Improving the Quality of Sparse-view Cone-Beam Computed Tomography via Reconstruction-Friendly Interpolation Network
Reconstructing cone-beam computed tomography (CBCT) typically utilizes a Feldkamp-Davis-Kress (FDK) algorithm to 'translate' hundreds of 2D X-ray projections on different angles into a 3D CT image. For minimizing the X-ray induced ionizing radiation, sparse-view CBCT takes fewer projections by a wider-angle interval, but suffers from an inferior CT reconstruction quality. To solve this, the recent solutions mainly resort to synthesizing missing projections, and force the synthesized projections to be as realistic as those actual ones, which is extremely difficult due to X-ray's tissue superimposing. In this paper, we argue that the synthetic projections should restore FDK-required information as much as possible, while the visual fidelity is the secondary importance. Inspired by a simple fact that FDK only relies on frequency information after ramp-filtering for reconstruction, we develop a Reconstruction-Friendly Interpolation Network (RFI-Net), which first utilizes a 3D-2D attention network to learn inter-projection relations for synthesizing missing projections, and then introduces a novel Ramp-Filter loss to constrain a frequency consistency between the synthesized and real projections after ramp-filtering. By doing so, RFI-Net's energy can be forcibly devoted to restoring more CT-reconstruction useful information in projection synthesis. We build a complete reconstruction framework consisting of our developed RFI-Net, FDK and a commonly-used CT post-refinement. Experimental results on reconstruction from only one-eighth projections demonstrate that using RFI-Net restored full-view projections can significantly improve the reconstruction quality by increasing PSNR by 2.59 dB and 2.03 dB on the walnut and patient CBCT datasets, respectively, comparing with using those restored by other state-of-the-arts.