藉由水分子在排列整齊之生物組織內進行擴散而產生的信號變化，擴散性磁振影像(diffusion MRI)提供了以擴散的非等向來觀察神經細胞結構之可行性，隨著擴散張量磁振影像 (diffusion tensor MRI)及擴散譜磁振影像 (diffusion spectrum MRI)之發展，大腦神經白質結構及連結得以非侵入性影像方式具體研究，然而這類研究始終沒有一個標準以證明其定義神經結構之正確性，同時研究僅侷限於大腦白質結構，至於大腦灰質結構則由於複雜性太高，傳統影像方法皆無法取得。有鑑於此，本論文提出了一個老鼠模型及實驗假體模型以驗證擴散張量磁振影像及擴散譜磁振影像之正確性，並提出以擴散譜磁振影像研究大腦灰質之可行性。
以往擴散張量磁振影像之證明侷限於侵入式的組織切片對比，然而這類證明由於複雜組織切片處理過程所導致的誤差，始終無法提供精確的正確性估計，更遑論三度空間神經結構之描述與證明。本論文提出動物模型克服這項問題，利用錳離子注射產生磁振對比影像，成功地證明擴散張量磁振影像與擴散譜磁振影像描繪神經方向之正確性，並證明擴散譜磁振影像描繪神經交會之正確性；結果顯示擴散張量磁振影像僅可描繪單神經走向，而擴散譜磁振影像可正確地描繪單神經或複雜神經之走向，其誤差幾近於雜訊邊緣。藉此特性，我們得以進一步地以擴散譜磁振影像描繪複雜神經細胞結構以及灰質與白質之神經連結。
對於大腦灰質複雜之神經結構，以往只能以光學式或電子式的顯微鏡來研究觀察，然而這類研究均必須以侵入方式取得組織樣本，並經由繁瑣的組織切片方式獲得實驗標本，再以不同的染色方式顯現所需要之神經結構，雖然如此可獲得非常高解析度之影像品質，卻無法適用於活體體內，亦難達到三度空間之神經結構研究，嚴重限制了這類技術的實用範圍。
本論文以老鼠海馬回 (hippocampus) 為研究試驗標的，成功地以擴散譜磁振影像技術獲得老鼠海馬回之大腦神經細胞結構，並與傳統組織切片方式對照，清楚地分辨海馬回中不同層次之組織結構；因為本技術可於體內以非侵入性之影像方式獲得複雜的神經細胞結構，使這項技術極具應用價值，也由於本技術可直接體外取得，避免了切片處理的限制，更促進了三度空間神經結構研究之進行。
本論文第一個成功地建立實驗模型，驗證擴散張量磁振影像與擴散譜磁振影像之正確性，並第一個證明擴散譜磁振影像可成功地獲得大腦複雜之神經細胞結構。放眼未來，我們所建立之實驗模型將可繼續為建立神經連結理論之發展與驗證，及擴散譜磁振影像技術作最佳使用。而擴散譜磁振影像於神經細胞結構之突破，未來亦可對大腦灰白質神經聯結、大腦發展或病變過程神經結構變化、腦部功能性與神經聯結等研究進一步發展作貢獻。

This dissertation will validate, optimize and apply diffusion magnetic resonance imaging (diffusion MRI) to map complex neural fiber architecture in rat brain. With the powerful concept that molecules can probe tissue structure at a microscopic scale during their diffusion-driven displacement, diffusion MRI provides and characterizes exquisite details of tissue microstructure. In an interested tissue such as brain, molecules bounce, cross, or interact with many tissue components such as cell membranes, fibers, or macromolecules will exhibit different signal characters under diffusion MRI encoding. Following the emergence of diffusion tensor MRI (DTI) and recent advance in diffusion spectrum MRI (DSI), orientations of organized microstructures can be studied and observed in vivo based on the anisotropy of water molecular diffusion.
To obtain an exact description of tissue microstructure and the correct connectivity of neural fiber tracts in vivo, a gold standard for validating the accuracy of DTI and DSI is important but still lacking. This dissertation presents the first validation of DTI and DSI with co-registered manganese-enhanced MRI. A rat model was developed in which optic tracts were enhanced by manganese. Deviation angles between tangential vectors of the enhanced tracts and the principal eigenvectors of the diffusion tensor or the principal orientations of the diffusion spectrum were computed pixel by pixel. In this way, the accuracy of DTI or DSI in defining axonal fiber orientation was determined. In addition, a phantom model with intersecting water-filled capillaries was designed to show the capability of DSI to infer tract orientations and validate the accuracy of DSI in defining intersecting fiber tracts. Owing to model-free of DSI and the character that DSI describes the three-dimensional spin displacement within each imaging voxel, our results showed that DSI provides details of tissue microstructure more faithfully than diffusion weighted MRI or DTI.
