Application of High Resolution Magnetic Resonance Imaging Methods for Spinal Cord Tissue Segmentation

Durlu, Caglayan
Erdogan, Hasan Balkar
Kucukdeveci, Osman Fikret
Gençer, Nevzat Güneri
This paper presents the primitive results of high resolution Magnetic Resonance (MR) Imaging experiments that are performed for spinal cord segmentation purposes. In the study, it is aimed to image the epidural space, the cerebrospinal fluid, the white matter and the gray matter tissues in the lower cervical and upper thoracic regions of the spine with a maximum voxel size of 1x1x1 mm(3). For this purpose, the MRI sequences providing T2 and T2* images and used for spinal cord segmentation in the literature are investigated and some of them are modified to fulfill the voxel requirement.


Realization of magnetic resonance current density imaging at 3 Tesla,
Göksu, Cihan; SADIGHI, MEHDI; Eyüboğlu, Behçet Murat (2014-08-26)
Magnetic Resonance Current Density Imaging (MRCDI) is an imaging modality, which reconstructs electrical current density distribution inside a material by using Magnetic Resonance Imaging (MRI) techniques. In this study, a current source with maximum current injection capability of 224.7mA, under 1k Omega resistive load is used. Experiments are performed with a 2D uniform phantom, in which a current steering insulator is inserted. Magnetic flux density distributions are measured, and current density images ...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
Magnetic Resonance - Electrical Impedance Tomography (MR-EIT) Research at METU
Eyüboğlu, Behçet Murat (2006-09-01)
Following development of magnetic resonance current density imaging (MRCDI), magnetic resonance - electrical impedance tomography (MR-EIT) has emerged as a promising approach to produce high resolution conductivity images. Electric current applied to a conductor results in a potential field and a magnetic flux density distribution. Using a magnetic resonance imaging (MRI) system, the magnetic flux density distribution can be reconstructed as in MRCDI. The flux density is related to the current density distr...
Magnetic Resonance Imaging in Inhomogeneous Magnetic Fields with Noisy Signal
Arpinar, V. E.; Eyüboğlu, Behçet Murat (2008-11-27)
In this study, an image reconstruction algorithm for a Magnetic Resonance Imaging (MRI) system with inhomogeneous magnetic fields is proposed. The proposed reconstruction algorithm uses spatial distributions of main magnetic field, Radio Frequency (RF) and gradient fields as inputs, together with the pulse sequence and the noisy Magnetic Resonance (MR) signal. To calculate the noise signal, noise model for MRI with homogeneous fields is extended for inhomogeneous magnetic fields. Using this embedded noise m...
Analysis of reconstruction performance of magnetic resonance conductivity tensor imaging (MRCTI) using simulated measurements
DEĞİRMENCİ, EVREN; Eyüboğlu, Behçet Murat (2017-01-01)
Magnetic resonance conductivity tensor imaging (MRCTI) was proposed recently to produce electrical conductivity images of anisotropic tissues. Similar to magnetic resonance electrical impedance tomography (MREIT), MRCTI uses magnetic field and boundary potential measurements obtained utilizing magnetic resonance imaging techniques. MRCTI reconstructs tensor images of anisotropic conductivity whereas MREIT reconstructs isotropic conductivity images. In this study, spatial resolution and linearity of five rec...
Citation Formats
C. Durlu, H. B. Erdogan, O. F. Kucukdeveci, and N. G. Gençer, “Application of High Resolution Magnetic Resonance Imaging Methods for Spinal Cord Tissue Segmentation,” 2016, Accessed: 00, 2020. [Online]. Available: