Balance between the number of projections and exposure time in photon counting CT with a data-driven approach

Abstract

Photon Counting Computed Tomography (CT) Imaging often requires high x-ray exposure due to low photon counts per channel, leading to prolonged scanning times, which may not be practical. Here, we explored a trade-off between the number of projections and exposure time per projection to optimize scanning efficiency and image quality. By increasing projections and reducing exposure, we initially generated noisier datasets, which we denoised using unsupervised, data-driven techniques. Extending our previous research, we applied unsupervised denoising to synthetic spectral CT datasets with a distinct K-edge in the x-ray absorption spectrum. We compared our results with an iterative reconstruction algorithm that uses a total variation constraint in the spatial and a total generalised variation constraint in the spectral dimension, which uses fewer projections and higher doses for each projection. Although this algorithm employs fewer projections and higher doses, it matches our method in scanning time, allowing a direct comparison to our methods. Our approach significantly reduced scanning time by 36-fold compared to traditional full-dose methods, without compromising image quality. It also eliminates the need for meticulous parameter tuning, simplifying the operational process and enhancing usability.

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Identifiers

ORCID for Raziye Kubra Kumrular: orcid.org/0000-0002-0976-3683

ORCID for Thomas Blumensath: orcid.org/0000-0002-7489-265X

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