NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling

Neighboring-slice self-supervised denoising is attractive forvolumetric medical imaging, yet inter-slice misalignment breaks anatom-ical correspondence and often yields ghosting and blurred margins whenadjacent slices are used naïvely as targets. We propose Neighbor-GuidedPatch Sampling (NGPS), a lightweight framework that constructs neigh-boring supervision under local inter-slice misalignment. To avoid learn-ing from misleading targets, prior methods commonly mask discrepantregions, but this stabilizes training at the cost of leaving a non-trivialportion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decouplingstructure matching from signal retrieval: for each masked location, itsearches a local neighborhood for structurally similar candidate patchesusing a simple guide image (e.g., fast bilateral filtering), while retriev-ing the supervision signal directly from the raw noisy neighbor at thematched coordinates. By matching on a noise-attenuated guide whileretrieving raw values from neighboring slices, NGPS constructs localpseudo targets without dense deformation-field estimation or spatial re-sampling. Across the evaluated CT and synthetic-Rician MRI settings,NGPS improves fidelity and structure-sensitive metrics. Code is availableat https://github.com/cv-cho/NGPS.