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Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography. (arXiv:2302.06436v2 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
The diagnostic quality of computed tomography (CT) scans is usually
restricted by the induced patient dose, scan speed, and image quality.
Sparse-angle tomographic scans reduce radiation exposure and accelerate data
acquisition, but suffer from image artifacts and noise. Existing image
processing algorithms can restore CT reconstruction quality but often require
large training data sets or can not be used for truncated objects. This work
presents a self-supervised projection inpainting method that allows optimizing
missing projective views via gradient-based optimization. By …
acquisition algorithms arxiv constraints data diagnostic image image processing inpainting noise patient processing quality reduce restore speed