Unsupervised denoising for sparse multi-spectral computed tomography
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Abstract (arXiv)
Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, PCDs with many spectral channels drastically increase the computational complexity of the CT reconstruction, and bespoke reconstruction algorithms need fine-tuning to varying noise statistics. revEspecially if many projections are taken, a large amount of data has to be collected and stored. Sparse view CT is one solution for data reduction. However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts. In this work, we investigate the suitability of learning-based improvements to the challenging task of obtaining high-quality reconstructions from sparse measurements for a 64-channel PCD-CT. In particular, to overcome missing reference data for the training procedure, we propose an unsupervised denoising and artefact removal approach by exploiting different filter functions in the reconstruction and an explicit coupling of spectral channels with the nuclear norm. Performance is assessed on both simulated synthetic data and the openly available experimental Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset. We compared the quality of our unsupervised method to iterative total nuclear variation regularized reconstructions and a supervised denoiser trained with reference data. We show that improved reconstruction quality can be achieved with flexibility on noise statistics and effective suppression of streaking artefacts when using unsupervised denoising with spectral coupling.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 50 references that are in the map. First posted on arXiv 2 Nov 2022.
Builds on
16 of 50 references.
References · 16
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4 more
- 2019Multi-Scale Learned Iterative Reconstructioncited by 18
- 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
- 2020Sparse-View Spectral CT Reconstruction Using Deep Learningcited by 3
- 2018Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiationcited by 2
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Citing papers per year: 2026: 1.
1 paper.