An I²SB diffusion model for CT FOV extension delivers RMSE of 49.8 HU on simulated data and 152.0 HU on real data with 0.19 s per-slice inference, over 700 times faster than cDDPM.
Prior- FOVNet: A multimodal deep learning framework for megavoltage computed tomography truncation artifact correction and field-of-view extension,
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Efficient Image-to-Image Schr\"odinger Bridge for CT Field of View Extension
An I²SB diffusion model for CT FOV extension delivers RMSE of 49.8 HU on simulated data and 152.0 HU on real data with 0.19 s per-slice inference, over 700 times faster than cDDPM.