Parameter Estimation of Intravoxel Incoherent Motion Based on Prior-driven

Intravoxel incoherent motion(IVIM) model leverages diffusion-weighted magnetic resonance imaging(DWI) to non-invasively ascertain the diffusion coefficient of water molecules in living tissue<i>(D</i>) and to gather blood perfusion data(<i>F,D^*</i>).However,conventional methods for estimating IVIM parameters are particularly susceptible to noise,which poses a significant challenge in abdominal organs like the liver where respiratory motion is prevalent.This sensitivity often compromises the efficacy of parameter estimation.To enhance the robustness against noise,this study introduces a novel algorithm,the prior-driven neural network(PDNN).This approach harnesses prior knowledge derived from fully supervised training to inform and guide unsupervised learning phases.The robustness of PDNN model to noise is systematically assessed using root mean square errors(RMSE) across various signal-to-noise ratios.Additionally,the coefficient of variation(CV) distribution is employed to effectively differentiate between healthy and cirrhotic liver tissues,indicating significant variations(<i>P</i>&lt;0.05) that underscore the model's diagnostic capability.The performance of the PDNN algorithm is compared with other advanced methods,including the nonlinear least squares approach,the voxel-based deep learning method IVIM-NET_optim,and SSUN,a 2D convolutional network grounded in domain-specific information.The results demonstrate that PDNN outperforms these methods in terms of noise robustness.Speci-fically,the RMSE values for the fitting parameters <i>[D,F,D^*</i>] in the proposed model are 27.63%,23.72%,and 31.46% lower,respectively,than those recorded by the sub-optimal method.Moreover,PDNN not only preserves the integrity of tissue structure information but also effectively distinguishes between healthy and cirrhotic livers,highlighting its potential as a superior tool for clinical diagnosis and evaluation.