Motion Compensated Abdominal Diffusion Weighted MRI by Simultaneous Image Registration and Model Estimation (SIR-ME)

Area of Science:

• Medical Imaging
• Biophysics
• Quantitative MRI

Background:

• Diffusion-weighted MRI (DW-MRI) offers non-invasive characterization of water molecule mobility in the abdomen, crucial for gastrointestinal and oncological applications.
• Accurate estimation of DW-MRI signal decay parameters is hindered by physiological motion (respiratory, cardiac, peristalsis) and suboptimal registration across b-values.
• Existing methods may suffer from low signal-to-noise ratio (SNR) and contrast differences, impacting parameter estimation reliability.

Purpose of the Study:

• To develop and evaluate a motion-compensated parameter estimation framework for DW-MRI.
• To improve the accuracy and reproducibility of water molecule mobility quantification in abdominal imaging.
• To enhance the diagnostic potential of DW-MRI in gastrointestinal and oncological diseases.

Main Methods:

• Introduced a simultaneous image registration and model estimation (SIR-ME) framework.
• Utilized the interdependence of acquired volumes along the diffusion weighting dimension for motion compensation.
• Evaluated the framework on 16 in-vivo DW-MRI datasets from Crohn's disease patients.

Main Results:

• The SIR-ME model significantly reduced the average root-mean-square error (RMSE) between observed and fitted signals by over 50%.
• SIR-ME model estimates demonstrated superior discrimination between normal and abnormal bowel loops compared to standard methods.
• Improved accuracy in estimating diffusion signal decay model parameters was achieved.

Conclusions:

• The proposed SIR-ME framework provides more accurate and robust parameter estimation in motion-affected abdominal DW-MRI.
• This advancement holds significant potential for improved non-invasive biomarkers in gastrointestinal and oncological diagnostics.
• Motion-compensated simultaneous estimation offers a superior approach for quantitative DW-MRI analysis.