肺動脈逆流(pulmonary regurgitation, PR)嚴重程度、右心室擴張和不良運動耐受度是評估修復後法洛氏四重症(repaired Tetralogy of Fallot, rTOF)患者是否應考慮再次進行肺動脈瓣置換術(pulmonary valve replacement, PVR)的三個重要指標。在過去，計算PR分數需要在肺動脈中取得至少一張 2D 相位對比磁共振影像(phase contrast magnetic resonance image, PC MRI)，這導致rTOF患者的掃描時間較長。 此外，PR引起的心臟變化的機制、右心室擴張對左心室變化的影響以及不良運動耐受度引起的心臟變化對於平衡穩態自由旋進動態影像(balanced steady-state free precession cine images, bSSFP cine images)中之心臟結構和灰度強度的細微變化機制尚不清楚。此外，傳統的心血管磁振造影(cardiovascular magnetic resonance, CMR)心室體積指數並不能完全區分上述危險因子。 儘管先前的研究已經證明傳統的未打對比劑T1值(native T1 values)可以區分TOF患者和正常對照組，但測量值仍存在重疊的情況。
CMR 放射組學(radiomics)提供比傳統影像分析更細微的心血管表型分析方法。 它有可能更深入地了解心血管型態和功能之間的複雜關係。 透過將radiomics與 bSSFP cine images和native T1 mapping相結合，我們的目標是提供心臟評估的多面向維度。透過這種整合，我們進一步揭示心室重塑的隱藏模式，識別細微的心肌紋理變化，並在PR ≥ 25%、PR ≥ 40%、右心室擴張、不良運動耐受度和radiomics衍生的表型特徵等數據之間建立連結。
此研究為 PVR 危險因素如何影響TOF 患者提供新的見解，包括潛在的獨特紋理變化和心室重塑模式。我們的研究表明，radiomics優於傳統的 CMR 指數，有助於改善由於 PVR 危險因子的影響而導致的心臟結構和組織變化的量化。 此外，所識別的組織特徵提供了超出傳統native T1 values的附加價值。 這些資訊有助於評估可能需進行PVR手術的風險因素。

Pulmonary regurgitation (PR) severity, right ventricular (RV) dilation, and exercise intolerance were three important indicators for assessing whether pulmonary valve replacement (PVR) should have been considered in repaired Tetralogy of Fallot (rTOF) patients. In the past, calculating PR fraction required at least one 2D phase contrast magnetic resonance image to be obtained in the pulmonary trunk, which resulted in longer scanning times for rTOF patients. Furthermore, the mechanisms underlying the changes in the heart caused by PR, the effects of RV dilation on left ventricular changes, and the alterations in the heart resulting from exercise intolerance, particularly in relation to subtle modifications in cardiac structure and grayscale intensity within balanced steady-state free precession (bSSFP) cine images, were not yet well understood. Additionally, conventional cardiovascular magnetic resonance (CMR) volumetric indices did not fully differentiate the aforementioned risk factors. Although conventional native T1 values had been demonstrated in a previous study to distinguish between rTOF patients and normal controls, there was still some overlap in the measurements.
CMR radiomics offered a more advanced approach to cardiovascular phenotyping than traditional image analysis. It had the potential to provide a deeper understanding of the complex relationship between cardiovascular morphology and function. By combining radiomics with bSSFP cine images and native T1 mapping, we aimed to unlock multifaceted dimensions of cardiac evaluation. Through this integration, we hoped to uncover hidden patterns of ventricular remodeling, identify subtle myocardial texture variations, and establish correlations between variables such as PR ≥ 25%, PR ≥ 40%, RV dilation, exercise intolerance, and radiomics-derived phenotypic profiles.
This study provided novel insights into how factors related to PVR impacted rTOF patients, including potential distinctive textural variations and ventricular remodeling patterns. Our study demonstrated that radiomics, surpassing conventional CMR indices, facilitated the improved quantification of changes in cardiac structure and tissue due to the effects of PVR risk factors. Moreover, the discerned tissue characteristics offered additional value beyond conventional native T1 values. This information could aid in assessing risk factors for PVR procedures.

