擴散權重影像與神經纖維追蹤可以用來探討腦區域之間的連結性，目前透過網路分析方式已經證實腦網路是有小世界的特性，最近也有研究不同受試者或者是病人之間的網路連結量測集中程度，但是擴散權重影像所運算出來的網路參數中間要經過很多步驟，這些中間步驟可能會影響到網路參數。所以有必要對於量測網路參數的受試者間變異性和重複量測重現性進行研究。本研究的目標是利用機率式神經纖維追蹤術量測大腦網路參數的重現性，探討三個會影響計算網路參數的重現性的變因，分別是，路徑定義方式、有無損耗正規化、受試者群體的網路連結篩選機制。變異係數定義（Coefficient of Variance, CV）為標準差除以平均值，分別計算二次量測之間的變異係數（CVwithin），以及受試者之間的變異係數（CVbetween），另外也計算組內相關係數（Intraclass correlation coefficient, ICC）。 掃描30受試者（15男，15女，年齡20~26）。每人掃描二次，並利用機率式神經纖維追蹤術計算網路連結，網路節點則是使用AAL標準模板定義的節點。若使用Wij = 1 – Pij定義長度，三項網路參數（區域效率、全域效率及損耗）重現性皆可接受（CVwithin<1.08%, CVwithin ≤ 10% and ICC > 0.7）。如果使用Wij=1/Pij定義長度，其損耗的CVwithin相較於Wij = 1 – Pij的大。如果長度的全距大，區域效率會不尋常地增加。如果二次掃描分別實施連結篩選，全域效率的CVwithin會較大。 本研究探討不同的網路建構方式將會影響測試內重現度，不同的研究團隊，縱使是採用相同的受試者群體和相同的儀器，所發表出來的網路參數可能會因為纖維追蹤術造成的誤差而不一致，因此實驗必須謹慎的分析資料以及闡述結果。 / Diffusion tensor imaging (DTI) with associate tractography can be used to access the connectivity of cortical regions in brain. Network analysis applied to connectivity matrix has demonstrated that brain has small world property. Recent studies also use network analysis to study the variation of concentricity among different group of subjects and patients. However the estimation of network metrics from DTI takes sophisticated processing steps. These intermediate steps may influence the estimation of network metric. It is therefore needed to investigate the potential variation of estimated network metrics using reproducibility test. The goal is to study the reproducibility of network properties derived from diffusion connectivity matrix constructed using probabilistic tractography. The effects of three factors on the reproducibility of network metrics estimation were studied. They are definition of path lengths of network matrix, path with and without cost normalization, the application of threshold to subjects groups. Coefficient of Variation (CV) defined as standard deviation divided by mean is used to test the intra-session (CVwithin) and inter subject (CVbetween) variability. Intra-class correlation coefficient (ICC) was also calculated. Images were acquired from 30 healthy participants (15 male, 15 female, aged 20-26 years). Each subject was scanned twice, denoted as N1 and N2. Probabilistic tractography was performed to mapping of cortico-cortical anatomical connections between regions defined from an anatomical atlas. All three of the tested network metrics (local efficiency, global efficiency and cost) were identified as acceptable (CVwithin < 1.08%, CVwithin ≤ 10% and ICC > 0.7) using path length defined as Wij = 1 – Pij. When the path length is defined as Wij = 1/Pij, cost showed higher CVwithin compared to Wij = 1 – Pij. It is unusual that local efficiency increase when the range of path length of edges is large. Global efficiency showed higher CVwithin as threshold is applied to N1 and N2 separately compared to both scans together. The present study revealed that different ways to construct cortical network had an effect on intra-session reproducibility. Our study also showed that despite evaluation of identical subjects using the same MRI system, variation of network metrics may be found by different research groups due to the potential errors from tractography. Replication of the experiment need to be carefully analyzed and interpreted.

近來三維數值城市及數碼城市（Cyber City）為各界極欲發展及研究的課題，為了要增加三維數值城市的擬真性及美觀程度，通常是將建物模型敷貼真實拍攝之牆面影像，增加三維模型的細緻化程度。而欲精確的敷貼牆面紋理影像，必須嚴密地將所拍攝之近景影像定位定向，一般採用光束法區域平差解算，此時需加上適當的控制點控制資訊才能完成，因此控制點控制資訊若來自地面測量將相當耗費成本。多年來，各地方政府製作大比例尺地形圖時已拍攝相當多的航照影像，可用來做為上述的控制資訊，亦即航空影像控制實體，若能使用這些航空影像控制實體作為控制資訊，不但可有效利用資源，亦能減少控制點取得所需花費的成本。因此，本研究將使用航空影像控制實體所提供的控制資訊做為控制來源。 本研究探討以航空影像控制實體作為控制資訊時，使用非量測型相機以類似傳統航測拍攝方式及旋轉多基線交向拍攝方式拍攝涵蓋建物牆面的目標區影像後，於最少控制且不同控制分布時，對光束法區域平差精度之影響。因使用非量測型相機，故本研究先以iWitnessPRO近景攝影測量軟體率定相機參數，接著以PHIDIAS近景攝影測量軟體解算光束法區域平差。過程中探討使用航空影像控制實體作為控制資訊時，於最少控制且不同控制分布時，加入附加參數解算的自率光束法區域平差與與一般光束法區域平差之精度。根據實驗結果，低樓層取像的光束法區域平差之檢核點RMSE精度，其結果大多可應用於LOD 3精度等級的牆面敷貼。另，因都市地區高樓林立，狹小巷弄多，有鑒於此，本研究使用旋轉多基線交向攝影，結果顯示其將有機會運用於近景攝影測量LOD 3精度等級的牆面紋理敷貼。 / Recently, the studies about the cyber city have become a popular topic. For improving the level of detail of cyber city, photo-realistic textures from images are mapped onto the surfaces of 3D building models. Before the accurate texture mapping, bundle block adjustment can be performed to recover the parameters of exterior orientation for each close-range images more accurate and more precise, where the control information is necessary. For the past years, many aerial photogrammetry projects were done by local governments for the mapping of 1/1000 topographic maps. Those historic aerial images can be used as control information to reduce the cost and increase the efficiency. Therefore, this study investigates the accuracy of bundle block adjustment about non-metric close-range images, taken from the ways similar to the traditional aerial photogrammetry and the rotating multi-baseline photogrammetry, by using control entities from historic aerial images as the minimal controls under various control distributions. Since the non-metric camera is used for collecting the close-range images, the iWitnessPRO software is utilized for camera calibration. After that, the PHIDIAS software, a close-range photogrammetry software, is employed to performed the bundle block adjustment. During performing the bundle block adjustment, the camera parameters are regarded as unknowns and determined, called as self-calibration bundle adjustment. The results of self-calibration bundle adjustment will be compared with conventional bundle adjustment. The test results show that the accuracy of most self-calibration bundle adjustment about close-range images covered with low buildings can be used for the application of LOD 3 texture mapping. Moreover, the test results of using close-range images from rotating multi-baseline photogrammetry in urban areas show the potential possibility for LOD 3 texture mapping in urban areas with high buildings and narrow alleys.