Derivation and properties of the convolution model for MRI gradient-induced cardiac stimulation

Area of Science:

• Medical Imaging Physics
• Biophysics
• Computational Neuroscience

Background:

• Patient safety in MRI is paramount, necessitating accurate prediction of gradient-induced peripheral nerve stimulation (PNS) and cardiac stimulation (CS).
• Existing models for PNS prediction require extension to effectively address CS risks.
• Modern MRI systems utilize complex gradient waveforms that demand robust safety assessment tools.

Purpose of the Study:

• To extend the dynamic convolution-based model for predicting peripheral nerve stimulation (PNS) to cardiac stimulation (CS).
• To theoretically analyze the general properties of the convolution model for gradient-induced stimulation.
• To compute and compare PNS and CS response functions for clinical MRI sequences.

Main Methods:

• Derived a cardiac stimulation (CS) convolution kernel from the exponential model of the strength-duration curve.
• Theoretically analyzed the self-consistency and properties of the convolution output (response function) for periodic trapezoidal waveforms.
• Computed peripheral nerve stimulation (PNS) and CS response functions for clinical 3T brain and pelvic imaging sequences.

Main Results:

• The CS convolution kernel is a simple, decaying exponential function.
• The convolution model aligns with the strength-duration curve for rectangular dG/dt pulses.
• Cardiac stimulation (CS) response correlates more with gradient amplitude than slew rate due to a long time constant, suppressing short pulse stimulation.
• Maximum PNS and CS occurred at the first slope's end on trapezoidal waveforms, independent of cycle count, indicating a limitation of the linear convolution model.

Conclusions:

• The developed CS convolution model enhances patient safety assessment for arbitrary MRI gradient waveforms.
• Understanding the convolution model's properties aids in designing safer gradient waveforms.
• The model is applicable to whole-body and anatomy-specific MRI systems with fast gradient fields.