Journal of biomedical physics & engineering 

About: Journal of biomedical physics & engineering is an academic journal published by Shiraz University of Medical Sciences. The journal publishes majorly in the area(s): Medicine & Dosimetry. It has an ISSN identifier of 2251-7200. It is also open access. Over the lifetime, 576 publications have been published receiving 3016 citations.

The mechanisms of radiation-induced bystander effect.

Abstract: The radiation-induced bystander effect is the phenomenon which non-irradiated cells exhibit effects along with their different levels as a result of signals received from nearby irradiated cells. Responses of non-irradiated cells may include changes in process of translation, gene expression, cell proliferation, apoptosis and cells death. These changes are confirmed by results of some In-Vivo studies. Most well-known important factors affecting radiation-induced bystander effect include free radicals, immune system factors, expression changes of some genes involved in inflammation pathway and epigenetic factors.

Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

Abstract: Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is needed for the neuroimaging applications. Methods: In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i) default segmentation, ii) SPM8 New-segmentation and iii) modified version using hidden Markov random field as implemented in SPM8-VBM toolbox. Results: The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results. Conclusion: A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

Covid-19 tragic pandemic: Concerns over unintentional “directed accelerated evolution” of novel coronavirus (sars-cov-2) and introducing a modified treatment method for ards

Abstract: Global health authorities are trying to work out the current status of the novel coronavirus (COVID-19) outbreak and explore methods to reduce the rate of its transmission to healthy individuals. In this viewpoint we provide insights concerning how health care professionals can unintentionally shift the novel coronavirus type to more drug-resistant forms. It is worth noting that viruses usually have different sensitivities to physical and chemical damaging agents such antiviral drugs, UV and heat ranging from extremely sensitive (ES) to extremely resistant (ER) based on a bell-shaped curve. Given this consideration, the widespread infection of people with such ER viruses would be a real disaster. Here, we introduce a modified treatment method for COVID-19-associated pneumonia. In this proposed method, COVID-19 patients will receive a single dose of 100, 180 or 250 mSv X-ray radiation that is less than the maximum annual radiation dose of the residents of high background radiation areas of Ramsar that is up to 260 mSv. In contrast with antiviral drugs, a single dose of either 100, 180 or 250 mSv of low LET X-rays cannot exert a significant selective pressure on the novel coronavirus (SARS-CoV-2) and hence does not lead to directed accelerated evolution of these viruses. Moreover, Low Dose Radiation (LDR) has the capacity of modulating excessive inflammatory responses, regulating lymphocyte counts, and controling bacterial co-infections in patients with COVID-19.

Transfer learning-based automatic detection of coronavirus disease 2019 (COVID-19) from chest x-ray images

Abstract: Background Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required. Objective This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. Material and methods In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. Results Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. Conclusion The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further.