Unsupervised denoising for sparse multi-spectral computed tomography

TL;DR

This paper introduces an unsupervised learning-based denoising method for sparse multi-spectral CT that enhances image quality and suppresses artifacts without requiring reference data, suitable for complex spectral imaging scenarios.

Contribution

The work presents a novel unsupervised denoising approach leveraging spectral coupling and nuclear norm regularization for improved sparse multi-spectral CT reconstruction.

Findings

Outperforms traditional iterative methods in image quality.

Effectively suppresses streaking artifacts in sparse CT data.

Works well across synthetic and real datasets.

Abstract

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. \rev{Especially 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.