Neighborhood-attention models for incorporating spatial information in deep learning parameter estimation applied to IVIM

Motivation: Conventional model-fitting approaches neglect spatial information. Recent work showed promise in using convolutional neural networks (CNNs) trained on spatially-correlated synthetic data. However, the convergence rate remained suboptimal, and the spatial extent was limited. Goal(s): To improve estimator performance by utilizing transformer networks and training on larger receptive-fields. Approach: Transformers with self-attention and neighborhood-attention with increased receptive-field were trained on spatially-correlated synthetic data (IVIM), and evaluated quantitatively using novel fractal-noise maps and in-vivo scans. Results: Transformers excelled in integrating spatial information over CNNs. The application of larger receptive-fields with neighborhood-attention effectively leveraged correlated signal information from nearby voxels, leading to improved estimator performance. Impact: The improved parameter estimation from neighborhood-attention models trained on synthetic data brings challenging ill-posed signal analysis problems, like IVIM, closer to clinical implementation. Additionally, the novel fractal-noise maps provide spatially-correlated ground truths, permitting new approaches to quantitative medical image analysis.