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研究生:黃芊丰
研究生(外文):Chien-Feng Huang
論文名稱:針對高擴散強度擴散權重影像的全自動運動假影校正演算法
論文名稱(外文):A correction method for motion-induced artifacts in high b-value diffusion MRI
指導教授:曾文毅曾文毅引用關係
指導教授(外文):Wen-Yih Isaac Tseng
口試日期:2017-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:醫療器材與醫學影像研究所
學門:醫藥衛生學門
學類:其他醫藥衛生學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:70
中文關鍵詞:擴散磁振造影運動假影運動假影校正高擴散敏感度
外文關鍵詞:diffusion MRImotionartifactmotion correctionhigh b-value
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磁振造影技術擁有以非侵入性的方式偵測神經纖維走向的能力,因此已成為探討大腦白質神經束的主要方法且廣泛地應用於臨床診斷與腦神經科學研究上。為了更精確地計算出白質複雜且細微的結構,近年來使用各種不同擴散梯度方向與強度組合的擴散影像漸漸被發展出來,如: 擴散頻譜影像及Q球擴散影像。但在擴散影像的掃描過程中,往往會因為病人躁動而造成影像的訊號喪失(signal dropout)與影像位置前後不一(misalignment)的問題,擁有這些問題的影像將會造成後續研究分析的錯誤,因此在分析前一定要將此類受到破壞的影像排除在分析之外或是校正好再使用。目前無論是一般擴散強度的擴散磁振造影或是高強度的擴散磁振造影都沒有辦法在事後將消失的訊號校正回來;而在影像位置前後不一致的問題上,一般擴散強度的擴散磁振造影可以利用腦部影像的輪廓或組織結構的特色來對位以補救回被破壞的影像,但基於高擴散強度的擴散磁振造影其影像組織特色與輪廓都非常地模糊,並不適用這樣的校正法。
因此,本論文發展了一套適用於高擴散強度擴散磁振造影的自動校正演算法,可將訊號喪失及影像前後不一致的問題在事後校正回來,使其成為可以分析使用的影像。本論文將分為以下四部分做說明: 第一為說明偵測訊號喪失的原理及演算法;第二為說明校正訊號喪失的原理及演算法;第三為說明偵測及校正影像前後位置不一問題的演算法;最後,我們將前三部份演算法結合成一套全自動校正系統並說明其校正的步驟與細節。
總結而言,本論文成功發展出一套適用於高擴散強度磁振造影的運動假影演算法,可在事後以全自動的方式將訊號喪失及影像位置前後不一致的問題補救回來。這樣的演算法可以救回許多本來要被丟棄的珍貴影像(如:罕見疾病病患的影像),讓後續的分析能順利進行,且方法簡單方便,無須於事前事後加裝任何軟硬體。
Diffusion MRI is becoming increasingly important for clinical and neuroscience studies owing to its capability to depict microstructural properties of white matter. For a more accurate estimation of diffusion index to reflect microstructural properties, recent advances in diffusion MRI techniques, such as diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging, acquire diffusion-weighted (DW) images with multiple diffusion sensitivities and directions. Due to the use of strong diffusion gradients, these techniques are sensitive to head motion, which can result in signal dropout and image misalignment. These artifacts may then lead to errors in diffusion index calculation. Therefore, it is necessary to discard or correct these degraded data in the subsequent analysis. Currently, there are no effective methods that use post-processing algorithms to restore signal dropouts, in either clinical or high b-value diffusion MRI. As for misalignment, the most frequently adopted approach is to align the images to a reference image based on image features. Unfortunately, this method will fail to correct high-b-value diffusion MRI, owing to its blurred edges of the tissue and outline of the brain.
Therefore, this thesis develops an automatic post-processing algorithm to correct signal dropout and misalignment for diffusion MRI. Images that would have been discarded can now be restored and made available for subsequent image analysis. This thesis is divided into four parts. First, we will explain the principle and algorithm for signal dropout detection. Second, we will explain the principle and algorithm for signal dropout correction. Third, we will explain the principle and algorithm for misalignment correction. Finally, we will combine the above three parts into an automatic motion correction algorithm and explain the details and steps of this algorithm.
In summary, this thesis successfully develops a motion correction algorithm that can be used in high b-value diffusion MRI and can correct signal dropout and misalignment retrospectively. This algorithm can salvage precious images, such as images of patients with rare diseases, that would have been discarded previously and allow for subsequent analysis of these images. Furthermore, this method is easy to implement, and does not require any additional software and hardware.
Chapter 1 Introduction 1
1.1 Diffusion MRI 1
1.1.1 What is diffusion? 1
1.1.2 Diffusion MRI 1
1.1.3 High b-value diffusion MRI 3
1.2 Head motion induced artifacts 3
1.3 Literature review 4
1.3.1 Signal dropout 4
1.3.2 Misalignment 5
1.4 The goal of this thesis 6
Chapter 2 Methods 7
2.1 DSI acquisition 7
2.2 Signal dropout detection 8
2.2.1 Theory 8
2.2.2 Parameters for detecting signal dropout 11
2.2.2.1 SI,SD,SV 11
2.2.2.2 〖STD〗_(SD(v)),〖STD〗_SD,〖STD〗_SV 14
2.2.2.3 A metric for judging signal dropout 15
2.2.3 Detection procedures 16
2.2.4 Subjects and validation 21
2.3 Signal dropout correction 21
2.3.1 Theory 21
2.3.2 Signal dropout simulation 22
2.3.3 Correction procedures 22
2.3.3.1 Signal dropout correction procedure: discrete signal dropouts 25
2.3.3.2 Signal dropout correction procedure: continuous signal dropouts 28
2.3.4 Validation 31
2.4 Misalignment correction 32
2.4.1 Theory 32
2.4.1.1 Projection profile 32
2.4.1.1.1 Procedure to calculate projection profile 32
2.4.1.1.2 Properties of the projection profile 35
2.4.1.1.3 Detect rotation angle 39
2.4.2.1 Cerebellar slices detection 40
2.4.2.1.1 Percentage error map 40
2.4.2.1.2 Signal intensity ratio 43
2.4.3 Misalignment simulation 46
2.4.4 Misalignment correction procedure 47
2.4.5 Validation 48
2.5 Automatic motion correction algorithm: a feasibility test 48
2.5.1 Subjects 48
2.5.2 Automatic motion correction algorithm 48
2.5.3 Validation 49
Chapter 3 Results 51
3.1 Signal dropout detection 51
3.2 Signal dropout correction 52
3.3 Misalignment correction 54
3.4 Automatic motion correction algorithm: a feasibility test 56
Chapter 4 Discussion 58
4.1 The deleterious effects of head motion on functional difference and GFA map in simulated and live experiments 58
4.2 Improvement in functional difference and GFA map of simulated and live experiments after signal dropout and misalignment correction 59
4.3 Insignificant improvement in automatic motion correction algorithm 59
4.4 Improvement and advantage 60
4.4.1 Signal dropout detection 60
4.4.2 Signal dropout correction 61
4.4.3 Misalignment correction 62
4.5 Limitations 63
4.5.1 Signal dropout detection 63
4.5.2 Signal dropout correction 65
4.5.3 Misalignment correction 66
Chapter 5 Conclusions 67
References 68
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18.Anderson, Adam W., and John C. Gore. "Analysis and correction of motion artifacts in diffusion weighted imaging." Magnetic resonance in medicine 32.3 (1994): 379-387.
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