臺灣博碩士論文加值系統

Tc-99m HMPAO是一種用於腦血流分析的典型顯跡物(tracer)，利用Tc-99m HMPAO的單光子電腦斷層掃描(SPECT)腦部攝影便成為一項評估腦功能時相當常用的技術。
在本論文中，我們提出一套針對SPECT影像的分析系統，執行一系列的影像處理步驟，包含三維影像對位、腦部區域萃取、灰階值正規化，使所有三維腦部資料都對應到相同空間上，以便進行後續分析。接下來就將對齊的影像來作一項標準的統計分析─配對t檢定，來偵測出兩組影像中具有顯著差異的區域。最後我們就將所得之統計t圖譜一張張用彩色圖表示，指出活化病灶區所在。此結果圖對於醫師針對精神分裂症患者的病灶評估和定位是相當有幫助的。
除此之外，我們特別對藉由共同資訊量(mutual information, MI)最大化之影像對位作研究。首先描述用來導出機率分佈函數及算出MI值的共同統計圖(joint histogram)。接著再針對由部分體積(partial volume, PV)內插法導致的內插假象(interpolation artifact)作討論和回顧。然後我們提出以權重方式(weighting method)將區域共同機率(local joint probability)加入原有共同機率計算MI值的方法有效解決假象問題。在計算成本的考量之下，我們也同時建立一個多解析度架構以減少搜尋時間。
對位結果利用由實際影像設計之假體影像以及所得之臨床SPECT影像來作評估。我們證實此方法可達到次體素正確性(subvoxel accuracy)，並維持前後一致性(consistency)，同時所提之權重方法也優於傳統PV內插法。

Tc-99m HMPAO is a typical tracer used in the analysis of cerebral blood flow. Single photon emission computed tomography (SPECT) brain imaging utilizing Tc-99m HMPAO is thus a popular method to assess brain function.
In this thesis, we present an image analysis system for SPECT images that performs a series of image processing procedures including 3D image registration, brain extraction, and gray-level normalization, which map all the 3D brain data to the same space for further analysis. Afterward, the aligned images undertake a standard statistical analysis, the paired t test, to detect the areas that have significant deviations between the two images. The statistical t map is then represented with a color plot for each brain slice to indicate the activation foci. The resulting maps are found very helpful to doctors for the lesion evaluation and localization in the clinical diagnosis of schizophrenic patients.
Besides, a study on image registration by maximization of mutual information is also given. We address the concept of joint histogram, which is used to derive the probability distribution and thus compute the mutual information (MI) value. A well-known interpolation artifact problem caused from the partial volume (PV) interpolation is discussed and reviewed here. Hence, we propose a weighting method, which adds the local joint probability term to the original joint probability, to eliminate the artifacts effectively. Under the consideration to computational cost, we also construct a multiresolution hierarchy to reduce the search time.
The registration results are evaluated using the designed phantom and the acquired clinical SPECT images. We show that the subvoxel accuracy is achieved and the consistency is also maintained. It is also proved to be superior to the PV interpolation when the proposed weighting method is used.

Chapter 1 Introduction .................................................................... 1
1.1 Motivation ............................................................................ 1
1.2 Outlines .............................................................................. 3
Chapter 2 Medical Image Registration .......................................................4
2.1 Concepts ...............................................................................4
2.2 Related Issues of Registration .........................................................8
2.3 Review .................................................................................9
Chapter 3 Image Processing and Analysis Procedures ........................................ 14
3.1 Data Acquisition ...................................................................... 14
3.2 Image Registration .................................................................... 15
3.3 Brain Extraction ...................................................................... 17
3.4 Intensity Normalization ............................................................... 19
3.5 Statistical Analysis .................................................................. 20
Chapter 4 Mutual Information .............................................................. 22
4.1 Theory ................................................................................ 22
4.2 Joint Histogram ....................................................................... 25
4.3 Interpolation ......................................................................... 28
4.4 Optimization .......................................................................... 45
4.5 Multiresolution ....................................................................... 47
Chapter 5 Validation and Discussion ....................................................... 49
5.1 Designed Phantom Registration ......................................................... 49
5.2 Clinical Data Registration ............................................................ 54
5.3 Visual Inspection ..................................................................... 59
Chapter 6 Conclusion and Future Work ...................................................... 63
6.1 Conclusion ............................................................................ 63
6.2 Future Researches ..................................................................... 64
Appendix .................................................................................. 66
References ................................................................................ 67
Vita ...................................................................................... 72

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