致謝 ii
摘要 iv
Abstract v
CONTENTS vii
FIGURES xi
TABLES xx
Chapter 1 Introduction 1
1.1 Tetralogy of Fallot 1
1.2 Diagnosis of TOF 2
1.2.1 Echocardiography 2
1.2.2 Cardiovascular Magnetic Resonance Imaging 5
1.3 Pulmonary Valve Replacement 7
1.3.1 Pulmonary Regurgitation 8
1.3.2 RV Dilation 10
1.3.3 Ventricular Interaction 11
1.3.4 Exercise Intolerance 11
1.4 Radiomics 12
1.4.1 Radiomic Analysis of Cine Images 13
1.4.2 Radiomic Analysis of Native T1 Mapping 15
1.5 Motivation 17
Chapter 2 Theory 19
2.1 Phase Contrast MRI 19
2.2 Balanced Steady State Free Precession Imaging 21
2.3 Modified Look-Locker Inversion Recovery 23
Chapter 3 Methodology 26
3.1 Study Cohort 26
3.2 MRI Acquisition 26
3.2.1 Cine Images 26
3.2.2 Native T1 Mapping 27
3.3 Radiomics Procedure 27
3.4 Segmentation 30
3.4.1 Cine Images 30
3.4.2 Native T1 Mapping 32
3.5 Radiomic Feature Extraction 33
3.5.1 Shape Radiomic Features 34
3.5.2 First Order Radiomic Features 35
3.5.3 Texture Radiomic Features 35
3.6 Machine Learning Scheme 36
3.6.1 Normalization 37
3.6.2 Feature Selection Methods 37
3.6.3 Classification Methods 39
3.6.4 Model Building 43
3.7 Evaluation of the Models 44
3.7.1 Receiver Operating Characteristic Curve 44
3.7.2 Evaluation Metrics 45
3.8 Preprocessing Filters 47
3.9 Statistical Analysis 49
Chapter 4 Results & Discussion : Radiomic Analysis of Cine Images 50
4.1 Differentiating rTOF Patients from Normal Controls 50
4.1.1 Demographics of Study Cohorts 50
4.1.2 Pedictive Performance of Feature Selection and Classification Methods 54
4.1.3 Correlogram 57
4.1.4 Predictive Performance and Evaluation of the Models 57
4.1.5 Predictive Performance of Preprocessing Filters 59
4.1.6 Radiomics Signatures 61
4.2 Identifying rTOF Patients with PR ≥ 25% 63
4.2.1 Demographics of Study Cohorts 63
4.2.2 Pedictive Performance of Feature Selection and Classification Methods 66
4.2.3 Predictive Performance and Evaluation of the Models 68
4.2.4 Predictive Performance of Preprocessing Filters 71
4.2.5 Radiomics Signatures 73
4.3 Identifying rTOF Patients with PR ≥ 40% 75
4.3.1 Demographics of Study Cohorts 75
4.3.2 Pedictive Performance of Feature Selection and Classification Methods 78
4.3.3 Predictive Performance and Evaluation of the Models 80
4.3.4 Predictive Performance of Preprocessing Filters 83
4.3.5 Radiomics Signatures 85
4.4 Identifying rTOF Patients with RV Dilation 87
4.4.1 Demographics of Study Cohorts 87
4.4.2 Predictive Performance of Feature Selection and Classification Methods 91
4.4.3 Predictive Performance and Evaluation of the Models 93
4.4.4 Predictive Performance of Preprocessing Filters 96
4.4.5 Radiomics Signatures 98
4.5 Identifying rTOF Patients with Exercise Intolerance 101
4.5.1 Demographics of Study Cohorts 101
4.5.2 Predictive Performance of Feature Selection and Classification Methods 104
4.5.3 Predictive Performance and Evaluation of the Models 106
4.5.4 Predictive Performance of Preprocessing Filters 109
4.5.5 Radiomics Signatures 111
4.6 Discussion 114
4.6.1 Differentiating rTOF patients from normal controls 114
4.6.2 Identifying rTOF patients with PR ≥ 25% 115
4.6.3 Identifying rTOF patients with PR ≥ 40% 116
4.6.4 Identifying rTOF patients with RV dilation 117
4.6.5 Identifying rTOF patients with exercise intolerance 118
Chapter 5 Results & Discussion : Radiomic Analysis of Native T1 Mapping 121
5.1 Differentiating rTOF Patients from Normal Controls 121
5.1.1 Demographics of Study Cohorts 121
5.1.2 Region Native T1 Value 124
5.1.3 Predictive Performance of Feature Selection and Classification Methods 125
5.1.4 Predictive Performance and Evaluation of the Models 127
5.1.5 Predictive Performance of Preprocessing Filters 130
5.1.6 Radiomics Signatures 132
5.2 Identifying rTOF Patients with PR ≥ 25% 134
5.2.1 Demographics of Study Cohorts 134
5.2.2 Segmental Mid Slice Native T1 Value 137
5.2.3 Predictive Performance of Feature Selection and Classification Methods 137
5.2.4 Predictive Performance and Evaluation of the Models 143
5.2.5 Predictive Performance of Preprocessing Filters 145
5.2.6 Radiomics Signatures 148
5.3 Identifying rTOF Patients with PR ≥ 40% 150
5.3.1 Demographics of Study Cohorts 150
5.3.2 Segmental Mid-slice Native T1 Value 152
5.3.3 Predictive Performance of Feature Selection and Classification Methods 153
5.3.4 Predictive Performance and Evaluation of the Models 159
5.3.5 Predictive Performance of Preprocessing Filters 161
5.3.6 Radiomics Signatures 163
5.4 Identifying rTOF Patients with RV Dilation 165
5.4.1 Demographics of Study Cohorts 165
5.4.2 Segmental Mid-slice Native T1 Value 168
5.4.3 Predictive Performance of Feature Selection and Classification Methods 169
5.4.4 Predictive Performance and Evaluation of the Models 175
5.4.5 Predictive Performance of Preprocessing Filters 177
5.4.6 Radiomics Signatures 180
5.5 Identifying rTOF Patients with Exercise Intolerance 182
5.5.1 Demographics of Study Cohorts 182
5.5.2 Segmental Mid-slice Native T1 Value 184
5.5.3 Predictive Performance of Feature Selection and Classification Methods 185
5.5.4 Predictive Performance and Evaluation of the Models 191
5.5.5 Predictive Performance of Preprocessing Filters 193
5.5.6 Radiomics Signatures 195
5.6 Discussion 197
5.6.1 Differentiating rTOF patients from normal controls 197
5.6.2 Identifying rTOF patients with PR ≥ 25% 198
5.6.3 Identifying rTOF patients with PR ≥ 40% 199
5.6.4 Identifying rTOF patients with RV dilation 201
5.6.5 Identifying rTOF patients with exercise intolerance 203
Chapter 6 Discussion & Conclusion 205
6.1 Discussion 205
6.1.1 Differentiating rTOF patients from normal controls 205
6.1.2 Identifying rTOF Patients with PR severity 207
6.1.3 Identifying rTOF Patients with RV Dilation 209
6.1.4 Identifying rTOF Patients with Exercise Intolerance 211
6.2 Limitation 214
6.3 Conclusion 215
6.4 Future work 216
References 217
Appendix 226
Response of Oral Defense 226
Abbreviation List 231
Definition List of Quantitative Indices 233
Plagiarism Detection 234

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