SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan
Abstract
Purpose Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3–4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms. Methods A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4DLung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-meansquared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) — patient body, lungs, planning target volume (PTV),…
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