Journal of Biomedical Physics & Engineering
Volume:11 Issue: 4, Jul-Aug 2021

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

The Total Skin Electron Therapy (TSET) targets the whole of skin using 6 to 10 MeV electrons in large field size and large Source to Surface Distance (SSD). Treatment in sleeping position leads to a better distribution of dose and patient comfort. This study aims to investigate the uniformity of absorbed dose in the sleeping Stanford technique on the Rando phantom using dosimetry. It is an experimental study which was performed using 6 MeV electron irradiation produced by Varian accelerator in the AP and PA positions with gantry angles of 318/3, 0 and 41/5 degrees, and RAO, LAO, RPO and LPO with 291/4 gantry angle and 45 degrees of collimator angle in the sleeping position. The results show that the dose uniformity achieved in this technique is in the range of (100 ± 25%) and, the dose accuracy was 6%. Total Skin Electron Therapy (TSET) technique in sleeping position is very suitable for elderly and disabled patients, and meets the required dose uniformity. Furthermore, the use of a flattening filter is recommended for the more dose distribution uniformity.

Selective targeting of malignant cells is the ultimate goal of anticancer studies around the world. There are some modalities for cancer therapy devastating tumor size and growth rate, meanwhile attacking normal cells. Utilizing appropriate ligands, like folate, allow the delivery of therapeutic molecules to cancer cells selectively. There are a variety of photosensitizers, like gold nanorods (GNRs), capable of absorbing the energy of light and converting it to heat, evidently build a photothermal procedure for cancer therapy. To develop a one-step approach for calculating the temperature distribution by solving the heat transfer equation with multiple heat sources originating from NIR laser-exposed GNRs. In this experimental study, we simulated NIR laser heating process in a single cancer cell, with and without incubation with folate conjugated PEG-GNRs. This simulation was based on a real TEM image from an experiment with the same setup. An in vitro experiment based on aforesaid scenario was performed to validate the simulated model in practice. According to the simplifications due to computational resource limits, the resulting outcome of simulation showed significant compatibility to the supporting experiment. Both simulation and experimental studies showed a similar trend for heating and cooling of the cells incubated with GNRs and irradiated by NIR laser (5 min, 1.8 W/cm2). It was observed that temperature of the cells in microplate reached 53.6 °C when irradiated by laser. This new method can be of great application in developing a planning technique for treating tumors utilizing GNP-mediated thermal therapy.

The diagnostic reference level (DRL) is measured with different methods in the common Computed tomography (CT) exams, but it has not been measured through the size-specific dose estimate (SSDE) method in Iran, yet. This study aimed to calculate the local DRL (LDRL) using the new quality control-based dose survey method (QC) and patients’ effective diameter (MQC) and compare them with a data collection method (DC) as well as local national DRLs (NDRL). In this cross-sectional study, LDRL, based on the third quartile of volumetric computed tomography dose index (CTDIvol) and dose length product (DLP) values, was calculated for the four common CT examinations in four CT scan centers affiliated with Shiraz University of Medical Sciences by DC, QC and MQC methods. The CTDIvol of each patient for each CT exam calculated with three methods was compared with paired t-test. Also, the LDRL using MQC method was compared with other national DRL studies. There was a significant difference between the CTDIvol values calculated with MQC and QC in all four examinations (P <0.001). The LDRL based on CTDIvol obtained by the MQC method for head, sinus, chest, abdomen, and pelvis were (50, 18, 15, 19) mGy, respectively, and the calculated DLP values were also (735, 232, 519, 984) mGy.cm. In MQC, LDRL based on CTDIvol was calculated with a mean difference percentage of (19.2 ± 11.6)% and (27.1 ± 8.1)% as compared to the QC and DC methods, respectively. This difference resulted from the use of the SSDE method and dose accuracy in the QC dose survey.

Radiotherapy plays an important role in the treatment of breast cancer. In the process of radiotherapy, the underling lung tissue receives higher doses from treatment field, which led to incidence of radiation pneumonitis. The present study aims to evaluate the predictive factors of radiation pneumonitis and related changes in pulmonary function after 3D-conformal radiotherapy of breast cancer. In prospective basis study, thirty-two patients with breast cancer who received radiotherapy after surgery, were followed up to 6 months. Respiratory symptoms, lung radiologic changes and pulmonary function were evaluated. Radiation pneumonitis (RP) was graded according to common terminology criteria for adverse events (CTCAE) version 3.0. Dose-volume parameters, which included percentage of lung volume receiving dose of d Gy (V5-V50) and mean lung dose (MLD), were evaluated for RP prediction. Pulmonary function evaluated by spirometry test and changes of FEV1 and FVC parameters. Eight patients developed RP. Among the dose-volume parameters, V10 was associated to RP incidence. When V10<40% and V10≥40% the incidences of RP were 5.26% and 61.54%, respectively. The FEV1 and FVC had a reduction 3 and 6 months after radiotherapy, while only FEV1 showed significant reduction. The FEV1 had more reduction in the patients who developed RP than patients without RP (15.25±3.81 vs. 9.2±0.93). Pulmonary function parameters, especially FEV1, significantly decreased at 3 and 6 months after radiotherapy. Since most patients with breast cancer who developed RP did not show obvious clinical symptoms, so spirometry test is beneficial to identify patients with risk of radiation pneumonitis.

