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

Intravoxel incoherent motion (IVIM) imaging and diffusion tensor imaging (DTI) facilitate noninvasive 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. 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 the 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. With model‐based reconstruction, the parameter maps exhibited 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 with the reference for all parameters. In 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. We propose a model‐based reconstruction framework for intravoxel incoherent motion (IVIM) and combined IVIM and diffusion tensor imaging (IVIM‐DTI) parameter estimation. The framework was validated with simulations and in vivo data. 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. We found that model‐based reconstruction is feasible for IVIM and IVIM‐DTI and improves the precision of the parameter estimates, particularly for f and D* maps.