Deep learning‐based dual‐domain reconstruction for nonstop gated CBCT in respiratory gating lung SBRT

BACKGROUND: The nonstop gated CBCT (ngCBCT) technique has been proposed as a next-generation replacement to the current inefficient clinical gated CBCT (gCBCT), reducing half-fan scan time from 2-8 min to 1 min on C-arm linear accelerators while substantially lowering imaging dose. However, ngCBCT yields highly non-uniform and under-sampled projections, posing a major challenge for conventional reconstruction methods to achieve high-quality images. PURPOSE: To develop a powerful and efficient Dual-Domain Convolutional Neural Network (DDCNN) tailored to the unique characteristics of ngCBCT projections to achieve high-quality ngCBCT imaging. METHODS: Clinical raw gCBCT projections from 31 free-breathing respiratory-gated lung SBRT patients (77 half-fan and 65 full-fan scans) were retrospectively retrieved and down-sampled based on respiratory signals to emulate ngCBCT acquisitions. The proposed DDCNN integrates a projection-domain network, which completes missing projection data, with an image-domain network that further reduces artifacts. The two domains are linked by the fast FDK algorithm, enabling accurate and efficient reconstruction suitable for real-time clinical use. RESULTS: DDCNN outperformed conventional reconstruction methods and image-domain-only CNN approaches both qualitatively and quantitatively. It achieved high image quality and rapid reconstruction (