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Volume Viewer Improved

A MATLAB graphical interface to visualize complex-valued N-D volumetric data.

Volume viewer improved (vvi) began in 2010 as an update to the volume viewer software for MATLAB written by Rafael O'Halloran in the Radiology MRI Research Group, University of Wisconsin - Madison. The original software was written for the analysis of dynamic imaging collected from GE Healthcare Signa and Aglient DirectDrive MRI scanners.

Purpose: The primary purpose of the vvi project has always been fast, easy viewing of complex-valued 3D and 4D datasets. That is, viewing of complex-valued MRI datasets with additional dynamic or parametric information (e.g. time-series data, data with multiple flip angles (FA), inversion times (TI), or echo times (TE)).

Key Features
  • Ability to select voxel to see display of additional information
  • Correct handling of complex-valued data
  • Correct handling of data with negative values
  • Correct handling of N-D (up to 4 dimensional) data
    • Automatic handling of multidimensional arrays
    • Ability to permute axes, swap z/t dimension
  • Tools for publication-quality figures
    • Multiple colormap options
    • Output plots and images or save to png/bmp/jpg/tif*
    • Antialiased image output
  • Responsive UI: unnecessary copies and operations on large matrices are minimised
  • Scalable UI, designed to look good and work correctly in Windows, Linux, and MacOS X
*TIFF output may not work correctly on OSX

Features in Progress
  • Ability to define and manage region of interest (ROI) locations
    • Display summary statistics such as mean and standard deviation
    • Ability to plot time course or parametric values
  • Advanced image processing features
    • Rescale or resample images to correct voxel dimensions
    • Show three-plane slice views of 3D data.
    • Resample data to arbitrary angles and slice locations (similar to spm_slice_vols)
  • Loading of images from both MATLAB workspace variables and file system (e.g. DICOM, NIfTI)
  • Ability to plot data against fitted curves for parameter estimation. For example:
    • T1 fitting from inversion recovery data
    • T1 fitting from variable flip angle (VFA / DESPOT) data
    • T2 fitting from multi-spin echo (MSE) data