http://fbt.tums.ac.ir/index.php/fbt/issue/feed Frontiers in Biomedical Technologies 2018-09-01T20:10:24+0430 Dr. Mohammad Reza Ay fbt@tums.ac.ir Open Journal Systems http://fbt.tums.ac.ir/index.php/fbt/article/view/158 Effect of Reconstruction Filters on Ultrasound Computed Tomography Image Quality in Two Different Detector Arrangements: a Preliminary Simulation Study 2018-09-01T20:10:23+0430 Razieh Solgi rsolgi@razi.tums.ac.ir Masoumeh Gity mgity@sina.tums.ac.ir Hojjat Mahani hmahani@aeoi.org.ir Hossein Ghadiri h-ghadiri@sina.tums.ac.ir <p>Sound-speed images can be used for cancer detection and diagnosis. In soft tissues, tumors have generally different sound speeds than normal tissues. Improving ultrasound image quality and variety of imaged tissue properties should prove beneficial to breast cancer screening and diagnosis. The goal of this study is to compare the effect of five reconstruction filters on fan beam acquisition modes of ultrasound computed Tomography (UCT) and investigate the image quality parameters of the point spread function (PSF), modulate transform function (MTF) and spatial resolution for two different arrangements of ultrasonic array, ring-shaped and parallel shaped. These study results show that the spatial resolution affected by not only detector arrangement but also reconstruction filters.</p> 2018-09-01T12:48:22+0430 ##submission.copyrightStatement## http://fbt.tums.ac.ir/index.php/fbt/article/view/167 A Simulation Study on the Investigation of Thermal Effects associated With Acoustic Radiation Force Shear Wave Interference Patterns Generation in the Liver Tissue 2018-09-01T20:10:24+0430 Vahid Sadeghi vahidsadeghi64119@gmail.com Pezhman Pasyar pasyar_p@Razi.tums.ac.ir Hassan Rezazadeh rezazadeh.hassan@yahoo.com Hossein Arabalibeik arabalibeik@yahoo.com Bahador Makkiabadi makkiabadib@gmail.com Seyyed Moayyed Alavian sm_alavian@yahoo.com <p>The mechanical map of liver tissue like stiffness is important as much as its anatomical image for clinical purposes as staging the liver fibrosis. Acoustic radiation force (ARF) based elastography is an imaging modality which can generate shear waves in the liver. As the ultrasound waves pass through different layered tissues, their mechanical energy turned into heat energy because of attenuation and absorption mechanisms. The ultrasonic exposure time and focal intensity are two crucial factors in the generated heat. In this simulation study we show the safety of using ARF for shear wave generation in the liver tissue.</p> 2018-09-01T17:57:48+0430 ##submission.copyrightStatement## http://fbt.tums.ac.ir/index.php/fbt/article/view/171 Investigating the Relationship between Theta/Beta Ratio and Intensity of Disease in Children with ADHD 2018-09-01T20:10:24+0430 Samaneh Farnia sm.farnia@gmail.com Abbas Alipour alipour.abbas59@gmail.com Javad Alaghbandrad alaghbandradjavad@gmail.com Arefeh beygum Shafaat arefehshafaat@gmail.com Samineh Fattahi samineh.fattahi@gmail.com Mahya Ghahremanloo mahya.ghahremanloo@gmail.com <p>Introduction: Attention/Deficit-Hyperactivity Disorder (ADHD) is a neurodevelopmental disease in which the affected child experiences evidently lowered ability to concentrate and control impulses in comparison to natural state. Electroencephalography (EEG) based Biomarkers including measurement of the brain’s theta/beta waves ratio in the vertex(Cz) region can be useful to achieve diagnostic and therapeutic objectives as a noninvasive method and as such EEG biomarkers have been under extensive use in ADHD research, though they have not been clinically confirmed so far. This study attempted to examine the relationship between theta/beta ration with disease intensity in ADHD children as well as sensitivity and theta/beta ratio characteristic to detect AHDD and healthy children and the accuracy of this ratio to differentiate diseased children from healthy ones in terms of ADHD.</p> <p>Materials and methods: This study is a case-control test, in which the statistical population consisted of 59 five-to-ten-year-old healthy children and 61 children with ADHD who had been chosen through simple random sampling, out of children visiting pediatric and adolescent psychiatric clinic in Zareh Hospital, Sari, Iran. All patients were examined in terms of disease intensity using parental Conner’s questionnaire. The theta/beta ratio in Cz and Fz points was tested and recorded individually each once during waking hours with open eyes with no mental task, and another time during a specific mental task by neurofeedback.