Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation
- PMID: 39460134
- PMCID: PMC11510812
- DOI: 10.3390/s24206654
Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation
Abstract
Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. Measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods-namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method-but also does not require complex parameter tuning.
Keywords: deep learning; spectral computed tomography; unsupervised denoising method.
Conflict of interest statement
The authors declare no conflicts of interest.
Figures











References
-
- Kehl C., Mustafa W., Kehres J., Dahl A.B., Olsen U.L. Distinguishing malicious fluids in luggage via multi-spectral CT reconstructions; Proceedings of the 3D-NordOst 2018, Anwendungsbezogener Workshop zur Erfassung, Modellierung, Verarbeitung und Auswertung von 3D-Daten; Berlin, Germany. 6–7 December 2018.
-
- Richtsmeier D., Guliyev E., Iniewski K., Bazalova-Carter M. Contaminant detection in non-destructive testing using a CZT photon-counting detector. J. Instrum. 2021;16:P01011. doi: 10.1088/1748-0221/16/01/P01011. - DOI
LinkOut - more resources
Full Text Sources