Browsing by Author "Soyak R."

Now showing 1 - 2 of 2

Accurate Dictionary Matching for Mr Fingerprinting Using Neural Networks and Feature Extraction
(Institute of Electrical and Electronics Engineers Inc., 2020) Soyak R.; Ersoy E.O.; Navruz E.; Fakultesi M.; Unay D.; Oksuz I.
Magnetic Resonance Fingerprinting is a recent technique which aims at providing simultaneous measurements of multiple parameters. MRF works by varying acquisition parameters in a pseudorandom manner so as to get unique, uncorrelated signal evolutions from each tissue. MRF is a dictionary based approach, and thus requires a database. This database can be created by simulating the signal evolutions from first principles using different physical models for a wide variety of tissue parameter combinations. Having this dictionary, a pattern recognition algorithm is used to match the acquired signal evolutions from each voxel with each signal evolution in the dictionary. In this paper, we compare the efficiency of deep learning based feature extraction method and neural network architectures in order to achieve state-of-the-art accuracy in dictionary matching for MRF. Our results showcase successful dictionary matching with high accuracy both quantitatively and qualitatively. © 2020 IEEE.
Citation - WoS: 5
Citation - Scopus: 5
Classification of Patients With Bipolar Disorder and Their Healthy Siblings From Healthy Controls Using Mri
(Institute of Electrical and Electronics Engineers Inc., 2019) Cigdem O.; Horuz E.; Soyak R.; Aydeniz B.; Sulucay A.; Oguz K.; Demirel H.
Detection of Bipolar Disease (BD), one of the most common neuroanatomical abnormalities, using machine learning algorithms together with Magnetic Resonance Imaging (MRI) data has been widely studied. BD is a highly heritable disease, yet not all siblings tend to have it despite they might have similar genetic and environmental risk factors. In this paper, the classifications of two self-acquired data groups, namely 26BD patients and 38 unrelated Healthy Controls (HCs) as well as 27 Healthy Siblings of BD (BDHSs) and 38HCs are examined. Voxel-Based Morphometry (VBM) is utilized to segment and pre-process the MRI data. In order to obtain the morphological alterations in the Gray Matter (GM) and White Matter (WM) of data groups separately, a general linear model is configured and a two sample t-test based statistical method is used. The obtained differentiated voxels are considered as Voxel of Interests (VOIs) and using VOIs reduces the dimension of the original data into the number of VOIs. The effects of using different covariates (i.e. total intracranial volume (TIV), age, and sex) on classification of the two data groups have been studied for GM-only, WM-only, and their combination. Principle Component Analysis (PCA) is used to reduce the dimension of the extracted VOIs data and Support Vector Machine (SVM) with Gaussian kernel is taken into account as a classifier. The experimental results indicate that among three covariates, TIV provides better results for both data groups and the classification accuracies of the combination of GM and WM maps is higher than that of GM-only and WM- only for both groups. In BD and HC comparison, the highest classification accuracies of 70.3% for GM, 79.7% for WM, and 82.8% for fusion of extracted GM as well as WM are obtained. In BDHS and HC comparison, the highest classification accuracies of 72.3% for GM, 76.9% for WM, and 78.5% for fusion of extracted GM as well as WM are obtained. © 2019 IEEE.