Image downsampling expedited adaptive least‐squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney

• Published in: Magnetic resonance in medicine Vol. 89; no. 3; pp. 1055 - 1067
• Main Authors: Stabinska, Julia, Zöllner, Helge J., Thiel, Thomas A., Wittsack, Hans‐Jörg, Ljimani, Alexandra
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2023
• Subjects: Algorithms; Coefficient of variation; Computer simulation; Data acquisition; Diffusion Magnetic Resonance Imaging - methods; DWI; Estimates; Human motion; Humans; IDEAL; Image Processing, Computer-Assisted - methods; In vivo methods and tests; IVIM; kidney; Kidney - diagnostic imaging; Kidneys; Least-Squares Analysis; Magnetic resonance imaging; Motion; Parameter estimation; Parameter robustness; Reliability analysis; Reproducibility of Results; Robustness (mathematics); Variation; IDEAL; kidney; IVIM; DWI
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.29517

Purpose To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least‐squares (IDEAL) approach. Methods The robustness of IDEAL was investigated using simulated DW‐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 (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). 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, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL‐based biexponential (coefficients of variation: 4%–50%) and triexponential (coefficients of variation: 7.5%–75%) IVIM modelling compared to the segmented (coefficients of variation: 4%–120%) and fixed D* (coefficients of variation: 17%–174%) methods, 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.