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研究生:沈宗翰
研究生(外文):Chung-Han Sheng
論文名稱:肺部微灌流磁振影像之全自動影像分析系統
論文名稱(外文):Automated analysis system for MR pulmonary perfusion images
指導教授:林益如林益如引用關係
指導教授(外文):Yi-Ru Lin
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:80
中文關鍵詞:影像分割肺部微灌流梯度向量流動態輪廓演算法臨界值法磁振造影.
外文關鍵詞:image segmentationpulmonary perfusionGVF snake modelthresholdingMRI.
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對比劑微灌流影像(contrast-enhanced perfusion)是一種可以有效診斷肺部疾病的方式,對病患注射對比劑之後,在經由醫學影像設備(CT、MRI…)觀察對比劑流進及流出目標區的狀況,便可以判定患者的血流是否正常以便進行後續的診斷。
為了量化分析肺部組織血流的狀況,取得血流(pulmonary blood flow, PBF),血量(pulmonary blood volume, PBV),以及平均通過時間(mean transit time, MTT)與到達時間(arriving time)等各種參數,需要統計及分析左肺及右肺的組織影像,因此必須從整張胸腔影像中,圈選出左肺及右肺的ROI。然而整體肺部輪廓跟肺動脈比起來,邊界更長並更為曲折複雜,且不同人圈選之輪廓可能會有很大的差異。再者還必須去掉血管的部分,平均每圈選一次都得花上3~5分鐘,且若失敗可能得重新開始,因此導入自動化選取肺部ROI是有其必要性的。
本文利用自動肺部區域定位,針對區域內影像做強化對比與分類,多重梯度向量流動態輪廓演算法,自動分類法取得動脈血管門檻值。經由這四個步驟來取得肺部ROI,並且透過模擬真實狀況的方式驗證本方法的正確性,再由專業的放射師來評比取得的ROI,本方法所取得的ROI除了方便醫療及研究人員量化患者的肺部血流狀況外,亦可以應用在許多方面的研究上。
Contrast-enhanced perfusion imaging is an effective method for diagnosis of lung diseases. After injection of contrast agent, the contrast agent washing-in and washing-out of tissues is observed through medical equipments such as CT and MRI. Then the blood flow condition of the patient can be determined and provided for the follow-up diagnosis. The quantification analysis of blood flow condition of lung tissues is to obtain perfusion parameters such as pulmonary blood flow (PBF), pulmonary blood volume (PBV), mean transit time (MTT) and arriving time. It needs statistic calculation and analysis on the images of right and left lung parenchyma, therefore ROIs of right and left lung should be segmented from the entire image of thoracic cavity. But the whole lung boundary is longer and more complex than pulmonary artery and a big difference exists among the boundaries segmented by various persons (inter-observer variation). It also needs to exclude blood vessels. The manual process takes about 3~5 minutes for each segmentation. Therefore it is necessary to implement automatic segmentation for lung ROI.
This study adopts 4 steps to obtain the lung ROI: (1) automated lung area location. (2) automated lung area enhancing. (3) multi GVF snake model. (4) artery thresholding. To verify the feasibility and accuracy of this method, we have tested our method on both simulated data and patients. These results show that this method is feasible and good enough for the segmentation of lung ROIs. ROIs obtained by this method can be used for quantifying the blood flow condition of the patient’s lung. It can be a convenient and useful tool for quantitative analysis of pulmonary perfusion.
ABSTRACT - 1 -
中文摘要 - 3 -
Chapter 1. Introduction - 5 -
1.0Background - 5 -
1.1 Motivation - 8 -
1.2 Related work - 10 -
1.3 Architecture - 12 -
Chapter 2.Theory - 13 -
2.1 Principle of perfusion MRI - 13 -
2.2 Segmentation algorithms - 19 -
2.3 Snake model and GVF snake model - 25 -
Chapter 3.Method and Materials - 30 -
3.1 Data acquisition - 30 -
3.2 Automated lung area location - 32 -
3.3 Automated lung area enhancing and classifying - 36 -
3.4 Multi-snake model - 41 -
3.5 Artery threshold - 44 -
3.6 ROC curve - 48 -
Chapter 4. Result - 50 -
4.1 Simulation result - 50 -
4.2 Ranking result - 57 -
4.3 Application: - 61 -
Comparison with lung scintigraphy images - 61 -
Chapter 5 Discussion and conclusion - 65 -
References - 67 -
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