Diffusion Tensor Imaging and Fractional Anisotropy: Imaging Biomarkers in Early Parkinson’s Disease

Description

The book covers all aspects of one of the most advanced magnetic resonance imaging techniques, namely Diffusion Tensor Imaging (DTI) and Fractional Anisotropy (FA) values in early Parkinson’s disease (PD) patients. It provides step-by-step descriptions of DTI and its use in the early diagnosis of Parkinson’s disease by using FA values at several grey and white matter regions of the brain with helpful MRI DTI images. It includes clear flow charts with MRI DTI imaging protocol for Parkinson’s disease to aid in early diagnosis and treatment. The book covers essential information on anatomy and pathology in Parkinson’s disease and includes dedicated chapters on diffusion tensor imaging and FA in Parkinson’s disease. Additionally, it covers the role of magnetic resonance imaging in Parkinson’s disease with routine findings for Parkinson’s disease in MRI, followed by advanced imaging biomarkers and predictors in Parkinson’s disease.  The book will assist the practitioners in the early detection of Parkinson’s disease using specific imaging biomarkers with the help of FA values, which will help in the early treatment of PD patients and thus extend and improve their quality of life. It will also be relevant for MD radiology, M.Sc. medical imaging technology students/trainees and Ph.D. medical imaging graduates as well as B.Sc MIT students. Foreword Preface Preface Contents About the Authors List of Figures List of Tables 1: History and Basic Principles of Magnetic Resonance Imaging 1.1 Introduction to MRI 1.2 History and Roadmap to Innovations in MRI 1.3 Atomic Structure 1.3.1 Motion Within the Atom 1.3.2 Hydrogen as MR Active Nucleus 1.4 Alignment and Precession 1.4.1 Alignment 1.4.2 Precession 1.5 Resonance, Signal Generation and Image Decoding 1.5.1 The MR Signal 1.5.2 Steps in MR Image Encoding 1.6 Understanding the Physics Behind Magnetic Resonance Imaging (MRI) 1.7 k-Space 1.7.1 Fast Fourier Transform (FFT) 1.7.2 k-Space Functions and Characteristics 1.7.3 Data Sampling Techniques 1.7.4 k-Space Traversal 1.7.5 k-Space in Pulse Sequences 1.8 Classification of MRI Pulse Sequences 1.9 Basic Pulse Timing Parameters References 2: Image Contrast Mechanisms in Diffusion-Weighted and Diffusion-Tensor Imaging 2.1 What Do We Understand by Image Contrast in MRI? 2.1.1 MRI-Specific Composition and Characteristics of Fat and Water in the Human Body 2.1.2 What Is T1 Recovery? Process of T1 Recovery in Fat and Water 2.1.2.1 T1 Recovery in Fat (T1 Relaxation) 2.1.2.2 T1 Recovery in Water (T1 Relaxation) 2.1.3 What Is T2 Decay? Process of T2 Decay in Fat and Water 2.1.3.1 T2 Decay in Fat (T2 Decay) 2.1.3.2 T2 Decay in Water (T2 Decay) 2.1.4 What Is T1 Weighting? How Are T1-Weighted Images Formed? 2.1.5 What Is T2 Weighting? How Are T2-Weighted Images Formed? 2.1.6 Proton Density Image Formation by Masking T1 and T2 Weighting 2.2 Mechanisms of Image Contrast in Diffusion-Weighted Imaging (DWI) 2.3 DTI-Based Scalar Derivative Fractional Anisotropy (FA) and Its Corresponding Image Contrast Mechanisms References 3: DWI Physics and Imaging Techniques 3.1 Physics from Diffusion-Weighted to Diffusion-Tensor Imaging 3.2 Introduction to Diffusion-Weighted Imaging (DWI) 3.2.1 Physics Behind Diffusion 3.2.2 Brownian Motion 3.2.3 b-Factor 3.2.4 Measurement of DWI: How Does Diffusion Affect the MR Signal? 3.2.5 DWI Image Contrast and ADC Calculation 3.2.6 Pulse Sequences Employed for DWI 3.2.6.1 Echo-Planar Imaging in DWI-EPI Technical Overview 3.2.6.2 Parallel Imaging Technical Overview 3.2.7 DWI Artefacts 3.2.7.1 Motion Artefacts 3.2.7.2 Eddy Currents 3.2.8 DWI Applications References 4: Advanced MRI Neuroimaging Technique: Diffusion-Tensor Imaging 4.1 Introduction to Diffusion-Tensor Imaging (DTI) 4.1.1 DTI Evolution 4.2 Diffusion Anisotropy 4.3 Diffusion-Tensor Matrix 4.4 Trace Imaging 4.5 Measurement of Diffusion-Tensor Data 4.6 Anisotropy Indices 4.6.1 Radial or Perpendicular Diffusivity (RD) 4.6.2 Mean Diffusivity (MD) 4.7 DTI Applications References 5: Fractional Anisotropy: Scalar Derivative of Diffusion-Tensor Imaging 5.1 Introduction and Overview of Fractional Anisotropy (FA) 5.2 Clinical Implications of FA in Brain White Matter 5.2.1 ROI Analysis for FA in the Brain 5.3 Quantitative Parameters