Researchers say CTO reconstructs CT images across sampling rates without retraining
The team reports that CTO is, on average, 500 times faster than diffusion models while scoring 6.02 dB higher on PSNR, an image-quality metric.
TLDR
A research team introduces Computed Tomography neural Operator (CTO) for sparse-view CT, which uses fewer X-ray projections. Taking fewer projections cuts radiation dose and scan time, the team says, but makes image reconstruction harder.
The researchers say existing deep learning approaches need a separate model for each sampling rate, while CTO handles any sampling rate without retraining. They report average PSNR gains of 3.42 dB over a traditional unrolled CNN variational network and 6.02 dB over diffusion models, alongside 500-fold faster performance than diffusion models. PSNR is an image-quality metric.
The announcement links to the paper, project page and code.
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