Robust estimation of the apparent diffusion coefficient invariant to acquisition noise and physiological motion

Area of Science:

• Medical Imaging
• Biophysics
• Quantitative MRI

Background:

• Diffusion-weighted imaging (DWI) is crucial for assessing tissue microstructure.
• Accurate estimation of the apparent diffusion coefficient (ADC) is often hindered by noise and patient motion.
• Existing methods may introduce bias due to data non-Gaussianity and registration inaccuracies.

Purpose of the Study:

• To develop a robust methodology for estimating ADC from multiple breath-hold DWI data.
• To address confounding factors of noise and motion in ADC estimation.
• To correct for bias arising from data non-Gaussianity and registration in joint estimation methods.

Main Methods:

• Extended a joint groupwise multimodal registration and ADC estimation method.
• Incorporated computationally light procedures to correct for estimation bias.
• Evaluated methodology on multiple breath-hold diffusion-weighted images.

Main Results:

• Achieved statistically significant improvements in displacement field calculation robustness.
• Demonstrated enhanced accuracy in apparent diffusion coefficient estimation compared to sequential approaches.
• Confirmed reproducibility of ADC estimation using real data and varying b-values.

Conclusions:

• The proposed methodology effectively corrects ADC estimation bias.
• Provides robust apparent diffusion coefficient maps, particularly in the liver.
• Enables accurate and reproducible tissue analysis using quantitative MRI.