Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient...

Area of Science:

• Medical Imaging
• Biophysics
• Computational Biology

Background:

• Cardiac motion is a major challenge in diffusion-weighted MRI, causing signal loss and necessitating repeated measurements.
• Investigating myocardial diffusion is difficult due to diffusion measurements' sensitivity to cardiac motion.

Purpose of the Study:

• To develop a mathematical model quantifying the effect of tissue motion on diffusion imaging.
• To identify time points in the cardiac cycle where diffusion is unaffected by myocardial strain and motion.
• To explore methods for reducing motion sensitivity in diffusion MRI sequences.

Main Methods:

• Developed a mathematical model based on the Bloch-Torrey equations, incorporating continuum mechanics for deformations.
• Utilized the finite element method for numerical simulations to predict diffusion signal sensitivity to cardiac motion.
• Compared numerically computed diffusion-weighted MR signals with literature data for various diffusion encoding schemes.

Main Results:

• Identified two specific time points within the cardiac cycle where diffusion measurements are independent of myocardial strain and cardiac motion.
• Demonstrated that these motion-free time points are dependent on the chosen diffusion encoding scheme.
• Showed that using spin echo techniques with motion compensation or stimulated echo acquisition mode can reduce motion sensitivity.

Conclusions:

• The developed numerical model accurately quantifies cardiac motion effects in diffusion MRI.
• The identification of motion-independent time points offers potential for improved cardiac diffusion imaging.
• Optimized diffusion encoding schemes and acquisition techniques can significantly mitigate motion artifacts, enhancing diagnostic accuracy.