Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney

Abstract

Purpose: To improve the reliability of intravoxel incoherent motion model (IVIM) parameter estimation for the diffusion-weighted imaging in the kidney using a novel Image Downsampling Expedited Adaptive Least-squares (IDEAL) approach. Methods: The robustness of IDEAL was investigated using simulated diffusion-weighted MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (Fixed D* and Segmented fitting). Results: The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise as indicated by relatively low absolute percentage bias (sMdPB [%]) and normalized root-mean-square error (RMSE [%]). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the Fixed D* and Segmented methods. Further, the use of IDEAL for the triexponential IVIM modelling resulted in reduced cortical and medullary coefficients of variation (CVs) for all IVIM parameters when compared with Fixed D*, reflecting greater accuracy of this method. Conclusion: The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.