陳志宏臺灣大學：電機工程學研究所黃三照Hwang, San-ChaoSan-ChaoHwang2007-11-262018-07-062007-11-262018-07-062005http://ntur.lib.ntu.edu.tw//handle/246246/53291藉由擴散權重磁振造影(Diffusion-Weighted Magnetic Resonance Imaging; DWI)對水分子擴散運動的敏感性，擴散權重磁振造影能夠量測活體內水分子擴散的狀況。並能顯示顯微結構的特性這在臨床診斷及基礎醫學研究上是一項有用的工具。為了得到活體內水分子的擴散特性，擴散權重磁振造影需要很多張的磁振影像在不同方向加上不同大小的梯度磁場。使用擴散權重迴訊平面影像(Diffusion-Weighted Echo Planar Imaging; DW-EPI)能增快造影時間，但由於快速切換的梯度磁場會在梯度磁場線圈周圍的金屬表面上產生渦電流效應，雖然主動式磁場屏敝可以大幅減低產生的渦電流效應，但當大而且快速切換的梯度磁場依然會產生足以造成影像扭曲的渦電流效應。 本研究應用線性迴歸類神經網路於梯度磁場的系統參數調整，這種方法不需要重覆量測與調整，因此所需時間非常短。這與人工憑經驗調整相比不僅較可靠，所需要的時間也由幾小時減為幾分鐘，以假體的擴散權重迴訊平面影像做驗證，用我們研究的系統參數調整方法，渦電流效應所造成影像扭曲和位移可有效抑制到一個影像點數以下。目前我們已經運用這項技術於豬心的擴散張量磁振影像上(Diffusion Tensor Imaging; DTI) ，並且成功的計算出豬心肌纖維的三度空間結構。未來我們將利用這磁振影像技術於人腦的神經聯結的研究上。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.Tables of Contents Chapter 1 Introduction 1.1 Introduction----------------------------------------------------------------------------1 1.2 Motivation and Goal------------------------------------------------------------------1 1.3 Outline----------------------------------------------------------------------------2 Chapter 2 Background 2.1 Introduction----------------------------------------------------------------------------3 2.1 Diffusion-Weighted Magnetic Resonance Imaging------------------------------4 2.2 Circuit Model of Eddy Current Effects--------------------------------------------8 2.3 Pulse Modification-------------------------------------------------------------------10 2.4 Imaging Post-Processing------------------------------------------------------------11 2.5 Preemphasis Adjustments-----------------------------------------------------------13 Chapter 3 EPI Gradient Preemphasis Optimization with Neural Networks Modeling 3.1 Introduction---------------------------------------------------------------------------15 3.2 Methods-------------------------------------------------------------------------------16 3.2.1 Preemphasis Parameters----------------------------------------------------16 3.2.2 Neural Networks Modeling------------------------------------------------17 3.3 Results---------------------------------------------------------------------------------18 3.4 Discussion----------------------------------------------------------------------------24 3.5 Summary------------------------------------------------------------------------------25 Chapter 4 Fast Eddy Current Compensation by Feedback Linearization Neural Networks: Applications in Diffusion-Weighted Echo Planar Imaging 4.1 Introduction---------------------------------------------------------------------------27 4.2 Feedback Linearization Control Theory------------------------------------------28 4.3 Methods-------------------------------------------------------------------------------30 4.3.1 Pickup Coil-------------------------------------------------------------------30 4.3.2 Feedback Linearization Neural Networks--------------------------------34 4.3.3 DW-EPI Acquisition--------------------------------------------------------35 4.3.4 Measurement of the DW-EPI Geometric Distortion--------------------36 4.4 Results---------------------------------------------------------------------------------37 4.4.1 Simulated and Experimental Results--------------------------------------37 4.4.2 DW-EPI Sequence Diagram-----------------------------------------------39 4.4.3 DW-EPI gradient waveforms with Preemphasis Adjustments--------39 4.4.4 DW-EPI Phantom Imaging-------------------------------------------------42 4.5 Discussion-----------------------------------------------------------------------------42 4.6 Conclusion----------------------------------------------------------------------------45 Chapter 5 B0 Shift Preemphasis Adjustments with Phase Evolution Measurement 5.1 Introduction---------------------------------------------------------------------------46 5.2 Methods-------------------------------------------------------------------------------47 5.2.1 Measurement of the Eddy Current Induced Field-----------------------47 5.2.2 Decomposition of Phase Evolution----------------------------------------48 5.2.3 Fitting B0 Shift Preemphasis Adjustment---------------------------------49 5.3 Results---------------------------------------------------------------------------------49 5.4 Pig Heart DT-EPI--------------------------------------------------------------------52 5.5 Summary------------------------------------------------------------------------------55 Chapter 6 Discussions and Conclusions 6.1 Image-Based Methods-----------------------------------------------------------56 6.2 Pulse Modification Methods----------------------------------------------------57 6.3 Preemphasis Adjustments Algorithms ----------------------------------------57 6.4 Gradient Waveform Measurements--------------------------------------------58 6.5 DW-EPI Experiment Concern--------------------------------------59 6.6 Conclusion-------------------------------------------------------------------------60 6.7 Future Works ----------------------------------------------------------------------6265218676 bytesapplication/pdfen-US擴散權重迴訊平面影像渦電流效應線性迴歸類神經網路DW-EPIEddy currentFeedback linearization neural networksPreemphasis adjustment.thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/53291/1/ntu-94-D83503049-1.pdf