Spatially Aware Acquisition‐Independent Deep Learning for IVIM MRI Parameter Estimation in Patients With Esophageal Cancer

Purpose Neural controlled differential equations ( NCDEs ) recently emerged as a robust deep learning approach for quantitative MRI parameter estimation. NCDEs offer flexibility to changes in acquisition protocols by modeling the signal evolution dynamics. However, NCDE implementations operate on a voxel‐by‐voxel basis and cannot exploit spatial information, limiting effectiveness. The purpose of this study is to develop and validate an acquisition‐independent and spatially aware neural network for intra‐voxel incoherent motion ( IVIM ) MRI parameter estimation. Methods Spatially aware NCDEs (Spatial NCDEs ) were evaluated in simulations across a range of acquisition protocols and signal‐to‐noise ratios. Performance was compared with least squares ( LSQ ) fitting, segmented fitting, voxel‐wise NCDEs , and spatially aware neural networks ( UNet ). In patients with esophageal cancer, discriminative ability for predicting response to neoadjuvant chemoradiotherapy was examined. Results Spatial NCDEs achieved lower mean squared error ( MSE ) for estimating IVIM parameters than LSQ , segmented fitting, 1D NCDE , and UNet . At SNR 20, MSE was 81%, 85%, and 83% lower than LSQ , 85%, 89%, and 88% lower than segmented fitting 62%, 71%, and 52% lower than 1D NCDE and 55%, 67%, and 44% lower than UNet for , , and , respectively. In patients with esophageal cancer, Spatial NCDE ‐based parameter estimates showed improved, though not statistically significant, discriminative ability for predicting response to neoadjuvant chemoradiotherapy compared to LSQ. Conclusion Spatial NCDEs provide a robust, accessible solution for high‐quality IVIM MRI parameter estimation, enabling broader adoption of deep learning‐based parameter estimation.