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 (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.

Investigation of diffusion time dependence of apparent diffusion coefficient and intravoxel incoherent motion parameters in the human kidney

“…To improve the stability of the extended bi-exponential fitting, the Image Downsampling Expedited Adaptive Least-squares (IDEAL) 20 approach was employed. First, the extended IVIM fitting of the DW signal obtained from the images downsampled to 1 x 1 with the loosely constrained lower and upper bounds was performed to determine initial values for the subsequent fitting step.…”

Toward Optimal Fitting Parameters for Multi-Exponential DWI Image Analysis of the Human Kidney: A Simulation Study Comparing Different Fitting Algorithms

“…In the context of the simulation, the diffusion coefficient parameter is denoted as d in order to eliminate any potential confusion with the limits of the NNLS fitting range Dmin or Dmax. Values of previous works utilizing NLLS fitting were used as starting parameters [9,11,13,20,21]. The NNLS diffusion fitting range was set accordingly to encompass the entire physiologically relevant spectrum [11,13].…”

“…All b-value distributions span the same range, with bmax = 750. Values of previous works utilizing NLLS fitting were used as starting parameters [9,11,13,20,21]. The NNLS diffusion fitting range was set accordingly to encompass the entire physiologically relevant spectrum [11,13].…”

“…To ensure more robust results, the utilization of the median was preferred to traditional mean values. This approach bypasses the strong influence of outliers, themselves heavily biased by the choice of constraint boundaries, the latter only being applicable to NNLS algorithms [21,24].…”

Toward optimal fitting parameters for multi-exponential DWI image analysis of the human kidney: A simulation study comparing different fitting algorithms

“…Values of previous works utilizing NLLS tting were used as starting parameter [9,20,13,11,21]. The NNLS diffusion tting range was set accordingly, to encompass the entire physiologically relevant spectrum [13,11].…”