</p> <p>Results: beta, theta, and theta/beta without test was larger in the Fz region in the patient group than in the control group (p&lt;0.001). There was a medium relationship between theta/beta (r=0.48; p&lt;0.001) in Fz region and Conner’s score.</p> <p>Theta/beta without test in Fz ( p=0.02; sensitivity=62%; specificity=71%) and in Cz (p=0.03; sensitivity=51%; specificity=73%) differentiate the two groups only at a medium level.</p> <p>Conclusion: It seems that further research should be conducted using more precise tools like QEEG with a larger sample volume and more limited age group.</p> 2018-09-01T18:00:54+0430 ##submission.copyrightStatement## http://fbt.tums.ac.ir/index.php/fbt/article/view/180 Optimization of Effective Parameters on Acoustic Radiation Force Shear Waves Interference Patterns Elastography by using a Finite Element Model 2018-09-01T20:10:24+0430 Vahid Sadeghi vahidsadeghi64119@gmail.com Pezhman Pasyar pasyar_p@Razi.tums.ac.ir Hassan Rezazadeh rezazadeh.hassan@yahoo.com Hossein Arabalibeik arabalibeik@yahoo.com Bahador Makkiabadi makkiabadib@gmail.com Seyyed Moayyed Alavian sm_alavian@yahoo.com <p>Variation in mechanical properties of soft tissues may be a sign of disease.&nbsp; ‎Since, some disease like fibrosis or cancer change the stiffness of related tissue, ‎we can assess the disease of soft tissue with its elasticity. The elastic stiffness ‎properties of soft tissues can be estimated by the use of locally induced ‎displacements and shear waves.‎We have made a two-dimensional plane finite element model as soft tissue. The soft tissue has been exposed by two amplitudes modulated high intensity ‎focused ultrasound transducer (AMHIFU) and hence shear wave interference patterns which can be captured by lower frame rate imaging are generated. The acoustic radiation force created by a self-focusing ultrasound transducer has been determined from an ultrasound pressure field simulation. A Gaussian function was fitted to the resulting acoustic radiation force (ARF) field and implemented in the form of a body force in the finite element model. The effect of different excitation parameters for elasticity estimation is investigated. The results show that choosing an appropriate excitation characteristic leads to better clinical usage.shear waves interference patterns elastography which doesn’t need high frame rate imaging with optimized parameters can be used as a non-invasive method for measuring the elastic stiffness of soft tissues.</p> 2018-09-01T18:04:03+0430 ##submission.copyrightStatement## http://fbt.tums.ac.ir/index.php/fbt/article/view/176 Differentiating Tumor and Edema in Brain Magnetic Resonance Images using a Convolutional Neural Network 2018-09-01T20:10:24+0430 Aida Allahverdi allahverdi.1774536@studenti.uniroma1.it Siavash Akbarzadeh akbarzadeh.1774526@studenti.uniroma1.it Alireza Khorrami Moghaddam ar.khorrami@gmail.com Armin Allahverdy armin.allahverdy@gmail.com <p><strong>Purpose- </strong>Glioblastoma is the most common subset of glioma with high grade of mortality. Therefore, early diagnosis may cause the better therapeutic interventions. Moreover, brain MRI shows good performance to tumor localization. But manual tumor localization is time consuming therefore an automatic tumor segmentation is recommended.</p> <p><strong>Method- </strong>In this study, an automatic brain tumor segmentation based on the fully convolutional neural network is presented. This segmentation method can localize and differentiate the active tumor and edema in multi-modal brain MRI. The convolutional neural network has a wide range application for machine vision and visual recognition. In this study, we introduced a novel convolutional neural network for brain tumor segmentation.</p> <p><strong>Results- </strong>This method was used for high grade and low grade subjects’ MRI. In this study a multi-modal MRI data contained T1 weighted, T1 enhanced, T2 weighted and FLAIR was used. For investigating the segmentation performance, the dateset was divided into train and test dataset. Moreover the fully convolutional neural network used the pixels of sliding window on MRI as input. The results shows that the increasing the window size cause the increment of train segmentation performance and has no significant effect on segmentation performance. Overall, the best train segmentation performance was 97.6% and best test segmentation performance was 89.7%.</p> 2018-09-01T20:07:55+0430 ##submission.copyrightStatement##