Using this powerful feature of DSI, we found that complex neural cytoarchitecture in cortical gray matter could be studied and resolved without pushing the spatial resolution to the level comparable with the tissue of interest. Such complex fiber structures in cortical gray matter, due to interdigitation, were difficult or impossible to identify individually with conventional MRI methods. This dissertation proposed the first study of complex neural cytoarchitecture of cortical gray matter in vivo. Rat hippocampus was studied as a “test pattern” owing to its well-known neural structures. With in-plane resolution of 180 mm, DSI showed that this technique could reveal detailed axonal fiber microstructure successfully, in consistent with cytoarchitecture study in histology. In the future, this capability would be very useful in the study of connectivity between the gray matter and the white matter.

1.Introduction
1-1 Background
1-2 Diffusion Measurement by NMR
1-2-1 NMR pulsed gradient diffusion measurement
1-2-2 Diffusion tensor magnetic resonance imaging
1-2-3 Diffusion spectrum magnetic resonance imaging
1-3 Outline
2.Validation of Diffusion Tensor Magnetic Resonance Axonal Fiber Imaging
with Registered Manganese-enhanced Optic Tracts
2-1 Introduction
2-2 Materials and Methods
2-2-1 Rat model
2-2-2 Imaging techniques
2-2-3 Diffusion tensor reconstruction
2-2-4 Registration techniques
2-2-5 Deviation angles
2-2-6 Variance analysis
2-3 Results
2-3-1 Mn2+-enhanced optic tracts and DTI
2-3-2 Deviation angles
2-3-3 Noise estimate
2-4 Discussion
2-4-1 Validation of DTI using Mn2+-enhanced optic tracts
2-4-2 Rationales of using Mn2+-enhanced optic tracts as a reference
2-4-3 Limitations
2-4-4 Error estimate
2-4-5 Application of Mn2+-enhanced optic tracts in validating diffusion tractography
2-5 Conclusion
3.Validation of Diffusion Spectrum Magnetic Resonance Axonal Fiber Imaging with Registered Manganese-enhanced Optic Tracts and Phantom Model
3-1 Introduction
3-2 Materials and Methods
3-2-1 Rat model and phantom design
3-2-2 Imaging techniques
3-2-3 Diffusion spectrum and diffusion tensor reconstruction
3-2-4 Registration techniques
3-2-5 Deviation angles
3-2-6 Optimization for DSI data acquisition
3-3 Results
3-3-1 Rat model
3-3-2 Phantom model
3-3-3 Deviation angles
3-3-4 Optimization for DSI data acquisition
3-4 Discussion
3-4-1 Rationales of using DSI to depict axonal fiber orientations
3-4-2 Validation of DSI using Mn2+-enhanced optic tracts and phantom model
3-4-3 Rationales of using Mn2+-enhanced optic tracts as a reference
3-4-4 Rationales of using water-filled capillaries as a reference
3-4-5 Limitations for phantom model
3-4-6 Error estimate
3-4-7 Optimization for DSI data acquisition
3-5 Conclusion
4.Diffusion Spectrum Imaging of Complex Neural Cytoarchitecture in Adult Rats
4-1 Introduction
4-2 Materials and Methods
4-2 1 Rat sample preparation
4-2-2 Imaging techniques
4-2-3 Diffusion spectrum and diffusion tensor reconstruction
4-2-4 DSI visualization
4-3 Results
4-3-1 DSI of complex neural cytoarchitecture in rat hippocampus
4-3-2 Comparison between DSI and DTI
4-4 Discussion
4-4-1 DSI of complex neural cytoarchitecture
4-4-2 Rationales of DSI in resolving complex neural cytoarchitecture
4-4-3 Limitations of DSI in resolving neural cytoarchitecture
4-4-4 DSI Visualization
4-5 Conclusion
5.Conclusions
5-1 Summary
5-2 Discussion
5-2-1 Reduction of data acquisition time
5-2-2 Increasing the imaging resolution
5-2-3 Neural architecture characterization using DSI
5-2-4 DSI of human brain
5-3 Conclusion
5-4 Future Work
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