Numerical-aperture transfer in holotomography with a deterministic diffusion prior

Abstract

Abstract

High-throughput holotomography often relies on long-working-distance, multiwell-compatible optics that reduce illumination numerical aperture (NA) and limit access to high spatial frequencies. Here we present ResShift-ODE, a deterministic diffusion-prior framework that transfers low-NA refractive-index (RI) tomograms to high-NA-equivalent volumes without modifying the acquisition hardware. We formulate low-NA-to-high-NA transfer as a diffusion-prior inverse problem under an explicit NA-limited Fourier-domain forward operator, distinct from suppressing artifacts within an already measured passband. The method extends residual-shifting diffusion to volumetric RI data and reformulates the reverse process as a probability-flow ordinary differential equation, enabling reproducible inference in five denoiser evaluations. On held-out emulated-pair test volumes, inferred volumes matched high-NA references with RI errors of 0.002-0.003 for >99% of voxels, while Fourier analysis confirmed measurement-anchored lateral-band recovery without filling the axial missing cone. 3D ResShift-ODE required five denoiser evaluations per volume, incurring ~5.8x the cost of a 3D U-Net while remaining ~166x faster than a 1000-step 3D denoising diffusion probabilistic model.