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Model-based reconstructions for intravoxel incoherent motion and diffusion tensor imaging parameter map estimations.

Susanne S Rauh¹, Oliver Maier², Oliver J Gurney-Champion³

¹Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands.

NMR in Biomedicine

|March 18, 2023

Summary

This summary is machine-generated.

Model-based reconstruction enhances Intravoxel Incoherent Motion (IVIM) and IVIM-Diffusion Tensor Imaging (DTI) parameter estimation. This method reduces noise and improves precision, especially for perfusion fraction (f) and pseudo-diffusion (D*) maps, making combined imaging more reliable.

Keywords:

DTI IVIM diffusion model-based reconstruction quantitative MRI

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Area of Science:

Medical Imaging
Biophysics
Quantitative MRI

Background:

Intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) are noninvasive techniques for quantifying tissue perfusion and diffusion.
Combined IVIM-DTI acquisition is desirable for comprehensive tissue characterization but faces challenges like noisy parameter maps and long scan times, particularly for perfusion fraction (f) and pseudo-diffusion coefficient (D*).
Model-based reconstruction offers a potential solution to overcome these acquisition and processing challenges.

Purpose of the Study:

To develop and validate a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation.
To assess the accuracy and precision of the model-based reconstruction compared to conventional methods.

Main Methods:

The IVIM and IVIM-DTI models were implemented within the PyQMRI model-based reconstruction framework.
Validation involved simulations with 100 noise realizations and in vivo data acquisition in human subjects (liver, kidneys, lower-leg muscles).
Voxel-wise nonlinear least-squares fitting served as the reference method for comparison.

Main Results:

Model-based reconstruction resulted in less noisy parameter maps, most notably for f and D* maps, in both simulations and in vivo data.
Simulation bias values were comparable between model-based reconstruction and the reference method.
The precision, indicated by a lower interquartile range (IQR), was improved with model-based reconstruction for all parameters compared to the reference method.

Conclusions:

Model-based reconstruction is a feasible approach for IVIM and combined IVIM-DTI parameter estimation.
This method significantly improves the precision of parameter estimates, particularly for the challenging f and D* maps.
The developed framework holds promise for more robust and efficient quantitative perfusion and diffusion MRI.