Journal of Medical Signals and Sensors
Volume:12 Issue: 1, Jan-Mar 2022

COVID‑19 is a respiratory infection brought about by SARS‑COV‑2. Most of the patients contaminated by this pathogen are afflicted by respiratory syndrome with multiple stages ranging from mild upper respiratory involvement to severe dyspnea and acute respiratory distress syndrome cases. Keeping in mind the high sensitivity of computed tomography (CT) scan in detecting abnormalities, it became the number one modality in COVID‑19 diagnosis. A wide diversity of CT features can be found in COVID‑19 cases, which can be observed before the onset of clinical signs. The review article is aimed to highlight recent discrepancies in CT‑scan and chest X‑ray (CXR) characteristics between COVID‑19 and Middle East Respiratory Syndrome (MERS).

This review study was performed in the literature from the beginning of COVID‑19 until the middle of April 2021. For this reason, all relevant works through scientific citation websites such as Google Scholar, PubMed, and Web of Science have been investigated in the mentioned period.

COVID‑19 was more reproductive than MERS, while MERS was significantly higher in terms of mortality rate (COVID‑19: 2.3% and MERS: 34.4%). Signs of ground‑glass opacity (GGO), peripheral consolidation, and GGO accompanying with consolidation are the same signs CXR in both MERS and COVID‑19. Indeed, fever, cough, headache, and sore throat are the most symptoms in all studied patients.

Both COVID‑19 and MERS have the same imaging signs. The most similar chest CT findings are GGO, peripheral consolidation, and GGO superimposed by consolidation in both studied diseases, and no statistical differences were seen among the mean number of chest CT‑scans in MERS and COVID‑19 cases.

Reconstruction of high quality two dimensional images from fan beam computed tomography (CT) with a limited number of projections is already feasible through Fourier based iterative reconstruction method. However, this article is focused on a more complicated reconstruction of three dimensional (3D) images in a sparse view cone beam computed tomography (CBCT) by utilizing Compressive Sensing (CS) based on 3D pseudo polar Fourier transform (PPFT).

In comparison with the prevalent Cartesian grid, PPFT re gridding is potent to remove rebinning and interpolation errors. Furthermore, using PPFT based radon transform as the measurement matrix, reduced the computational complexity.

In order to show the computational efficiency of the proposed method, we compare it with an algebraic reconstruction technique and a CS type algorithm. We observed convergence in <20 iterations in our algorithm while others would need at least 50 iterations for reconstructing a qualified phantom image. Furthermore, using a fast composite splitting algorithm solver in each iteration makes it a fast CBCT reconstruction algorithm. The algorithm will minimize a linear combination of three terms corresponding to a least square data fitting, Hessian (HS) Penalty and l1 norm wavelet regularization. We named it PP‑based compressed sensing‑HS‑W. In the reconstruction range of 120 projections around the 360° rotation, the image quality is visually similar to reconstructed images by Feldkamp‑Davis‑Kress algorithm using 720 projections. This represents a high dose reduction.

The main achievements of this work are to reduce the radiation dose without degrading the image quality. Its ability in removing the staircase effect, preserving edges and regions with smooth intensity transition, and producing high‑resolution, low‑noise reconstruction results in low‑dose level are also shown.

The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images.

We fused two MRI and PET images automatically with a pretrained convolutional neural network (CNN, VGG19). First, the PET image was converted from red‑green‑blue space to hue‑saturation‑intensity space to save the hue and saturation information. We started with extracting features from images by using a pretrained CNN. Then, we used the weights extracted from two MRI and PET images to construct a fused image. Fused image was constructed with multiplied weights to images. For solving the problem of reduced contrast, we added the constant coefficient of the original image to the final result. Finally, quantitative criteria (entropy, mutual information, discrepancy, and overall performance [OP]) were applied to evaluate the results of fusion. We compared the results of our method with the most widely used methods in the spatial and transform domain.

The quantitative measurement values we used were entropy, mutual information, discrepancy, and OP that were 3.0319, 2.3993, 3.8187, and 0.9899, respectively. The final results showed that our method based on quantitative assessments was the best and easiest way to fused images, especially in the spatial domain.

It concluded that our method used for MRI‑PET image fusion was more accurate.

Temperament (Mizaj) determination is an important stage of diagnosis in Persian Medicine. This study aimed to evaluate thermal imaging as a reliable tool that can be used instead of subjective assessments.

