Model-based reconstructions for intravoxel incoherent motion and diffusion-tensor imaging parameter map estimations by Susanne S Rauh

NMR Biomed. 2023 Mar 18:e4927. doi: 10.1002/nbm.4927. Online ahead of print.

ABSTRACT

PURPOSE: Intravoxel incoherent motion (IVIM) imaging and diffusion-tensor imaging (DTI) facilitate non-invasive quantification of tissue perfusion and diffusion. Both are promising biomarkers in various diseases and a combined acquisition is therefore desirable. This comes with challenges, including noisy parameter maps and long scan times, especially for the perfusion fraction f and pseudo-diffusion coefficient D*. A model-based reconstruction has the potential to overcome these challenges. As a first step, our goal was to develop a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation.

METHODS: The IVIM and IVIM-DTI models were implemented in the PyQMRI model-based reconstruction framework and validated with simulations and in vivo data. Commonly used voxel-wise nonlinear least-squares fitting was used as reference. Simulations with the IVIM and IVIM-DTI models were performed with 100 noise realizations to assess accuracy and precision. Diffusion-weighted data were acquired for IVIM-reconstruction in the liver (n=5) as well as for IVIM-DTI in the kidneys (n=5) and lower-leg muscles (n=6) of healthy volunteers. The median and interquartile range (IQR) values of the IVIM and IVIM-DTI parameters were compared to assess bias and precision.

RESULTS: With model-based reconstruction, the parameter maps exhibit less noise, which was most pronounced in the f and D* maps both in the simulations and in vivo. The bias values in the simulations were comparable between model-based reconstruction and the reference method. The IQR was lower with model-based reconstruction compared to the reference for all parameters.

CONCLUSION: Model-based reconstruction is feasible for IVIM and IVIM-DTI and improves the precision of the parameter estimates, particularly for f and D* maps.

PMID:36932842 | DOI:10.1002/nbm.4927