Simultaneous Multislice Acquisition Technique on Auto-Regularized Parallel Imaging for Fast MR Imaging

• Main Authors: Hsih-Kuo Tai, 蔡詩國
• Other Authors: 陳志宏
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/41653852559904498704

碩士 === 國立臺灣大學 === 電機工程學研究所 === 93 === Since the invention of MRI, major improvements in imaging speed have been conceived and implemented, like echo planar, turbo, and spiral acquisitions, and even combinations of these methods. A very different approach to spatial encoding has been known of old, i.e. using the sensitivity profiles of a set of receiver coil elements to localize the signal’s source [1]. This so-called parallel imaging or sensitivity encoding methodology has recently gained renewed interest by the introduction of SMASH [2], and especially SENSE [3]. Parallel imaging was generally implemented today to achieve speed scanning, SNR enhancement, improvement spatial and temporal resolution. Furthermore, the maximum of acceleration always depends on the numbers of elements in phase array coil. In order to speed scanning and enhance SNR, we will combine Simultaneous Multislice Acquisition (SIMA) [7-12] with parallel imaging for higher spatiotemporal resolution and SNR in anatomical image. In this work, we excited two parallel slice profiles simultaneously and segregated intermixed slices by Hadamard transform and utilizing non-homogeneous sensitivity profile of phase array coil [13]. DTI (diffusion tensor image) was well put into practice with SIMA technique in phantom and in vivo experiment. Moreover, we excited two slices and speeded up scan time in 2, 3 and 4 accelerations both in phantom and animal study. Utilizing Tikhonov regularization to parallel image reconstruction and mingled image segregation, the approaches were also faultlessly practiced in phantom and in vivo experiment. From this novel idea, the acceleration ratio will transcend the amount of phase array coil. Furthermore, by increasing accelerations and spatiotemporal resolution, sensitivity encoding always involves ill-condition problem [4] which induce serious noise amplification in anatomical image reconstruction. It’s also the price for the increased spatiotemporal resolution in parallel MRI. In this thesis, we propose a reconstruction based on Tikhonov regularization [5] that reduces SNR loss due to geometric correlations in the spatial information from the array coil elements [6]. Reference scans are utilized as a priori information about the final reconstructed image to provide regularized estimates for the reconstruction using the L-curve technique. In Bruker 3T MRI system, we could achieve more than 4 accelerations by exploiting SIMA and auto-regularized SENSE with a 4-channel rat head array coil for higher SNR and spatiotemporal resolution.