臺灣博碩士論文加值系統

腦瘤是指局部腦組織細胞發生不正常的分裂增殖而生成瘤塊，佔據腦部有限密閉空間或侵害正常腦組織並造成腦部症狀。腦瘤主要可分為原發性和轉移性，原發性腦瘤依組織類型可分為神經元膠質細胞或非神經元膠質細胞，依病理形態又可分為良性與惡性腦瘤。轉移性腦瘤是指由其他部位癌症轉移而來，常見的為乳癌、肺癌、大腸癌等。惡性腦瘤的好發率雖然不高，但因為其生存率不高，手術預後差，常導致影響生活功能，且腦部為人體無可取代之重要器官，因此需給予重視。磁振造影以非侵入性方式為腦組織提供了高解析度及高對比影像，因而成為臨床中最常使用的腦瘤診斷工具。眾多磁振造影技術中，擴散性磁振造影是目前唯一可透過觀察大腦白質微結構組織水分子擴散狀態進而以三維呈現大腦白質神經束走向的工具。白質神經束追蹤技術雖然已發展超過20年，但由於受到機器硬體設備及數據處理複雜性的影響導致神經追蹤結果不準確性因而局限了其在臨床中的應用性。近年來大腦白質神經追蹤技術開始廣泛地應用於神經外科，透過解析大腦白質微結構以輔助腫瘤的判讀、手術的規劃、手術導航、甚至術後功能的評估。為了將大腦白質神經追蹤術能更有效地應用於神經外科，在此論文中我們首先針對臨床最常使用的白質神經追蹤術演算法進行優化及驗證，為神經外科醫師開發了一款全自動化的神經追蹤軟體DiffusionGo，以降低因繁瑣操作及人為因素導致神經追蹤結果之差異。其次，我們針對目前臨床常用的掃描參數進行優化，採用了更高階的擴散掃描參數及模型，以突顯腦瘤周邊水腫之神經束並加以驗證其準確性。在論文的最後部分，我們採用了高階擴散影像模型，對腦瘤種類及級別進行分類，且進一步探討了影像參數與腫瘤細胞之間的關係，提供術前虛擬生物切片，為手術規劃提供更多的訊息。總結而言，此論文透過優化擴散性磁振造影造影之掃描參數及分析流程，以協助神經外科於臨床之輔助診斷、手術規劃、手術導航及功能評估。

A brain tumor refers to an abnormal division and proliferation of brain tissue cells that form tumor masses, occupy a confined space in the brain, invading normal brain tissues, and producing neurological symptoms. More than 150 different brain tumors have been documented, but the two main groups of brain tumors can be categorized as primary and metastatic. Primary brain tumors include tumors that originate in the tissue of the brain or the immediate surroundings of the brain. Primary brain tumors can be classified according to tissue type into glial (composed of glial cells) or nonglial cells (developed on or in the structures of the brain, including nerves, bold vessels, and glands), and according to their pathological form into benign and malignant tumors. Metastatic brain tumors include tumors that arise elsewhere in the body (such as breast, lung, etc.) and migrate to the brain, usually through the bloodstream. Metastatic brain tumors are considered cancer and are malignant. Malignant brain tumors are well known to be rare, but their survival rates are low, the prognosis of surgery is poor, and they often affect the way we live, so it is an issue that must be taken seriously. Magnetic resonance imaging provides images of brain tissue in high-resolution and high-contrast noninvasively, and is therefore the most commonly used clinical diagnostic tool for brain tumors. Among all MR techniques, diffusion MRI is the only technique that can visualize the direction of white matter fiber bundles in three dimensions by probing the diffusion of water molecules in the white matter microstructure. Although the white matter fiber tractography technique has been developed for more than two decades, its clinical application is still limited due to the higher variability in the virtual fiber dissection caused by the influence of the hardware equipment and the complexity of the data processing. During the past few years, white matter fiber tractography has been widely used in neurosurgery, by probing the white matter microstructure to assist tumor diagnosis, surgical planning, surgical navigation, and postoperative functional evaluation. To apply diffusion MRI techniques in neurosurgery for precision surgery, we first optimized and verified the most commonly used fiber tractography algorithm in clinical practice. In order to reduce the variability of virtual fiber dissection due to the complexity of operation and human factors, we developed a fully automated fiber tractography software for neurosurgeons, DiffusionGo. In addition, we adapted advance diffusion protocols and models to highlight the edematous fiber tracts and verified its accuracy through five-year follow-up data. Finally, we use the advanced diffusion model to classify brain tumor types and grades, and we further explore the relationship between diffusion indices and tumor cellularity and develop a preoperative virtual biopsy to assist in surgical planning. In summary, this dissertation has optimized the diffusion MRI scanning protocol and analysis procedure to assist neurosurgeons in clinical diagnosis, surgical planning, surgical navigation, and postoperative functional evaluation.

TABLE OF CONTENTS
Chinses Abstract i
English Abstract ii
Table of Contents iv
List of Figures vi
List of Tables viii
CHAPTER 1 Introduction 1
1.1 Background 1
1.2 Motivations and purpose 10
CHAPTER 2 Multiple Assigned Criteria Fiber Tractography Algorithm: The Validation and Application 12
2.1 Introduction 12
2.2 Material and Method 14
2.2.1 Multiple assigned criteria fiber tractography 14
2.2.2 MAC algorithm in clinical application and validation 17
2.3 Results 21
2.3.1 Postmortem brain fiber dissection 21
2.3.2 Similarity comparison between postmortem brain and fiber tractography 23
2.3.3 MAC algorithm fiber tracking 24
2.3.4 DiffusionGo clinical application 24
2.4 Discussion 30
2.5 Conclusion 33
CHAPTER 3 Exploring Peritumoral Neural Tracts by using Neurite Orientation Dispersion and Density Imaging 34
3.1 Introduction 34
3.2 Material and Method 37
3.2.1 Participants 37
3.2.2 MRI Acquisition 37
3.2.3 Image Analysis 38
3.3 Results 45
3.3.1 ODI threshold calibration 45
3.3.2 Comparison between DTI and NODDI 47
3.3.3 ROC diagnostic performance analysis 49
3.3.4 NODDI fraction comparison 51
3.3.5 Tracts verifications after releasing the vasogenic edema 53
3.4 Discussion 54
3.5 Conclusion 58
CHAPTER 4 Assessing Microstructure Indices of Brain Tumor: A NODDI Study 59
4.1 Introduction 59
4.2 Material and Method 61
4.2.1 Participants 61
4.2.2 MRI acquisition 61
4.2.3 Imaging Analysis 62
4.3 Results 65
4.3.1 Group comparison of diffusion indices 65
4.3.2 Diagnostic performance between LGG and HGG 66
4.3.3 Histology validation 68
4.3.4 Clinical application 69
4.4 Discussion 71
4.5 Conclusion 74
CHAPTER 5 Conclusion, Limitations, and Future Extensions 75
5.1 Conclusion 75
5.2 Limitations 76
5.3 Future Extensions 77
References 78
Appendix 93

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