Multimodal Stroke Magnetic Resonance Imaging for Clinical and Pre-clinical Research = 임상 및 전임상 연구를 위한 뇌졸중 다중 자기공명영상기법

다국어 초록 (Multilingual Abstract)

Stroke is a leading cause of morbidity and death, caused by blocked cerebral blood flow. Acute ischemic stroke can be treated by revascularization of occluded vessels; however, the number of patients who can be treated in this manner is still limited and predicting treatment efficacy is difficult. Therefore, the development of magnetic resonance imaging (MRI) techniques to detect salvageable brain tissue to aid in selection of patients who can be treated is crucial. MRI can also be used in the later stages of ischemic stroke to assess spontaneous or therapy-induced recovery. However, considering that the lesion location and severity are varied with each patient, generalizing the mechanism of recovery is difficult. Therefore, it is crucial to develop MRI techniques for investigating functional recovery after stroke.
In the present study, I developed multimodal MRI techniques to select patients who could be treated from both the acute to late phases of stroke, to predict outcome, and assess their functional recovery. In Part 1, I developed a novel MRI technique to evaluate collateral circulation by post-processing of dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP) images. I also confirmed that the DSC-MRP-derived collateral grading system developed from this study could be used to select patients that can receive therapy, and to predict their prognosis. In Part 2, I developed an MRI-based monitoring method to assess the therapeutic efficacy of using mesenchymal stem cells (MSCs) in an experimental stroke model, using diffusion tensor imaging (DTI). In Part 3, functional MRI (fMRI) was used to investigate optimal MRI parameters for detection of recovery following treatment with MSC-derived extracellular vesicles (EVs). In addition, I also investigated characteristics of blood oxygen level-dependent fMRI signals.
In conclusion, multimodal MRI techniques can be used as promising tools for early diagnosis, predicting prognosis, and monitoring therapy-induced recovery after stroke.

목차 (Table of Contents)

• ABSTRACT 1
• GENERAL BACKGROUND 3
• Part 1. Collateral Flow Imaging 6
• Part 1.1. A novel MRI approach to collateral flow imaging in ischemic stroke 6
• 1.1.1. INTRODUCTION 6
• 1.1.2. MATERIALS AND METHODS 8
• 1.1.2.1. Patient Selection 8
• 1.1.2.2. Manual Postprocessing Techniques for the MRI-derived Collateral Flow Map 8
• 1.1.2.3. Automatic Postprocessing Techniques for the MRI-derived Collateral Flow Map 15
• 1.1.2.4. Collateral Grading on DSA and MRI-derived Collateral Flow Map 15
• 1.1.2.5. Treatment and Imaging Analysis 20
• 1.1.2.6. Outcome Measurements 20
• 1.1.2.7. Statistical Analysis 21
• 1.1.3. RESULTS 22
• 1.1.3.1. Correlation with Angiographic Collaterals 25
• 1.1.3.2. Predicting Outcome after Acute Stroke 27
• 1.1.4. DISCUSSION 34
• PART 1.2. Impact of slow blood filling via collaterals on infarct growth: Comparison between MR parameters for target mismatch vs. collateral status 40
• 1.2.1. INTRODUCTION 40
• 1.2.2. MATERIALS AND METHODS 42
• 1.2.2.1. Patient Selection 42
• 1.2.2.2. MRP Methods and Image Analysis 44
• 1.2.2.3. Post-processing Techniques to Generate an MRP-Derived Collateral Flow Map 45
• 1.2.2.4. Recanalization Therapy and TICI grading 49
• 1.2.2.5. Outcome Measurements 49
• 1.2.2.6. Statistical Analysis 49
• 1.2.3. RESULTS 51
• 1.2.4. DISCUSSION 60
• PART 2. Diffusion Tensor Imaging 63
• PART 2.1. Brain morphological and connectivity changes on MRI after stem cell therapy in a rat stroke model 63
• 2.1.1. INTRODUCTION 63
• 2.1.2. MATERIALS AND METHODS 65
• 2.1.2.1. Focal Cerebral Ischemia Model 65
• 2.1.2.2. Preparation of Serum and MSCs 65
• 2.1.2.3. Experimental Groups and Intravenous Infusion of hMSCs 66
• 2.1.2.4. MRI Image Acquisition 66
• 2.1.2.5. MRI Image Analysis 67
• 2.1.2.6. Behavioral Testing 70
• 2.1.2.7. Statistical Analysis 70
• 2.1.3. RESULTS 72
• 2.1.3.1. Functional Improvement after MSCs Treatment 72
• 2.1.3.2. Brain Morphological Changes 75
• 2.1.3.3. Brain Microstructure/Connectivity Changes 78
• 2.1.3.4. Correlation between Brain Morphological Changes and DTI 83
• 2.1.4. DISCUSSION 86
• PART 3. Functional Magnetic Resonance Imaging 91
• PART 3.1. MRI can be used to detect treatment efficacy of extracellular vesicles in a mouse model of stroke 91
• 3.1.1. INTRODUCTION 91
• 3.1.2. MATERIALS AND METHODS 93
• 3.1.2.1. Photothrombotic Stroke Model 93
• 3.1.2.2. Preparation of Extracellular Vesicles 93
• 3.1.2.3. Experimental Designs and Intravenous Infusion of EVs 94
• 3.1.2.4. MRI Image Acquisition 95
• 3.1.2.5. MRI Image Analysis 96
• 3.1.2.6. Behavioral Testing 98
• 3.1.2.7. Statistical Analysis 99
• 3.1.3. RESULTS 100
• 3.1.3.1. Brain Morphological Changes 100
• 3.1.3.2. Brain Microstructural and Anatomical Connectivity Changes 103
• 3.1.3.3. Functional Connectivity Changes 108
• 3.1.3.4. Functional Behavioral Recovery 110
• 3.1.3.5. Correlation between Behavioral Testing and MRI Parameters 112
• 3.1.4. DISCUSSION 116
• PART 3.2. Gradient-echo and spin-echo BOLD fMRI at ultrahigh fields of 9.4 T and 15.2 T 120
• 3.2.1. INTRODUCTION 120
• 3.2.2. MATERIALS AND METHODS 122
• 3.2.2.1. Animal Preparation and Electrical Forepaw Stimulation 122
• 3.2.2.2. fMRI 125
• 3.2.2.3. fMRI Data Analysis 128
• 3.2.3. RESULTS 130
• 3.2.3.1. Baseline Relaxation Time and TE Dependence of BOLD Contrast 130
• 3.2.3.2. Magnetic Field Strength and Echo-Type Dependencies of BOLD Signal Change 133
• 3.2.3.3. BOLD Signal Change in Superficial and Parenchyma Regions 136
• 3.2.4. DISCUSSION 139
• REFERENCES 146
• 국문초록 173