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DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.

David S Smith1, Xia Li1, Lori R Arlinghaus1

  • 1Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA.

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We developed a fast, open-source toolkit for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) analysis. It offers significant speedups and comparable accuracy to existing tools, even with noisy data.

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is crucial for quantitative tissue analysis.
  • Existing DCE-MRI processing tools can be computationally intensive, limiting their widespread application.
  • There is a need for efficient and accurate open-source software for DCE-MRI data analysis.

Purpose of the Study:

  • To introduce and validate a novel, open-source toolkit (DCEMRI.jl) for rapid DCE-MRI data processing.
  • To assess the accuracy and performance of the toolkit against established phantoms and in vivo data.
  • To analyze the computational scaling of the toolkit for different problem sizes.

Main Methods:

  • Developed a fast, open-source toolkit (DCEMRI.jl) for DCE-MRI data processing.
  • Validated the toolkit using Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms.
  • Evaluated performance on a realistic in vivo breast DCE-MRI dataset.
  • Analyzed run time complexity with respect to the number of time points (N).

Main Results:

  • The toolkit achieved near-perfect data recovery on phantoms in noise-free conditions.
  • Demonstrated an estimated 10-20x speedup in run time compared to existing tools.
  • Achieved processing times of less than 1 second for a 192x192 breast image.
  • Run time per voxel scales as O(N^1.9), where N is the number of time points.
  • Showed comparable accuracy to other packages, even with noisy data.

Conclusions:

  • DCEMRI.jl provides a highly efficient and accurate solution for DCE-MRI data analysis.
  • The toolkit's speed and accuracy make it suitable for both research and clinical applications.
  • Its open-source nature promotes accessibility and further development in quantitative MRI.