Sparse2View: Rotatory Novel View Synthesis From Scarce X-Ray Inputs

The X-ray computed tomography (CT) requires a large number of X-ray projections for construction, exposing patients to radiation and a prolonged imaging time, To address these concerns, this work proposes Sparse2View, a novel rotational X-ray view synthesis framework, able to generate full-view projections from only a few sparse view inputs. Sparse2View features two key innovations: (1) intra-view feature refinement, which employs a tailored anisotropic spectral operator to capture the frequency information within the projection sequences; and (2) inter-view geometric fusion, which enhances geometric representation with cross-view information integration. Additionally, we curate a large-scale, deep learning–ready dataset comprising 1,614 object-level (mouse skeleton) scans, yielding a total of 419,640 medical expert–annotated 2D X-ray images. Extensive experiments demonstrate that Sparse2View can synthesize rotatory novel views on-demand under extremely sparse settings. For instance, taking only 4 views as inputs, it can generate the full 260-view of X-ray views over a 208-degree rotating angle (dramatically reducing the radiation dose by 65 folds), with an average PSNR of 36.72 and an SSIM of 0.931. Moreover, results under the zero-shot settings validate that the Sparse2View can be well generalized to other anatomical regions, unseen fractured bones, and human organs, underscoring its potential medical applications.