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Full-Text Articles in Engineering
Machine Learning Approaches In Comparative Studies For Alzheimer’S Diagnosis Using 2d Mri Slices, Zhen Zhao, Joon Huang Chuah, Chee-Onn Chow, Kaijian Xia, Yee Kai Tee, Yan Chai Hum, Khin Wee Lai
Machine Learning Approaches In Comparative Studies For Alzheimer’S Diagnosis Using 2d Mri Slices, Zhen Zhao, Joon Huang Chuah, Chee-Onn Chow, Kaijian Xia, Yee Kai Tee, Yan Chai Hum, Khin Wee Lai
Turkish Journal of Electrical Engineering and Computer Sciences
Alzheimer’s disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented …
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Turkish Journal of Electrical Engineering and Computer Sciences
As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …
Decoupling Network For Tx/Rx Body Coil For 7t Mri, Ashraf Abuelhaija, Sana Salamh, Osama Nashwan
Decoupling Network For Tx/Rx Body Coil For 7t Mri, Ashraf Abuelhaija, Sana Salamh, Osama Nashwan
Turkish Journal of Electrical Engineering and Computer Sciences
The parallel imaging technique is widely used in 7T MRI scanners. It employs multichannel RF coil arrays to apply a concurrent excitation and acquisition method. Concurrent excitation faces significant challenges in terms of electromagnetic coupling between the RF coil elements. In order to prevent interference between the RF coil elements' exciters, several decoupling methods have been developed to compensate for coupling and to permit independent work for the exciters. This paper studies the coupling between meander coils arranged in two different geometrical setups and investigates the isolation performance between the coils by applying two different decoupling networks depending on the …
Iterative Sensitivity Matrix-Based Magnetic Resonance Conductivity Tensor Imaging, Evren Deği̇rmenci̇
Iterative Sensitivity Matrix-Based Magnetic Resonance Conductivity Tensor Imaging, Evren Deği̇rmenci̇
Turkish Journal of Electrical Engineering and Computer Sciences
Magnetic resonance conductivity tensor imaging (MRCTI) reconstructs high-resolution anisotropic conductivity images, which are proved to have critical importance in radio-oncological imaging as well as source localization fields. In the MRCTI technique, linearly independent current injections are applied to the region to be imaged and resulting magnetic flux densities are measured using magnetic resonance imaging techniques. In this study, a novel iterative reconstruction algorithm based on a sensitivity matrix approach is proposed and tested using both simulated and experimental measurements. Obtained results show that the proposed technique can reconstruct anisotropic conductivity images with high and position-independent spatial resolution in addition to …
Estimating Left Ventricular Volume With Roi-Based Convolutional Neural Network, Feng Zhu
Estimating Left Ventricular Volume With Roi-Based Convolutional Neural Network, Feng Zhu
Turkish Journal of Electrical Engineering and Computer Sciences
The volume of the human left ventricular (LV) chamber is an important indicator for diagnosing heart disease. Although LV volume can be measured manually with cardiac magnetic resonance imaging (MRI), the process is difficult and time-consuming for experienced cardiologists. This paper presents an end-to-end segmentation-free method that estimates LV volume from MRI images directly. The method initially uses Fourier transform and a regression filter to calculate the region of interest that contains the LV chambers. Then convolutional neural networks are trained to estimate the end-diastolic volume (EDV) and end-systolic volume (ESV). The resulting models accurately estimate the EDV and ESV …
A New Segmentation Method Of Cerebral Mri Images Based On The Fuzzy C-Means Algorithm, Mohamed Zaki Abderrezak, Mouatez Billah Chibane, Karim Mansour
A New Segmentation Method Of Cerebral Mri Images Based On The Fuzzy C-Means Algorithm, Mohamed Zaki Abderrezak, Mouatez Billah Chibane, Karim Mansour
Turkish Journal of Electrical Engineering and Computer Sciences
The aim of this work is to present a new method for cerebral MRI image segmentation based on modification of the fuzzy c-means (FCM) algorithm. We used local and nonlocal information distance in the initial function of the robust FCM model. The obtained results of the classification of MRI images showed the effectiveness of the suggested model. Calculation of the similarity index confirms that our method is well adapted to MRI images even in the presence of noise.
Mri Image Enhancement Using Biot-Savart Law At 3 Tesla, Yunus Emre Esi̇n, Ferda Nur Alpaslan
Mri Image Enhancement Using Biot-Savart Law At 3 Tesla, Yunus Emre Esi̇n, Ferda Nur Alpaslan
Turkish Journal of Electrical Engineering and Computer Sciences
Coil sensitivity is considered as the most valuable data in the parallel reconstruction of magnetic resonance imaging (MRI). In this study, a novel coil sensitivity map extraction method is introduced for spatially fixed phased array coils. The proposed technique uses the Biot--Savart law with coil internal shape information and low-resolution phase image data to form sensitivity maps. The performance of this method is tested in a parallel image reconstruction task, using the sensitivity-encoding (SENSE) technique. Under the quasi-static assumption and using the duality principle, we computed the sensitivity maps of a phased-array head coil and reconstructed full FOV images. The …
Vessel Segmentation In Mri Using A Variational Image Subtraction Approach, Ayşe Nurdan Saran, Fati̇h Nar, Murat Saran
Vessel Segmentation In Mri Using A Variational Image Subtraction Approach, Ayşe Nurdan Saran, Fati̇h Nar, Murat Saran
Turkish Journal of Electrical Engineering and Computer Sciences
Vessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance venography (MRV) images. In this study, we propose a novel method that uses MRI images as an atlas, assuming that the patient has an MRI image …