Fast Eddy Current Compensationby Feedback Linearization Neural Networks:Applications inDiffusion-Weighted Echo Planar Imaging

• Main Authors: San-Chao Hwang, 黃三照
• Other Authors: Jyh-Horng Chen
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/21710301381692880631

博士 === 國立臺灣大學 === 電機工程學研究所 === 93 === Diffusion-weighted magnetic resonance imaging (DWI) sensitizes the magnetic resonance images to the diffusive mobility of water and maps water diffusion in tissue. It can highlight the microstructural characteristics of biological tissues and serve as a useful imaging tool for both clinical diagnosis and basic medical research. A large number of images with different magnitudes and directions of the diffusion sensitizing gradients need be acquired in order to estimate the diffusion properties. For efficiency, these images are usually acquired using diffusion-weighted echo-planar imaging sequence (DW-EPI). The rapid switching of the gradient pulses of DW-EPI can generate eddy currents in conducting surfaces surrounding the gradient coils. Although generation of eddy currents is greatly decreased in an active shielded gradient system, this can still occur especially when using large and rapidly rising and falling diffusion sensitization gradient pulses. This study describes the application of the feedback linearization neural networks, known from neural network computing, to the problem of gradient preemphasis. This approach of preemphasis adjustment doesn’t require an iterative procedure between measurement and adjustment, therefore is essentially instantaneous in its execution. Based on our study, gradient compensation determined by our procedure effectively suppressed eddy current induced geometric distortion and spatial shift of diffusion-weighted EPI images. Comparing the manual preemphasis adjustments, this approach not only is reliable and accurate but also can reduce the spent time from several hours to several minutes. We have successfully applied this technique to the pig heart fiber tracking with diffusion tensor echo planar imaging (DT-EPI). In the future, the human brain white matter connectivity will be also studied.