Excited to share our @eccvconf paper: Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT. We introduce Computed Tomography neural Operator (CTO), the first neural operator framework for sparse-view CT reconstruction. Sparse-view CT cuts radiation dose and scan time by taking fewer X-ray projections, but reconstruction then becomes ill-posed and needs a learned prior. Existing deep learning models are tied to one subsampling rate. However, this is not scalable since clinical protocols vary across organs and diagnostic purposes, so in practice you need a separate model for each subsampling rate. CTO instead learns a mapping between function spaces. Because a function has no fixed resolution, one model ingests sinograms at any subsampling rate and outputs high quality reconstructions, with no retraining required. On an average, CTO beats traditional unrolled CNN variational network by 3.42 dB PSNR and is 500x faster than diffusion models while being 6.02 dB PSNR better. @Caltech Paper: Project page: Code:

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