The temperament of 34 participants was assessed by a PM specialist using standardized Mojahedi Mizaj Questionnaire (MMQ) and thermal images of the wrist in the supine position, the back of the hand, and their whole face under supervision of the physician were recorded. Thirteen thermal features were extracted and a classifying algorithm was designed based on the genetic algorithm and Adaboost classifier in reference to the temperament questionnaire.

The results showed that the mean temperature and temperature variations in the thermal images were relatively consistent with the results of MMQ. Among the three body regions, the results related to the image from Malmas were most consistent with MMQ. By selecting six of the 13 features that had the most impact on the classification, the accuracy of 94.7 ± 13.0, sensitivity of 95.7 ± 11.3, and specificity of 98.2 ± 4.2 were obtained.

The thermal imaging was relatively consistent with standardized MMQ and can be used as a reliable tool for evaluating warm/cold temperament. However, the results reveal that thermal imaging features may not be only main features for temperament classification and for more reliable classification, it needs to add some different features such as wrist pulse features and some subjective characteristics.

Advances in the medical applications of brain–computer interface, like the motor imagery systems, are highly contributed to making the disabled live better. One of the challenges with such systems is to achieve high classification accuracy.

A highly accurate classification algorithm with low computational complexity is proposed here to classify different motor imageries and execution tasks. An experimental study is performed on two electroencephalography datasets (Iranian Brain–Computer Interface competition [iBCIC] dataset and the world BCI Competition IV dataset 2a) to validate the effectiveness of the proposed method. For lower complexity, the common spatial pattern is applied to decrease the 64 channel signal to four components, in addition to increase the class separability. From these components, first, some features are extracted in the time and time– frequency domains, and next, the best linear combination of these is selected by adopting the stepwise linear discriminant analysis (LDA) method, which are then applied in training and testing the classifiers including LDA, random forest, support vector machine, and K nearest neighbors. The classification strategy is of majority voting among the results of the binary classifiers.

The experimental results indicate that the proposed algorithm accuracy is much higher than that of the winner of the first iBCIC. As to dataset 2a of the world BCI competition IV, the obtained results for subjects 6 and 9 outperform their counterparts. Moreover, this algorithm yields a mean kappa value of 0.53, which is higher than that of the second winner of the competition.

The results indicate that this method is able to classify motor imagery and execution tasks in both effective and automatic manners.

Quran memorizing causes a state of trance, which its result is the changes in the amplitude and time of P300 and N200 components in the event related potential (ERP) signal. Nevertheless, a limited number of studies that have examined the effects of Quran memorizing on brain signals to enhance relaxation and attention, and improve the lives of patients with autism and stroke, generally have not presented any analysis based on comparing structural differences relevant to features extracted from ERP signal obtained from the two groups of Quran memorizer and nonmemorizer by using the hybrid of graph theory and competitive networks.

In this study, we investigated structural differences relevant to the graph obtained from the weight of neural gas (NG) and growing NG (GNG) networks trained by features extracted from the ERP signal recorded from two groups during the PRM test. In this analysis, we actually estimated the ERP signal by averaging the brain background data in the recovery phase. Then, we extracted six features related to the power and the complexity of these signals and selected optimal channels in each of the features by using the t test analysis. Then, these features extracted from the optimal channels are applied for developing the NG and GNG networks. Finally, we evaluated different parameters calculated from graphs, in which their connection matrix was obtained from the weight matrix of the networks.

. The outcomes of this analysis show that increasing the power of low frequency components and the power ratio of low frequency components to high frequency components in the memorizers, which represents patience, concentration, and relaxation, is more than that of the nonmemorizers. These outcomes also show that the optimal channels in different features, which were often in frontal, peritoneal, and occipital regions, had a significant difference (P < 0.05). It is remarkable that two parameters of the graphs established based on two competitive networks, i.e. average path length and the average of the weights in the memorizers, were larger than the nonmemorizers, which means more data scattering in this group.

This condition in the mentioned graphs suggests that the Quran memorizing causes a significant change in ERP signals, so that its features have usually more scattering.

Nowadays, there has been a growing demand for low‑dose computed tomography (LDCT) protocols. CT has a critical role in the management of the diagnosis chain of pulmonary disease, especially in lung cancer screening. There have been introduced several dose reduction methods, however, most of them are time‑consuming, intricate, and vendor‑based strategies that are hardly used in clinics routinely. This study aims to evaluate the image quality and pulmonary nodule detectability of LDCT protocols that are feasible and easy implemented. Image quality was analyzed in a general quality control phantom (Gammex) and then in a manmade lung phantom with nodules‑equivalent objects.

