Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution

Arbitrary slice super-resolution reconstructs isotropic vol-umes from anisotropic clinical acquisitions by synthesizing intermedi-ate slices at arbitrary scales. However, treating this ill-posed inverseproblem as unconstrained residual-based regression risks hallucinatinganatomically implausible structures or altering the originally observeddata. To address both concerns, this paper presents the Dual-PriorNull-Space Learning (DP-NSL) framework, which reformulates the taskas a constrained recovery process guided by two complementary priors.A Measurement-Consistent Projection (MCP) enforces a DeterministicObservation Prior : the reconstruction undergoes an exact orthogonalprojection that reproduces every acquired slice with zero error, confin-ing all learned details to the unobservable null space. Within this nullspace, a Mixture-of-Splines (MoS) module imposes a Geometric Conti-nuity Prior by dynamically mixing B-spline experts of different analyticorders, allowing each anatomical region to be modeled with a content-aware level of continuity. To promote spatial coherence, a Local SpatialConsistency Decoder (LSCD) further injects local inductive bias. Ex-periments on three CT and one MRI benchmark show that DP-NSLoutperforms existing approaches while strictly preserving measurementconsistency. Code is available at https://github.com/DeepMed-Lab-ECNU/Medical-Image-Reconstruction.