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

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.

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.

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.

DDCNN outperformed conventional reconstruction methods and image-domain-only CNN approaches both qualitatively and quantitatively. It achieved high image quality and rapid reconstruction (<1 min) for ngCBCT acquisitions, supporting clinical adoption in pretreatment setup for respiratory gating lung SBRT.

Pairing the innovative ngCBCT acquisition strategy with the DDCNN framework enables substantial reductions in scan time and imaging dose while maintaining high image quality. This advancement has the potential to improve patients' treatment experience and overall efficiency of respiratory gating lung SBRT, and also paves the way for broader adoption of respiratory gating techniques in other motion-affected tumor sites.