This study was designed in a two steps, in the first step, a feasible low‑dose lung CT protocol was selected with quality assessment of accreditation phantom image. In the second step, the selected low‑dose protocol with an appropriate image quality was performed on a manmade lung phantom in which there were objects equivalent to the pulmonary nodule. Finally, image quality parameters of the phantom at the appropriate scan protocol were compared with the standard protocol.

A reduction of about 17% of kVp and 46% in tube current leads to dose reduction by about 70%. The contrast‑to‑noise ratio in the low‑dose protocol remained almost unchanged. The signal‑to‑noise ratio in the low‑dose protocol decreased by approximately 32%, and the noise level has increased by about 1.5 times. However, this reduction method hardly affected the detectability of nodules in man‑made pulmonary phantom.

Here, we demonstrated that the LDCT scan has an insignificant effect on the perception of lung nodules. In this study, patient dose in lung CT was reduced by modifying of kVp and mAs about approximately 70%. Hence, to step in toward low‑dose strategies in medical imaging clinics, using easy‑implemented and feasible low‑dose strategies may be helpful.

The objective of this study was to investigate the influence of iterative reconstruction (IR) algorithm on radiation dose and image quality of computed tomography (CT) scans of patients with malignant pancreatic lesions by designing a new protocol.

The pancreas CT was performed on 40 patients (23 males and 17 females) with a 160‑slice CT scan machine. The pancreatic parenchymal phase was performed in two stages: one with a usual dose of radiation and the other one after using a reduced dose of radiation. The images obtained with usual dose were reconstructed with Filtered Back Projection (FBP) method (Protocol A); and the images obtained with the reduced dose were reconstructed with both FBP (Protocol B) and IR method (Protocol C). The quality of images and radiation dose were compared among the three protocols.

Image noise was significantly lower with Protocol C (10.80) than with Protocol A (14.98) and Protocol B (20.60) (P < 0.001). Signal‑to‑noise ratio and contrast‑to‑noise ratio were significantly higher with Protocol C than with Protocol A and Protocol B (P < 0.001). Protocol A and Protocol C were not significantly different in terms of image quality scores. Effective dose was reduced by approximately 48% in Protocol C compared with Protocol A (1.20 ± 0.53 mSv vs. 2.33 ± 0.86 mSv, P < 0.001).

Results of this study showed that applying the IR method compared to the FBP method can improve objective image quality, maintain subjective image quality, and reduce the radiation dose of the patients undergo pancreas CT.

Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee’s cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker‑controlled watershed segmentation for the detection of Baker’s cyst in the knee’s joint MRI sagittal and axial T2‑weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker’s cyst can be clearly extracted from original images in axial and sagittal planes. The marker‑controlled watershed segmentation was able to detect Baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies.

Nuclear medicine technicians would receive unavoidable exposures during the preparation and administration of radiopharmaceuticals. Based on the staff dose monitoring, the dose reduction efficiencies of the radiation protection shields and the need to implement additional strategies to reduce the staff doses could be evaluated. In this study, medical staff doses during the preparation and administration of Tc‑99 m, I‑131, and Kr‑81 radiopharmaceuticals were evaluated. The dose reduction efficiencies of the lead apron and thyroid shield were also investigated. GR‑207 thermoluminescent dosimeter (TLD) chips were used for quantifying the medical staff doses. The occupational dose magnitudes were determined in five organs at risk including eye lens, thyroid, fingers, chest, and gonads. TLDs were located under and over the protective shields for evaluating the dose reduction efficiencies of the lead apron and thyroid shield. The occupational doses were normalized to the activities used in the working shifts. During preparation and injection of Tc‑99 m radiopharmaceutical, the average annual doses were higher in the chest (4.49 mGy) and eye lenses (4 mGy). For I‑131 radiopharmaceutical, the average annual doses of the point‑finger (15.8 mGy) and eye lenses (1.23 mGy) were significantly higher than other organs. During the preparation and administration of Kr‑81, the average annual doses of the point‑finger (0.65 mGy) and chest (0.44 mGy) were higher. The significant dose reductions were achieved using the lead apron and thyroid shield. The radiation protection shields and minimum contact with the radioactive sources, including patients, are recommended to reduce the staff doses.