Fast model-based restoration of noisy and undersampled spectral CT data

David Rigie; Patrick J. La Riviere

doi:10.1117/12.2043140

19 March 2014 Fast model-based restoration of noisy and undersampled spectral CT data

David Rigie, Patrick J. La Riviere

Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 90333F (2014) https://doi.org/10.1117/12.2043140
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In this work we propose a fast, model-based restoration scheme for noisy or undersampled spec- tral CT data and demonstrate its potential utility with two simulation studies. First, we show how one can denoise photon counting CT images, post- reconstruction, by using a spectrally averaged im- age formed from all detected photons as a high SNR prior. Next, we consider a slow slew-rate kV switch- ing scheme, where sparse sinograms are obtained at peak voltages of 80 and 140 kVp. We show how the missing views can be restored by using a spectrally av- eraged, composite sinogram containing all of the views as a fully sampled prior. We have chosen these ex- amples to demonstrate the versatility of the proposed approach and because they have been discussed in the literature before³,⁶ but we hope to convey that it may be applicable to a fairly general class of spectral CT systems. Comparisons to several sparsity-exploiting, iterative reconstructions are provided for reference.

Citation Download Citation

David Rigie and Patrick J. La Riviere "Fast model-based restoration of noisy and undersampled spectral CT data", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90333F (19 March 2014); https://doi.org/10.1117/12.2043140

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KEYWORDS

Data modeling

Sensors

Computed tomography

Composites

Image processing

Model-based design

Photodetectors

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