Noise reduction in photon-counting CT using frequency-dependent optimal weighting

Mats Persson; Norbert J. Pelc

doi:10.1117/12.2534911

28 May 2019 Noise reduction in photon-counting CT using frequency-dependent optimal weighting

Mats Persson, Norbert J. Pelc

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110721R (2019) https://doi.org/10.1117/12.2534911
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Spectral computed tomography (CT) allows optimizing image quality by combining the data in several energy channels with optimal weighting factors. In an improvement of this technique, the weighting factors are made dependent on spatial frequency, and previous work has shown that this can improve detectability for a simple detector model. In this work, we investigate the achievable detectability improvement from frequency-dependentweighting for realistic models of photon-counting detectors. We use a Monte-Carlo based simulation model to obtain point-spread functions and autocovariances for two detector models with 0.5 × 0.5 mm² pixels, one CdTe-based with five energy bins and one silicon-based with eight energy bins. We generated noise-only images for two different energy weighting schemes: one where optimal weights were selected individually for each spatial frequency, and one where the weights optimal for zero frequency were applied to all frequencies. The modulation transfer function was set equal in both schemes. Results show that frequency-based weighting can decrease noise variance by 11 % for Si and by 38 % for CdTe, for an edge-enhancing MTF, demonstrating that optimal frequency-dependent weighting has the capability of reducing noise in high-resolution CT images.

Citation Download Citation

Mats Persson and Norbert J. Pelc "Noise reduction in photon-counting CT using frequency-dependent optimal weighting", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721R (28 May 2019); https://doi.org/10.1117/12.2534911

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KEYWORDS

Modulation transfer functions

Sensors

Silicon

Monte Carlo methods

Denoising

Image quality

Photodetectors

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