Experimental studies have shown that infiltration of inflammatory cells as well as upregulation of some cytokines play a central role in the development of late effects of ionizing radiation in heart tissues. Evidences have shown that an increased level of TGF-β has a direct correlation with late effects of exposure to ionizing radiation such as chronic oxidative stress and fibrosis. Recent studies have shown that TGF-β, through upregulation of pro-oxidant enzymes such as NOX2 and NOX4, promotes continuous ROS production and accumulation of fibrosis. In present study, we aimed to evaluate the expression of NOX2 and NOX4 signaling pathways as well as possible modulatory effects of melatonin on the expression of these genes. In this experimental study, four groups of 20 rats (5 in each) were used as follows; G1: control; G2: melatonin; G3: radiation; G4: radiation + melatonin. 100 mg/kg of melatonin was administrated before irradiation of heart tissues with 15 Gy gamma rays. 10 weeks after irradiation, heart tissues were collected for real-time PCR. Results showed a significant increase in the expression of TGF-β, Smad2, NF-kB, NOX2 and NOX4. The upregulation of NOX2 was more obvious by 20-fold compared to other genes. Except for TGF-β, melatonin could attenuate the expression of other genes. This study indicated that exposure of rat’s heart tissues to radiation leads to upregulation of TGF-β-NOX4 and TGF-β-NOX2 pathways. Melatonin, through modulation of these genes, may be able to alleviate radiation-induced chronic oxidative stress and subsequent consequences.

The importance of cellular dosimetry in both diagnostic and radiation therapy is becoming increasingly recognized. This study aims to compare surviving fractions, which were predicted using Geant4 and contained three types of cancer cell lines exposed to 188Re with the experimentally surviving fraction determined by MTT assay. In this comparative study, Geant4 was used to simulate the transport of electrons emitted by 188Re from the cell surface, cytoplasm, nucleus or medium around the cells. The nucleus dose per decay (S-value) was computed for models of single cell and random monolayer cell. Geant4-computed survival fraction (SF) of cancer cells exposed to 188Re was compared with the experimental SF values of MTT assay. For single cell model, Geant4 S-values of nucleus-to-nucleus were consistent with values reported by Goddu et al. (ratio of S-values by analytical techniques vs. Geant4 = 0.811–0.975). Geant4 S-values of cytoplasm and cell surface to nucleus were relatively comparable to the reported values (ratio =0.914–1.21). For monolayer model, the values of SCy→N and SCS→N, were greater compared to those for model of single cell (2%–25% and 4%–38% were larger than single cell, respectively). The Geant4 predicted SF for monolayer MCF7, HeLa and A549 cells was in agreement with the experimental data in 10μCi activity (relative error of 2.29%, 2.69% and 2.99%, respectively). Geant4 simulation with monolayer cell model showed the highest accuracy in predicting the SF of cancer cells exposed to homogeneous distribution of 188Re in the medium.

It is recommended for each set of radiation data and algorithm that subtle deliberation is done regarding dose calculation accuracy. Knowing the errors in dose calculation for each treatment plan will result in an accurate estimate of the actual dose achieved by the tumor. This study aims to evaluate the equivalent path length (EPL) and equivalent tissue air ratio (ETAR) algorithms in radiation dose calculation. In this experimental study, the TEC-DOC 1583 guideline was used. Measurements and calculations were obtained for each algorithm at specific points in thorax CIRS phantom for 6 and 18 MVs and results were compared. In the EPL, calculations were in agreement with measurements for 27 points and differences between them ranged from 0.1% to 10.4% at 6 MV. The calculations were in agreement with measurements for 21 points and differences between them ranged from 0.4% to 13% at 18 MV. In ETAR, calculations were also in consistent with measurements for 21 points, and differences between them ranged from 0.1% to 9% at 6 MV. Moreover, for 18 MV, the calculations were in agreement with measurements for 17 points and differences between them ranged from 0% to 11%. For the EPL algorithm, more dose points were in consistent with acceptance criteria. The errors in the ETAR were 1% to 2% less than the EPL. The greatest calculation error occurs in low-density lung tissue with inhomogeneities or in high-density bone. Errors were larger in shallow depths. The error in higher energy was more than low energy beam.

