DF-DiffVSR: Deformable Field-Driven Diffusion Model for Inter-Slice Continuity Enhancement in Medical Volume Super-Resolution.

Medical volumetric imaging is crucial for precise diagnosis, but limited by equipment and acquisition constraints, anisotropic resolution leads to challenges in detecting small lesions and 3D visualization. While volumetric super-resolution methods can mitigate this issue, existing techniques suffer from limited receptive fields, failing to fully exploit inter-slice correlations and resulting in compromised inter-slice continuity. To address this limitation, we propose DF-DiffVSR, a novel deformable field-enhanced diffusion model for medical volume super resolution. The proposed method integrates optical flow principles with diffusion models through a Deformable Field Extraction (DFE) module, which explicitly learns inter slice motion information to enhance structural continuity in the through-plane direction. Furthermore, we design a Multiscale Large Kernel Convolution (MLKC) module that employs striped convolutions with varying kernel sizes to expand the receptive field and capture global anatomical context. Evaluated on RPLHR-CT and IXI-T2 datasets, DF DiffVSR achieves state-of-the-art (SOTA) performance, surpassing the sub-optimal method by 0.732 dB and 0.214 dB in PSNR, respectively, demonstrating superior capabilities in preserving inter-slice continuity and recovering fine grained details.