Browsing by Subject "bSSFP"

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Open Access
bSSFP phase correction and its use in magnetic resonance electrical properties tomography
(International Society for Magnetic Resonance in Medicine, 2019) Özdemir, Safa; İder, Yusuf Ziya
Purpose Balanced steady‐state free precession (bSSFP) sequence is widely used because of its high SNR and high speed. However, bSSFP images suffer from “banding artifact” caused by B0 inhomogeneity. In this article, we propose a method to remove this artifact in bSSFP phase images and investigate the usage of the corrected phase images in phase‐based magnetic resonance electrical properties tomography (MREPT). Theory and Methods Two bSSFP phase images, obtained with different excitation frequencies, are collaged to get rid of the regions containing banding artifacts. Phase of the collaged bSSFP image is the sum of the transceive phase of the RF system and an error term that depends on B0 and T2. By using B0 and T2 maps, this error is eliminated from bSSFP phase images by using pixel‐wise corrections. Conductivity maps are obtained from the uncorrected and the corrected phase images using the phase‐based cr‐MREPT method. Results Phantom and human experiment results of the proposed method are illustrated for both phase images and conductivity maps. It is shown that uncorrected phase images yield unacceptable conductivity images. When only B0 information is used for phase correction conductivity, reconstructions are substantially improved, and yet T2 information is still needed to fully recover accurate and undistorted conductivity images. Conclusions With the proposed technique, B0 sensitivity of the bSSFP phase images can be removed by using B0 and T2 maps. It is also shown that corrected bSSFP phase images are of sufficient quality to be used in conductivity imaging.
Open Access
Improvement and comparison of complex B₁ mapping techniques for use in MREPT
(2018-09) Özdemir, Safa
Impedance imaging, (i.e., conductivity, , and permittivity, ) provides helpful information about contrast between healthy and malignant tissues. As one of the impedance imaging techniques, Magnetic Resonance Electrical Properties Tomography (MREPT) uses the perturbation on B1 caused by electrical properties, and via solving the inverse problem with the help of measured B1 field, electrical properties are obtained. Therefore, to obtain conductivity using MREPT, the knowledge of B1 phase and magnitude is required. This thesis focuses on improvement and comparison of complex B1 mapping techniques for use in MREPT. In this manner, balanced steady-state free precession (bSSFP) imaging, which is one of the best candidates to obtain B1 phase, is investigated. bSSFP imaging has high speed, high signal-to-noise ratio (SNR), motion insensitivity and automatic eddy current compensation. On the other hand, it suffers greatly from B0 inhomogeneity and the concomitant "banding artifact". In regions of banding artifact, MR signal reduces significantly in magnitude, and also phase errors occur. The correction of phase errors is conducted by using three different techniques: Inserting B0 and T2 information, linearization for off-resonance estimation (LORE) algorithm, and PLANET method. In the next step, 2D version of phase-based convection-reaction equation based MREPT (phase-based cr-MREPT) technique is utilized to obtain conductivity maps from corrected phase images that are acquired from three aforementioned techniques. In order to verify the effects of correction techniques, an experimental agar-saline phantom with conductivity contrasts is constructed. It is shown that, for all phase correcting techniques, banding artifact is removed from phase images and accurate conductivity maps are obtained. Yet, inserting B0 and T2 information results in lengthy scanning time if both B0 and T2 information is acquired via traditional, reliable methods which are widely considered as golden truth. On the other hand, PLANET method suffers from B0 drift and propagation of error. Therefore, LORE algorithm is considered as the best candidate to obtain B1 phase images which is required to find conductivity maps. Besides phase-based MREPT methods, there also exists MREPT methods that requires both B1 phase and magnitude information. In the purpose of acquiring B1 magnitude images, three different methods are investigated, namely double angle (DA) method, actual ip-angle imaging (AFI) method, and Bloch-Siegert shift (BSS) based method. To analyze B1 magnitude mapping qualities of these methods, theoretical SNR calculations and phantom experiments are conducted. Both theoretical and experimental studies reveal that, based on SNR results, BSS based method is advantageous over AFI method and DA method. For each of B1 magnitude mapping methods, conductivity maps are obtained. It is found that, although standard MREPT method is indifferent to the choice of B1 magnitude mapping methods, high-SNR B1 magnitude maps provide better conductivity results for standard cr-MREPT method.
Open Access
Reconstruction by calibration over tensors for multi-coil multi-acquisition balanced SSFP imaging
(John Wiley & Sons, 2018) Bıyık, Erdem; Ilıcak, Efe; Çukur, Tolga
Purpose: To develop a rapid imaging framework for balanced steady-state free precession (bSSFP) that jointly reconstructs undersampled data (by a factor of R) across multiple coils (D) and multiple acquisitions (N). To devise a multi-acquisition coil compression technique for improved computational efficiency. Methods: The bSSFP image for a given coil and acquisition is modeled to be modulated by a coil sensitivity and a bSSFP profile. The proposed reconstruction by calibration over tensors (ReCat) recovers missing data by tensor interpolation over the coil and acquisition dimensions. Coil compression is achieved using a new method based on multilinear singular value decomposition (MLCC). ReCat is compared with iterative self-consistent parallel imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions. Results: Compared to parallel imaging or profile-encoding methods, ReCat attains sensitive depiction of high-spatial-frequency information even at higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1 ± 1.0 dB over SPIRiT and by 0.9 ± 0.3 dB over PE-SSFP (mean ± SD across subjects; average for N = 2-8, R = 8-16). Furthermore, reconstructions based on MLCC achieve 0.8 ± 0.6 dB higher PSNR compared to those based on geometric coil compression (GCC) (average for N = 2-8, R = 4-16). Conclusion: ReCat is a promising acceleration framework for banding-artifact-free bSSFP imaging with high image quality; and MLCC offers improved computational efficiency for tensor-based reconstructions. Magn Reson Med 79:2542-2554, 2018.