust enough to remove noise in the application of neuroimaging.
Keywords: Non-local means, denoising, adaptive multi-scale, average gradient orientation, neuroimaging
Abstract: Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness…to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity.
Keywords: Brain tissue segmentation, magnetic resonance images, fuzzy C-means, grayscale and spatial information, Gaussian radial basis kernel, image histogram