Spatially Aware Acquisition-Independent Deep Learning for IVIM MRI Parameter Estimation in Patients With Esophageal Cancer.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands.
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands.
- Queensland Digital Health Centre, University of Queensland, Brisbane, Australia.
- Centre for Big Data Research in Health, UNSW Sydney, Sydney, Australia.
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands.
Abstract
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. 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. 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 <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi></mrow> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msup><mi>D</mi> <mo>*</mo></msup> </mrow> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>f</mi></mrow> </math> , 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. Spatial NCDEs provide a robust, accessible solution for high-quality IVIM MRI parameter estimation, enabling broader adoption of deep learning-based parameter estimation.