FBFormer: interventional ultra-sparse CT reconstruction with image prior using feature back-projection and transformer

<i>Objective</i>. CT-guided interventional procedures hold a significant position in clinical practice. However, due to the high number of scans and prolonged procedure times, patients are exposed to considerable radiation doses. This study aims to utilize intraoperative x-ray imaging as an alternative to intraoperative CT guidance, integrating preoperative prior scan images to reconstruct interventional CT.<i>Approach</i>. We present Feature back-projection Transformer (FBFormer), a reconstruction framework using Transformer and a novel feature back-projection module to reconstruct tomographic data from ultra-sparse(less than 10 views) intraoperative x-ray images. A mixed UNet and PVT encoder is employed to extract the local and global feature of the x-ray images and preoperative image prior, supporting more various input, while a feature back-projection module is utilized to achieve 2D-3D conversion in a more natural and geometrically consistent manner.<i>Main Results</i>. Extensive experiments on our simulated 4DCT and clinical validation datasets demonstrate the effectiveness of the proposed method and our method achieves superior results in reconstruction performance metrics and outperforms other compelling methods.<i>Significance</i>. The proposed method has the potential to significantly reduce radiation exposure during interventional procedures while maintaining high-quality reconstruction. The proposed FBFormer framework demonstrates the potential to enhance the clinical reliability of interventional CT reconstruction from ultra-sparse x-ray projections by the integration of image priors and the geometric preserving feature back-projection module.