Journal of Biomedical Physics & Engineering
Volume:11 Issue: 5, Sep-Oct 2021

Estimation of eye lens dose is important in head computed tomography (CT) examination since the eye lens is a sensitive organ to ionizing radiation. The purpose of this study is to compare estimations of eye lens dose in head CT examinations using local size-specific dose estimate (SSDE) based on size-conversion factors of the American Association of Physicists in Medicine (AAPM) Report No. 293 with those based on size-conversion factors of the AAPM Report No. 220. This experimental study is conducted on a group of patients who had undergone nasopharyngeal CT examination. Due to the longitudinal (z-axis) dose fluctuation, the average global SSDE and average local SSDE (i.e. particular slices where the eyes are located) were investigated. All estimates were compared to the measurement results using thermo-luminescent dosimeters (TLDs). The estimated and measured doses were implemented for 14 patients undergoing nasopharyngeal CT examination. It was found that the percentage differences of the volume CT dose index (CTDIvol), average global SSDE based on AAPM No. 220 (SSDEo,g), average local SSDE based on AAPM No. 220 (SSDEo,l), average global SSDE based on AAPM No. 293 (SSDEn,g) and average local SSDE based on AAPM No. 293 (SSDEn,l) against the measured TLD doses were 22.5, 21.7, 15.0, 9.3, and 2.1%, respectively. All comparisons between dose estimates and TLD measurements gave p -values less than 0.001, except for SSDEn,l (p -value = 0.566). SSDE based on AAPM Report No. 293 can be used to accurately estimate eye lens radiation doses by performing the calculations on a number of specific slices containing the eyes.

Skin is a sensitive organ and should be spared in radiotherapy and irradiation of skin in radiotherapy can cause to acute and late skin effects such as erythema, desquamation, epilation, color change, or even necrosis. The aim of the present study is to do skin dosimetry in radiotherapy of parotid cancer using Gafchromic EBT3 radiochromic film. EBT3 radiochromic films were calibrated in 0.2-5 Gy dose range. This is an experimental study in the field of radiotherapy physics. Treatment planning was performed on a RANDO phantom for treatment of parotid cancer by a clinical oncologist. Based on the treatment planning, the skin dose at various points in the overlapping region of right anterior-oblique and right posterior-oblique fields were measured using EBT3 radiochromic film. The minimum and maximum skin doses in a fraction (with 2.0 Gy prescribed dose) were 0.50 Gy and 0.97 Gy, respectively. Based on these values, the total skin dose in 30 treatment fractions (for removed tumor) or in 35 treatment fractions (for unremoved tumor) was in the range of 15-33 Gy. Based on the skin dosimetry results of parotid cancer radiotherapy using EBT3 films, it is predicted that there will occur mild skin reactions and these reactions can be neglected due to being mild.

fNIRS is a useful tool designed to record the changes in the density of blood’s oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) molecules during brain activity. This method has made it possible to evaluate the hemodynamic changes of the brain during neuronal activity in a completely non-aggressive manner. The present study has been designed to investigate and evaluate the brain cortex activities during imagining of the execution of wrist motor tasks by comparing fMRI and fNIRS imaging methods. This novel observational Optical Imaging study aims to investigate the brain motor cortex activity during imagining of the right wrist motor tasks in vertical and horizontal directions. To perform the study, ten healthy young right-handed volunteers were asked to think about right-hand movements in different directions according to the designed movement patterns. The required data were collected in two wavelengths, including 845 and 763 nanometers using a 48 channeled fNIRS machine. Analysis of the obtained data showed the brain activity patterns during imagining of the execution of a movement are formed in various points of the motor cortex in terms of location. Moreover, depending on the direction of the movement, activity plans have distinguishable patterns. The results showed contralateral M1 was mainly activated during imagining of the motor cortex (p <0.05). The results of our study showed that in brain imaging, it is possible to distinguish between patterns of activities during wrist motion in different directions using the recorded signals obtained through near-infrared Spectroscopy. The findings of this study can be useful in further studies related to movement control and BCI.

Given the extensive use and preferred diagnostic method in common mammography tests for screening and diagnosis of breast cancer, there is concern about the increased dose absorbed by the patient due to the sensitivity of the breast tissue.

This study aims to evaluate the entrance surface air kerma (ESAK) before irradiation to the patient through its estimation.

In this descriptive paper, firstly, a phantom was used to measure some data, including ESAK, Kvp, mAs, HVL, and type of filter/target. Secondly, the MultiLayer Perceptron (MLP) neural network model was trained with Levenberg-Marquardt (LM) backpropagation training algorithm and finally, ESAK was estimated.

Based on results obtained from the program in different neuron numbers, it was found that the number of 35 neurons is the most optimal value, offering a regression coefficient of 95.7%. The Mean Squared Error (MSE) for all data was 0.437 mGy and accounting for 4.8% of the output range changes, predicting 95.2% accuracy in the present research.

Using neural networks in ESAK prediction, the method proposed in the present research leads to the possible ESAK estimation of patients before X-Ray. The results suggested that the regression coefficient represented 4.3% difference between the kerma measured by solid-state dosimeter in the radiation field and the value predicted in the research. In comparison with the Monte-Carlo simulation method, this method has better accuracy.

