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  • 1. Overview
    • 1.1. Development Team
    • 1.2. Support
  • 2. Workflows
  • 3. Installation
  • 4. Data Preparation
  • 5. Image Registration
    • 5.1. Overview
      • 5.1.1. Why Registration is Important
      • 5.1.2. Registration Pipeline Architecture
    • 5.2. Command-Line Interface
      • 5.2.1. Complete Automated Workflow (Recommended)
      • 5.2.2. Step-by-Step Workflow
    • 5.3. Required Inputs
      • 5.3.1. input_mat_file
      • 5.3.2. output_directory
    • 5.4. Optional Inputs
      • 5.4.1. --processes <num> (or -p <num>)
      • 5.4.2. --template <template_file> (Registration Step Only)
      • 5.4.3. --template-strategy <strategy>
      • 5.4.4. --res X Y Z (Conversion Step Only)
    • 5.5. Output
      • 5.5.1. 1. Intermediate NIfTI Files
      • 5.5.2. 2. Registered NIfTI Files
      • 5.5.3. 3. Final Registered .mat File
      • 5.5.4. 4. Visualization Files (Optional)
    • 5.6. Example Usage
      • 5.6.1. Example 1: Basic Registration Workflow
      • 5.6.2. Example 2: Custom Resolution and Parallel Processing
      • 5.6.3. Example 3: Custom Template Registration
      • 5.6.4. Example 4: MATLAB Integration
    • 5.7. Processing Time and Hardware Requirements
      • 5.7.1. Hardware Requirements
      • 5.7.2. Processing Time Estimates
    • 5.8. Technical Notes
      • 5.8.1. Registration Algorithm Details
      • 5.8.2. Template Selection Strategies
    • 5.9. Troubleshooting
      • 5.9.1. Common Issues and Solutions
    • 5.10. Performance Optimization Tips
  • 6. Spectroscopic Image Estimation
    • 6.1. Selection of Beta
      • 6.1.1. Required Inputs
      • 6.1.2. Optional Inputs
  • 7. Spectroscopic Image Visualization
    • 7.1. Plotting spectroscopic images
      • 7.1.1. Required Inputs
      • 7.1.2. Optional Inputs
      • 7.1.3. Output
    • 7.2. Plotting average spectra
      • 7.2.1. Required Inputs
      • 7.2.2. Optional Inputs
      • 7.2.3. Output
    • 7.3. Plotting component maps
      • 7.3.1. Required inputs
      • 7.3.2. Optional Inputs
      • 7.3.3. Outputs
  • 8. Phantom Data
    • 8.1. Optional Inputs
    • 8.2. Outputs
  • 9. Cramér-Rao Bounds
    • 9.1. Required Inputs
    • 9.2. Optional Field Inside funcfile
    • 9.3. Output
  • 10. File formats
  • 11. Tutorials
    • 11.1. Converting NIfTI / DICOM data to the imgfile format
      • 11.1.1. Goal
      • 11.1.2. Step 1: Understand your NIfTI layout
      • 11.1.3. Step 2: Build the data variable
      • 11.1.4. Step 3: Set resolution
      • 11.1.5. Step 4: Set spatial_dim
      • 11.1.6. Step 5: Set the 4×4 transform matrix
      • 11.1.7. Step 6: Save the imgfile
      • 11.1.8. What about DICOM input?
    • 11.2. Registration for 2D Phantom data
      • 11.2.1. Introduction
        • What is Registration?
        • Registration Methods
        • Why Use GPU-Accelerated Registration?
      • 11.2.2. Traditional registration methods (e.g., FSL, ANTs) are CPU-based and can be slow for high-dimensional data. This tool uses:
      • 11.2.3. Goal
      • 11.2.4. Prerequisites
      • 11.2.5. Get Started
        • Step 1: Verify Input Data
        • Step 2: Set Up Working Directory
        • Step 3: Convert .mat to NIfTI Format
        • Step 4: Apply Nonlinear Deformation (Simulation)
        • Step 5: Register the Deformed Volumes
        • Step 6: Convert Back to .mat Format
        • Step 7: Assess Registration Quality
      • 11.2.6. Understanding the Registration Process
        • Multi-Stage Pipeline Details
        • Template Selection Impact
      • 11.2.7. Troubleshooting Common Issues
        • Issue 1: Registration produces blurry results
        • Issue 2: Registration fails to converge
        • Issue 3: “CUDA out of memory” error
        • Issue 4: Very slow processing (>12 hours)
      • 11.2.8. Advanced Topics
        • Customizing Registration Parameters
        • Batch Processing Multiple Datasets
        • Integration with Spectral Analysis Pipeline
      • 11.2.9. Output Files Summary
    • 11.3. Generate 2D Phantom data
      • 11.3.1. Goal
      • 11.3.2. Relevant links
      • 11.3.3. Get started
      • 11.3.4. Outputs
    • 11.4. Estimating Spectrum from Generated 2D Phantom data
      • 11.4.1. Goal
      • 11.4.2. Get started
      • 11.4.3. Output
    • 11.5. Visualising Estimated Spectra for 2D Phantom Data
      • 11.5.1. Goal
      • 11.5.2. Relevant downloads
      • 11.5.3. Get started
      • 11.5.4. Plot Spectroscopic image:
      • 11.5.5. Plot Average Spectra:
      • 11.5.6. Plot Component Maps:
    • 11.6. Generate 1D Phantom data
      • 11.6.1. Goal
      • 11.6.2. Relevant links
      • 11.6.3. Get started
      • 11.6.4. Outputs
    • 11.7. Estimating Spectrum from Generated 1D Phantom data
      • 11.7.1. Goal
      • 11.7.2. Get started
      • 11.7.3. Output
    • 11.8. Visualising Estimated Spectra for 1D Phantom Data
      • 11.8.1. Goal
      • 11.8.2. Relevant downloads
      • 11.8.3. Get started
      • 11.8.4. Plot Spectroscopic image:
      • 11.8.5. Plot Average Spectra:
      • 11.8.6. Plot Component Maps:
    • 11.9. Computing CRLB for a 2D Diffusion–\(T_2\) Model and comparing two encoding schemes:
      • 11.9.1. Goal
      • 11.9.2. Relevant links
      • 11.9.3. Get started
  • DRSuite GitHub Site
  • DRSuite Documentation
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