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Stim-CODE: PNS and CNS Constraint-Optimized Diffusion Encoding

Stim-CODE extends the GrOpt toolbox to enable peripheral nerve stimulation (PNS) and cardiac nerve stimulation (CNS) constraint–optimized diffusion-encoding waveform design.

It provides tools for generating diffusion-encoding gradients in MRI that satisfy hardware, sequence, and physiological constraints. The SAFE model (Hebrank, ISMRM, 2000) is incorporated to provide vendor-specific PNS/CNS response.


Requirements

Stim-CODE builds on the GrOpt framework:

Make sure GrOpt is installed and accessible in your Python environment before using this package.


Getting Started

A step-by-step demonstration is available:

  • Jupyter notebook: Examples/demo.ipynb
  • Google Colab (interactive): Open in Colab

The demo walks through:

  • Generating diffusion-encoding waveforms
  • Applying PNS/CNS constraints as (1) Constant Threshold, (2) Arbitrary envelope, and (3) Envelope based on other gradient events
  • Comparing waveforms to conventional diffusion-encoding

References

Associated work (in preparation):

Hannum AJ, Loecher M, Chen Q, Arbes E, Setsompop K, Zaitsev M, Ennis DB.
Stim-CODE: PNS and CNS Constraint-Optimized Diffusion-Encoding for Neuroimaging on 200 mT/m Whole-Body Gradients.
__ (in preparation).

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PNS & CNS Constrained Optimized Diffusion Encoding

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