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 D , D * , and f , 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.