Comparison of algorithms for IVIM modelling of DW-MRI with a clinical set of b-values for head and neck cancer: In silico and in vivo analysis.
BACKGROUND Intravoxel incoherent motion (IVIM) modelling of diffusion-weighted magnetic resonance imaging (DW-MRI) quantifies tissue diffusion (Dt), capillary microcirculation (Dp) and perfusion fraction (Fp). However, accurate IVIM modelling using the few b-values acquired in clinical settings is challenging. This study mainly investigated the performance of four algorithms for accurate IVIM parameter modelling with few b-values before exploring the IVIM parameters' biomarker potential for head and neck cancer (HNC) radiotherapy response. METHODS Synthetic IVIM signals with 4, 5 and 11 b-values were generated. DW-MRI was acquired with 11 b-values for 10 HNC patients. Self-supervised (DNNSSL) and supervised (DNNSL) deep neural networks, and least square (LSQ) and segmented (SEG) fitting algorithms estimated IVIM parameters for both synthetic and patient imaging data using 4, 5 and 11 b-values. The algorithm accuracy was evaluated in silico and in vivo by calculating the error between the estimated and ground truth parameter values. A Kaplan-Meier analysis evaluated the associations between progression free survival (PFS) of 20 HNC patients and longitudinal changes in IVIM parameters estimated with 4 b-values. RESULTS LSQ and DNNSL were the least and most accurate IVIM modelling algorithms, respectively. DNNSL, however, was biased towards the mean which limits the parameters' biomarker potential. None of the algorithms, however, estimated Dp accurately with few b-values. Preliminary survival analysis suggested that an increase in Fp during radiotherapy reduced the PFS of HNC patients. CONCLUSIONS SEG and DNNSSL were the preferred algorithms for IVIM modelling few b-values, although reliable estimation was limited to the Dt and Fp parameters.