Computed tomography (CT) is a routine procedure for diagnosing using ionization radiation which has hazardous effects especially on sensitive organs. The aim of this study was to quantify the dose reduction effect of lead apron shielding on the testicular region during routine chest CT scans. In this measurement study, the routine chest CT examinations were performed for 30 male patients with common lead aprons folded and positioned in testis regions. The patient’s mean body mass index (BMI) was 26.2 ± 4.6 kg/m2. To calculate the doses at testis region, three thermoluminescent dosimeters (TLD-100) were attached at the top surface of the apron as an indicator of the doses without shielding, and three TLDs under the apron for doses with shielding. The TLD readouts were compared using SPSS software (Wilcoxon test) version 16. The radiation dose in the testicular regions was reduced from 0.46 ± 0.04 to 0.20 ± 0.04 mGy in the presence of lead apron shielding (p < 0.001), the reduction was equal to 56%. Furthermore, the heritable risk probability was obtained at 2.0 ×10-5 % and 4.6 ×10-5 % for the patients using the lead apron shield versus without shield, respectively. Applying common lead aprons as shielding in the testis regions of male patients undergoing chest CT scans can reduce the radiation doses significantly. Therefore, this shield can be recommended for routine chest CT examinations.

Based on thermal temperatures around the breast, thermography is considered a promising approache providing information about the condition of the breast without any side effects. Using thermography, breast screening is highly dependent on the process of heat recognition. The angular effects in the process of thermal patterns recognition can increase false detection. The effect can be observed in breasts with growing mammary glands. This study aims to develop a system to identify breast conditions through analysis of temperature and thermal patterns. In this experimental study, analysis of thermal patterns are performed using the Canny method, specifically detection of anomalies in the breast. Twenty-four Wistar female rats were used as experimental animal models with group 1 (normal), group 2 (induced with DMBA), group 3 (rats with growing mammary gland). At the end of 8 weeks, all rats were sacrificed and histopathology analysis was performed. The body temperature was measured every week using the Infrared Camera type TiS20 brand Fluke camera. Histopathology indicated average temperature of 36.66 °C, 37.77 °C and above 38.87 °C in normal, growing mammary glands, and cancerous breasts, respectively. These results revealed significantly higher heat in breasts with cancerous lesions. In the analysis of thermal pattern recognition for breast, no curve was formed in the normal group, while cancerous and growing mammary glands demonstrated a perfectly closed curve and an imperfect curve pattern, respectively. Breast screening through the analysis of temperature and thermal patterns can distinguish normal, cancerous and breast with growing mammary glands.

Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions. This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas. In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software. The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p < 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions. Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.

Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis. This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm. In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.

Cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ECG) signals. Moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain. In this paper, an image-based and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of VF, VT, SVT and the normal signal. In this simulation study, the ECG data used are collected from 3 databases, including Boston Beth University Arrhythmias Center, Creighton University, and MIT-BIH. The proposed algorithm was implemented using MATLAB R2015a software and its simulation. At first, the signal is transmitted to the state space using an optimal time delay. Then, the optimal delay values are obtained using the particle swarm optimization algorithm and normalized mutual information criterion. Furthermore, the result is considered as a binary image. Then, 19 features are extracted from the image and the results are presented in the multilayer perceptron neural network for the purpose of training and testing. In order to classify N-VF, VT-SVT, N-SVT, VF-VT, VT-N-VF, N-SVT-VF, VT-VF-SVT and VT-VF-SVT-N in the conducted experiments, the accuracy rates were determined at 99.5%, 100%, 94.98%, 100%,100%, 100%, 99.5%, 96.5% and 95%, respectively. In this paper, a new approach was developed to classify the abnormal signals obtained from an ECG such as VT, VF, and SVT compared to a normal signal. Compared to Other related studies, our proposed system significantly performed better.

Preeclampsia is one of the most common complications of pregnancy that is very difficult to control and manage during the outbreak of COVID-19. One way to control and manage this disease is to use self-care applications. Therefore, the aim of this study was to design and develop a mobile-based application to facilitate self-care for women, who suffer from pregnancy poisoning in the COVID-19 pandemic. This study was conducted in two stages: In the first stage, according to the opinion of 20 obstetricians and pregnant women, a needs assessment was performed. In the second stage, based on the identified needs, the application prototype was designed and then evaluated. For evaluation, 20 pregnant women were asked to use the application for 10 days. QUIS questionnaire version 5.5 was used for evaluation. Descriptive statistics and mann-whitney test in SPSS software version 23 were used for data analysis. Out of the 66 information needs that were identified via the questionnaire, 58 were considered in designing the application. Features of the designed application were placed in 5 categories: User’s profile, lifestyle, disease prevention and control, application capabilities and user’s satisfaction. The capabilities of the application consist of introducing specialized COVID-19 medical centers, search for the location of medical centers and doctors’ offices, drug management, drug allergies, self-assessment, stress reduction and control, nutrition and diet management, sleep management, doctor’s appointment reminders, communication with other patients and physicians, application settings. Pregnant women rated the usability of the application at a good level. The designed application can reduce the anxiety and stress due to preeclampsia feel and also improve their knowledge as well as attitude towards the COVID-19 pandemic and preeclampsia.