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研究生:陳偉權
研究生(外文):Wei-Chuan Chen
論文名稱:使用相位對比磁振造影非侵入性測量顱內順應性
論文名稱(外文):Non-invasive measurement of intracranial compliance with Phase-Contrast MRI
指導教授:程大川程大川引用關係
學位類別:碩士
校院名稱:中國醫藥大學
系所名稱:生物醫學影像暨放射科學學系碩士班
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:81
中文關鍵詞:相位對比磁振造影顱內壓順應性分割
外文關鍵詞:Phase-contrast MRIintracranial pressurecompliancesegmentation
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  • 下載下載:1
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顱內壓是一項重要的生理數據,許多腦部疾病的判別皆需要確認顱內
壓的數值,如腦積水、腦中風,且在腦部外傷或腦部手術時也是需要監控
的生理參數。而顱內壓與顱內順應性具有著線性關係。
Phase-contrast MRI 是一種對流速敏感的造影方式,可以非侵入性的
收集到人體內流速的數據。這項技術可以用來探討顱內的血液動力學,亦
可以非侵入性的探測顱內順應性。在應用時,因探討的是流量的數據,輪
廓圈選的準確性便很重要。本研究的目的是,發展一套自動化的非侵入性
顱內順應性測量技術,並提高分析的精確性。
針對脊髓的部分,已有自動分割的方法被提出,但是其需要人工點選
初始的參考點,點選的位置易對其結果產生影響,導致測量數據時產生誤
差。而血管的部分,動脈形狀渾圓、特徵明顯,大多數的分割方法都能準
確地分割;靜脈較為橢圓,偶有奇特形狀,且於接近邊緣處常有訊號降低
的情形,以梯度來進行分割易產生誤差。
本研究藉由初步的流量特徵篩選,篩選出流速明顯的區域作為起始輪
廓與參考點,結合先前已被提出的分割方法,降低人為影響的誤差。
實驗的結果,順應性的平均誤差百分比為 9.96%,而壓力梯度與體積
變化則分別為 6.56%與 8.53%。

Intracranial pressure is an important physiological information for clinical to diagnose brain disease such as hydrocephalus and stroke. We must emphasize that the correlation between compliance and intracranial is linear.
The flow data can be obtained from phase-contrast magnetic resonance imaging which is non-invasive technology. The images are so sensitive to velocity that we can use to evaluate hemodynamic and intracranial compliance.
Since the data correlate with flow, the precision of region-of-interesting is important. The aim of this study is to develop a semi-automatic and non-invasive method to measure the intracranial compliance.
Although the method of segmentation of spine images have published from previous studies, it need a manual selected point to get reference curve. Location of the point will affect the result. In vessel, artery has circular shape and clear feature. It can be segmented easily. Vein usually be an ellipse, but sometimes has strange shape. Segmentation may have some mistake because it often has low gradient at vein wall.
We use some simple feature to extract initial flow region as initial contour or reference curve. After that, use segmentation method which publish previously to segment narrowly make human error decreased.
The average coefficient of variation of compliance, pressure gradient, and volume change was 9.96%, 6.56%, and 8.53%, respectively.

一、 前言 ........................ 1
1. 相關疾病 ...................... 1
1.1. 腦血管疾病 ................... 2
1.2. 腦積水 ...................... 3
1.3. 頭部外傷 ...................... 4
2. 顱內壓與順應性 ................... 6
3. 研究目的 ....................... 6
二、 文獻回顧與理論 ................. 8
1. 門羅凱利假說 ..................... 10
2. 生理構造 ........................ 11
2.1. 動靜脈系統 .................... 11
2.2. 腦脊髓液系統 .................... 13
3. 顱內壓測量方法 .................... 14
3.1. 侵入性測量 ..................... 14
3.2. 非侵入性測量 ................... 19
4. 顱內系統模型 ...................... 24
4.1. Marmarou 模型 ................ 24
4.2. Ursino-Lodi 模型 ................ 27
4.3. Czosnyka 模型 .................. 29
4.4. Kashif 模型 ................... 32
三、 材料與方法 ...................... 35
1. 相位對比磁振造影 ................... 35
2. 影像自動分割 ..................... 39
2.1. 流量特徵擷取 ................ 40
2.2. Pulsatility-Based Segmentation (PUBS) .... 49
2.3. Active Contour Model without edge ............. 50
3. 取得生理資訊 ..................... 54
四、 結果 ........................ 56
1. 流量特徵擷取 .................. 56
2. 自動分割 .................... 65
3. 生理資訊測量 .................... 67
五、 討論 ........................ 72
六、 結論 ............................ 75
七、 參考文獻 ......................... 76

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