Radiation protection is an important principle in some wards of the hospital such as radiology, catheterization laboratory and operating room. Due to the increasing use of radiation in the operating room, there is a need to design an accurate and appropriate tool to evaluate the radiation protection capability of operating room personnel. This study aims to test the psychometric properties of a questionnaire on radiation protection capability. This cross-sectional study was conducted in two stages. The first stage was designing items based on the review of available literature, and the second stage was measuring the validity and reliability of the questionnaire using face validity and content validity Content Validity Index (CVI) and Content Validity Ratio (CVR). Then the questionnaire was filled out by 200 operating room nurses to evaluate the construct validity by Principal Component Analysis method. Reliability of the questionnaire was evaluated by test–retest and Cronbach’s alpha analysis method. Due to the results, test–retest correlation coefficient was 0.912, and Cronbach’s alpha coefficient was 0.824, indicating a desirable internal consistency. This study introduces a valid and reliable questionnaire for evaluating the radiation protection capability of operating room nurses.

Examination of the knee to assess the narrowing of the joint gap or joint space width (JSW) is commonly done by manually checking radiographs and measuring the JSW using a ruler. This study aims to compare manual and automatic measurements with the diagnosis of grade I and grade II knee osteoarthritis. In this cross-sectional study, 40 patients with the criteria for primary osteoarthritis (OA), aged 46 to 65 years old had knee OA grades of either I or II. The knee image was evaluated by a computer program and a radiologist manually viewing and measuring the JSW joint gap using a ruler. The results showed there were no differences in the measurement of JSW medial and JSW lateral manually in grade I and grade II knee OA, at p=0.605 and p=0.344, respectively. Whereas in the automatic measurements, there was a difference between JSW medial and lateral JSW in grade I and grade II knee OA, each with p<0.001. The manual JSW measurement between medial JSW and lateral JSW in grade I and II showed that the medial and lateral knee joints have a similar distance. In the automatic, the average value of measurement lateral JSW in OA grades I and II was greater than the medial JSW. Automatic measurements showed that both of medial and lateral JSW at grade II OA knee were narrower than the results at grade I. Automatic measurement of JSW results was more consistent than the manual measurement method.

A medical device is any instrument, apparatus, implement, machine, appliance, software, material, which is intended material, to be utilized, either alone or in combination, for medical purpose. These devices should work precisely and the maintenance program of them has also a key role to achieve this goal. Many of the maintenance programs have not considered important functional parameters such as equipment type, risk factors, and expert opinion.

The purpose of this study is to present a novel fuzzy method for medical device risk assessment. The obtained values for risk could be used to prioritize maintenance operations by considering allocation budget.

This experimental study aims to make a new application of Ordered-Weighted Average operator in aggregation of different parameters for calculating Risk Priority Number. This model is a fuzzy multi-criteria decision making approach based on risk maintenance framework for medical device prioritization.

A limited budget is one of the barrier in medical centers. The suggested framework presents a simple and reliable method to choose the best maintenance strategy for each kind of medical device by considering budget limitation. Based on obtained results from numerical model, defibrillators and surgical suction have respectively the highest and the lowest priority in mentioned example.

Risk prioritization of medical devices is valuable because the medical centers can prioritize maintenance operations and thereby to establish preference of maintenance strategy. Implementation of our proposed maintenance program has many effective results in medical center budgets.

Microbubbles are widely used in diagnostic ultrasound applications as contrast agents. Recently, many studies have shown that microbubbles have good potential for the use in therapeutic applications such as drug and gene delivery and opening of blood- brain barrier locally and transiently. When microbubbles are located inside an elastic microvessel and activated by ultrasound, they oscillate and induce mechanical stresses on the vessel wall. However, the mechanical stresses have beneficial therapeutic effects, they may induce vessel damage if they are too high. Microstreaming-induced shear stress is one of the most important wall stresses. The overall aim of this study is to simulate the interaction between confined bubble inside an elastic microvessel and ultrasound field and investigate the effective parameters on microstreaming-induced shear stress. In this Simulation study, we conducted a 2D finite element simulation to study confined microbubble dynamics, also we investigated both acoustical and bubble material parameters on microbubble oscillation and wall stress. Based on our results, for acoustic parameters in the range of therapeutic applications, the maximum shear stress was lower than 4 kPa. Shear stress was approximately independent from shell viscosity whereas it decreased by increasing the shell stiffness. Moreover, shear stress showed an increasing trend with acoustic pressure. Beside the acoustical parameters, bubble properties have important effects on bubble behavior so that the softer and larger bubbles are more appropriate for therapeutic application as they can decrease the required frequency and acoustic pressure while inducing the same biological effects.

QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an automatic QT-interval measurement is necessary. A new method for automatic QT-interval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0.575 and 0.290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99.70%, 99.78%, and 99.92%, respectively. The range of 95% LoA for the comparison between manual and the system’s QT-interval measurement is 0.216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.

If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.