Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) and its expansion to multi-physics multi-contrast magnetic resonance imaging


Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2021

Tezin Dili: İngilizce

Öğrenci: MEHDI SADIGHI

Danışman: Behçet Murat Eyüboğlu

Özet:

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is one of the emerging imaging modalities to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging (DTI) and magnetic resonance electrical impedance tomography (MREIT) techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors(C and D) in a porous medium. On the other hand, knowledge of the current density (J) distribution is used in many medical applications to optimize and plan treatments like transcranial direct and alternating current stimulations (tDCS and tACS) and deep brain stimulation (DBS). Magnetic resonance current density imaging (MRCDI) is used to acquire cross-sectional current-induced magnetic flux density (Bz) and J distributions of the externally injected currents. The clinical applicability of DT-MREIT and MRCDI is highly dependent on the sensitivity of the acquired noisy Bz and the estimated J distributions. In this thesis, a novel pulse sequence, namely the injected current nonlinear encoding -multi-echo–FLASH (ICNE-ME-FLASH), is implemented for MRCDI to acquire qualified (high SNR) Bz and J distributions in a clinically acceptable scan time. Also, an analysis is developed to investigate the combined effect of relevant sequence parameters on the SNR level and the total acquisition time of the acquired Bz images. The minimum total acquisition time for the desired SNR level or the highest SNR achievable in a given time can be estimated using the proposed analysis. Also, the analysis provides different sets of sequence parameters (i.e., TR, NEX, α) to achieve the desired SNR level in almost the same acquisition time that can be used in different experimental situations. Using the proposed ICNE-ME-FLASH pulse sequence, the Bz distributions with the estimated SNR of 13 dB associated with I= 200 and 400 μA current injection can be measured in the total scan time less than 19 and 5 minutes, respectively. Also, the effects of magnetohydrodynamic (MHD) flow velocity (v) and the intensive utilization of the gradients in the MRCDI experiments using ICNE-ME-FLASH are investigated for the first time. A novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The conductivity tensor distributions of two imaging phantoms with I= 3 mA current injection are reconstructed using the proposed single current DT-MREIT. The total data acquisition time for DTI and Bz imaging is 21 and 30 minutes. The same MRCDI procedure with two current injections lasts twice as much. The SNR of the measured Bz using ICNE-ME-FLASH pulse sequence is estimated as 36 dB and 32 dB for the two phantoms. Furthermore, a multi-physics multi-contrast pulse sequence is proposed and implemented to acquire D, Bz, and v data simultaneously instead of acquiring these multiple data individually using three different pulse sequences. The proposed pulse sequences, the SNR and total acquisition time analysis, and the reconstruction algorithms are evaluated using simulated measurements and physical experiments. All these improvements and the proposed methods could increase the clinical potential of the current density and conductivity tensor imaging.
current density imagingconductivity tensor imagingSNR analysismulti-contrast imagingreconstruction algorithmFLASHpulse sequence parameter optimization
https://hdl.handle.net/11511/91124
Graduate School of Natural and Applied Sciences, Thesis