Optimizing the functional diffusion map using Monte Carlo simulations.

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Functional diffusion maps (fDMs) are used to monitor tumor treatment response.
• Accurate measurement of the apparent diffusion coefficient (ADC) is crucial for fDM reliability.
• Segmentation threshold accuracy is vital for interpreting fDM results.

Purpose of the Study:

• To optimize the diagnostic accuracy of functional diffusion maps for monitoring tumor treatment response.
• To identify factors affecting the accuracy of ADC measurements and threshold determination.
• To improve the fidelity of future clinical studies using fDMs.

Main Methods:

• Monte Carlo simulations were used to evaluate ADC measurement precision and threshold accuracy.
• Repeatability limits of ADC were investigated as a function of diffusion weighting.
• Phantom and in-vivo experiments were performed to validate simulation findings.

Main Results:

• Diagnostic accuracy of fDMs is minimally affected by T(2) relaxation time differences.
• Differing ADCs between tumor and reference tissue impair diagnostic accuracy by affecting segmentation thresholds.
• Decreasing the maximum b-factor and increasing signal averages, or optimizing b-factor schemes, can address threshold inaccuracies.

Conclusions:

• Inaccurate segmentation thresholds can impair the diagnostic accuracy of functional diffusion maps.
• Optimization strategies, including sequence parameter adjustments, are derived to enhance fDM fidelity.
• This work provides a foundation for more reliable clinical studies monitoring tumor treatment response.