Affecting FA Values 5.4 Case Reports and Case Series Related to Diffusion-Tensor Imaging and Fractional Anisotropy Values 5.4.1 Evidence on Influence of b-Value and TE on FA Values 5.4.1.1 b-Value Effects on FA 5.4.1.2 b-Value and Time of Echo (TE) Effects on FA 5.4.1.3 High- and Low-Resolution DTI Effects on FA 5.4.1.4 An Animal Study on Time of Echo (TE) on FA 5.4.1.5 Animal Study (Rats) 5.4.1.6 The Influence of SNR on FA 5.4.1.7 DTI Parameter Effects of b-Value and Field Strength 5.5 FA in Normative Healthy Brain White Matter 5.5.1 Evidence of Human Studies on Normative and Neurodegenerative Disorders 5.5.1.1 Application of DTI and FA 5.5.1.2 Normal Pressure Hydrocephalus and DTI (FA) 5.5.1.3 Normative FA Using Small and High-Resolution Diffusion-Tensor Images 5.5.1.4 FA and White Matter Alteration in Idiopathic Standard Hydrocephalus Pressure (INPH) 5.5.1.5 Radiofrequency Coils and FA 5.5.1.6 Normative FA in an Adult Healthy Population 5.5.1.7 FA and Brain Myelination in Healthy Volunteers 5.5.1.8 Normative FA of Brain Structures in Children/Infants 5.5.1.9 Magnetic Field Strengths and FA 5.5.1.10 MR Magnets and FA as a Robust Diffusivity Measure 5.5.1.11 FA and MD Using SNR Measurements 5.5.1.12 Diffusion-Weighted Directions and FA 5.5.1.13 Q-Ball Imaging and FA 5.5.1.14 Decreasing FA with an Increase in Age References 6: Diffusion-Tensor Imaging Instrumentation 6.1 MRI Hardware for Diffusion-Tensor Imaging 6.2 Superconducting Magnet Specifications for Diffusion-Tensor Imaging 6.2.1 Gradient Coils for Diffusion-Tensor Imaging 6.2.2 Radiofrequency Coils for Diffusion-Tensor Imaging 6.2.3 Shim Coils in Diffusion-Tensor Imaging 6.3 Software Requirements for DTI Applications References 7: Diffusion-Tensor Imaging and Fractional Anisotropy Protocol at 1.5-T MRI for Early Parkinson’s Disease 7.1 Introduction: Diffusion-Tensor Imaging Protocol for Obtaining FA at the Brain White and Grey Matter 7.2 Quantitative Factors Affecting DTI and FA Values 7.2.1 Specific Indications for DTI in Early Parkinson’s Disease 7.2.2 General Contraindications for MRI 7.2.3 Patient Preparation for MR-DTI Brain Examination 7.2.4 Technical Positioning Considerations for MR-DTI Brain 7.3 MR-DTI Protocol for Early PD 7.3.1 MRI Conventional Brain Routine Sequences for PD 7.3.2 DTI-MR Brain Planning for Estimating FA Values 7.3.3 DTI Post-processing for FA Values References 8: Introduction: Types of Parkinson’s Disease 8.1 Background 8.2 DTI Matrix 8.3 Comparing FA Values of PD with Other Neurodegenerative Diseases 8.4 Diffusion Tensor Imaging: Tumours (Neoplastic and Non-neoplastic Lesion Characterization with DTI) 8.5 Surgical Extent and Outcome vs. DTI and Tractography References 9: Evidence of Fractional Anisotropy in Parkinson’s Disease 9.1 Background 9.2 Clinical Use of FA in the Brain 9.3 FA’s Role in Parkinson’s Disease 9.4 FA Evidence as Imaging Markers in PD 9.5 Case Reports and Case Series on PD-FA, Corpus Callosum and PD 9.6 FA, Substantia Nigra and PD 9.7 FA, White and Grey Matter of the Brain and PD 9.7.1 Variables and Learning Definition Terms 9.7.1.1 Diffusion-Weighted Imaging 9.7.1.2 b-Value 9.7.1.3 ADC 9.7.1.4 Time of ECHO (TE) 9.7.1.5 Diffusion-Tensor Imaging 9.7.1.6 Fractional Anisotropy (FA) References 10: FA Characteristics as Imaging Biomarkers Among the Indian Population in Early Parkinson’s Disease 10.1 Background 10.2 Clinical and Demographic Characteristics 10.2.1 UPDRS Criteria in PD 10.2.1.1 Step 1: Parkinsonian Syndrome Diagnosis 10.2.1.2 Step 2: Exclusion Criteria for Parkinson’s Disease 10.2.1.3 Step 3: Supportive Prospective Positive Criteria for Parkinson’s Disease [1] 10.2.2 Predictive Performance of Diffusion-Tensor Imaging 10.3 Imaging Biomarkers in Early PD 10.3.1 Independent Sample t-Test Findings Among Indian Population 10.3.2 Brain Region-Specific FA Range 10.3.3 Receiver Operator Characteristics Curve Findings in Early PD 10.3.4 Significant Predictors for PD vs. Control Group 10.4 Predicting Clinical Outcomes Using DTI 10.4.1 Significance of Inclusion of GM and WM for FA in PD 10.4.2 Substantia Nigra and Early PD 10.4.3 Putamen and Early PD 10.4.4 Corpus Callosum and Early PD 10.4.5 Cerebral Peduncles and Early PD 10.4.6 Pons and Early PD 10.5 Brain Region Involvement in Early PD 10.5.1 Conventional MRI vs. DTI in PD 10.6 Clinical and Future Implications References Glossary