以擴散磁振影像為基礎之三維神經纖維呈現

Diffusion MRI Based 3D Stereo Visualization of Neural Tracts

羅畯義(Chun-Yi Lo)

指導教授 ： 林慶波 ； 蘇振隆

中原大學/工學院/醫學工程研究所/碩士(2007年)

https://doi.org/10.6840/cycu200700701

摘要

腫瘤與周圍組織的關係對於腫瘤切除、治療、評估侵蝕程度皆非常重要。磁振Gd-T1影像以及T2影像能夠讓醫師了解腫瘤以及水腫資訊；擴散張量磁振影像(DTI)更能夠評估腫瘤的良惡性程度、觀察治療及癒後情況。本論文主要利用擴散磁振影像提供腦內水分子之主要擴散方向，追蹤可能神經纖維方向並重建出神經影像，顯示大腦內神經路徑。結合虛擬實境(Virtual Reality)技術可將腦部資訊利用三維立體影像充分表現出來，提供腫瘤以及腦神經視覺化的呈現，應用於臨床手術的術前計畫、以及術後評估等。
研究方法是以擴散張量磁振影像能夠顯示出大腦中水分子的擴散情形，假設水分子主要擴散方向代表神經纖維的主要路徑，利用神經纖維追蹤演算法FACT(fiber assignment by continuous tracking)算出腦神經影像，FA閥值為0.2，角度限制60度；在Gd-T1影像上分割腫瘤輪廓、T2影像上分割水腫位置，並且對腫瘤、水腫進行三維重建。此外，利用OpenGL函式將三維影像以雙眼視角作繪圖，經立體顯示系統產生虛擬實境的三維立體神經影像。研究中透過真實假體影像以及OpenGL繪製線條假體測試虛擬實境的顯示結果，以評估立體影像顯示的正確性。
真實假體影像測試結果為100%之正確率；以OpenGL繪圖影像因假體線條顏色與背景對比度而造成平均0.6條之誤判。而將假體線條顏色比照神經纖維顯示顏色輸出，則亦可得到100%之正確率。將本系統應用到不同的擴散張量磁振影像(DTI)，亦均成功的將神經纖維影像以立體的方式呈現；此結果結合腫瘤及水腫的資訊，更容易了解神經纖維與腫瘤、水腫之關係。本系統除顯示全腦之神經影像之外，也提供ROI(Region of Interest)之選取之機制，得以呈現特定區域之神經走向，而顯示腫瘤周圍神經纖維。
本論文已建立一套整合腦部神經影像及腫瘤、水腫輪廓的虛擬實境系統，其成果能幫助醫師對病患腦部有更清楚的了解；對於手術方式、下刀位置、腫瘤大小及位置的界定等術前規劃，以及術後觀察都能達到最佳的成效，進而提昇醫療品質。

關鍵字

虛擬實境 ； 假體驗證 ； 神經影像 ； 擴散張量磁振影像 ； 神經追蹤

並列摘要

The relationship between tumor mass and peri-tumoral structures is important for tumor excision, treatment, and estimating the tumor-infiltrated degree. Conventionally, Gadolinium(Gd)-T1WI and T2WI provide the information of tumor mass and peri-tumoral edema. Diffusion tensor MRI (DTI) has shown its feasibility in grading tumor and monitoring therapeutic effects as well as clinical outcome. Moreover, the neural fiber direction would be traced and the neuroimage would be reconstructed for showing the neural pathway thru the process of DTI. The purpose of this thesis is to develop a virtual reality (VR) system. By combining with the virtual reality (VR) technique, the information in the brain can be represented via the three- dimension stereo image, and the visualization of tumor and neural tracts can be provided for the applications of the pre-surgery trajectory plan and post-surgery evaluation.
DTI was used to display the diffusion motion of water molecules in the brain. By assuming the principle diffusion direction as that of the neural tract, the neuroimage was preformed via one of the tractography algorithm, namely, the fiber assignment by continuous tracking (FACT). In this study, FA threshold was set to be 0.2, and the angular limitation was set to be 60 degrees as the criteria for the FACT algorithm for several different DTIs. By applying region of interest (ROI) selection techniques, the tumor mass and edema were respectively segmented on Gd-T1WI and T2WI, and then to build both objects by 3D reconstruction. Moreover, With the OpenGL functions, the 3D image was reconstructed via the viewpoints of two eyes, and the VR 3D stereo neuroimage was shown by stereo display system. Finally, the image of real phantom and the other which was reconstructed with OpenGL were used to evaluate the accuracy of this system.
Results show that the accuracy of phantom image is 100%. However, the test average error line of OpenGL image was 0.6 which due to the color contrast of the lines and background. When the lines color of OpenGL image was set with the color of neuroimage, the accuracy is 100%, too. By the result, the accuracy of display system could be evaluated by using the phantom image. By applying to DTIs, the neuroimage was displayed in the 3D stereo mode successfully via this system. By integrating the tumor mass and edema information, the relationship among neural tracts, tumor mass and edema is much easier to realize. Moreover, the whole brain neural tracts displaying, peri-tumoral tracts or tracts through the specific area is shown by different ROI selections.
In this study, a 3D VR system integrating with neuroimage and contours of tumor, and edema was developed. It helps the clinicians to understand the anatomy information of patient’s brain and then the pre-surgical planning including the surgery mode, trajectory, and tumor mass size, position determination and the post-surgical outcome can be optimized in neurosurgery.

並列關鍵字

Neuro-image ； Virtual Reality ； Diffusion Tensor MRI ； Phantom Validation ； Tractography

參考文獻

[2] T. M. Peters, "Image-guidance for surgical procedures," Phys Med Biol, vol. 51, pp. R505-40, Jul 21 2006.

[4] M. E. Bastin, M. Delgado, I. R. Whittle, J. Cannon, and J. M. Wardlaw, "The use of diffusion tensor imaging in quantifying the effect of dexamethasone on brain tumours," Neuroreport, vol. 10, pp. 1385-91., May 14 1999.

[5] D. A. Hamstra, T. L. Chenevert, B. A. Moffat, T. D. Johnson, C. R. Meyer, S. K. Mukherji, D. J. Quint, S. S. Gebarski, X. Fan, C. I. Tsien, T. S. Lawrence, L. Junck, A. Rehemtulla, and B. D. Ross, "Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma," Proc Natl Acad Sci U S A, vol. 102, pp. 16759-64, Nov 15 2005.

[6] C. P. Lin, W. Y. Tseng, H. C. Cheng, and J. H. Chen, "Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts," Neuroimage, vol. 14, pp. 1035-47, Nov 2001.

[7] S. Mori, B. J. Crain, V. P. Chacko, and P. C. van Zijl, "Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging," Ann Neurol, vol. 45, pp. 265-9, Feb 1999.