Experimental study of neural network material decomposition to account for pulse-pileup effects in photon-counting spectral CT

Parker Jenkins; Taly Gilat Schmidt

doi:10.1117/12.2513447

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1 March 2019 Experimental study of neural network material decomposition to account for pulse-pileup effects in photon-counting spectral CT

Parker Jenkins, Taly Gilat Schmidt

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Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109481Y (2019) https://doi.org/10.1117/12.2513447
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Spectral CT with photon-counting detectors has demonstrated potential for improved material decomposition but is challenged by nonideal effects such as pulse pileup. The purpose of this study was to investigate the performance of Neural Network (NN) material decomposition under varying pulse-pileup conditions. We hypothesize that the NN can compensate for pileup effects as it learns the relationship between the spectral measurements and basis material thicknesses through calibration. Photon-counting experiments were performed to: (1) investigate the optimal NN architecture across varying pileup conditions, (2) quantify the performance of NN material decomposition across varying pileup conditions and (3) demonstrate material decomposition of photon-counting CT data across varying pileup conditions. The NN was trained with log-normalized spectral transmission measurements through known thicknesses of basis materials (PMMA and aluminum) at five flux levels. The trained NN was then applied to photoncounting transmission measurements through Teflon, Delrin, and Neoprene to estimate the basis material thicknesses. The trained neural network was also applied to photon-counting CT data of a rod phantom. The optimal NN configuration remained generally consistent across the studied flux levels, thus a NN configuration with five hidden-layer nodes was selected for the subsequent analysis. For the test material slabs, overall decomposition error decreased with flux for Teflon and Delrin, while increasing with flux for Neoprene. The CT experiments showed lowest material decomposition error for the lower flux condition, but similar error for the two higher flux conditions. Pulse-pileup decreased the variation in material decomposition estimates. The effects of pileup on material decomposition error varied across different materials. These preliminary results suggest that NN material decomposition may account for pulse-pileup effects in photon-counting CT.

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Parker Jenkins and Taly Gilat Schmidt "Experimental study of neural network material decomposition to account for pulse-pileup effects in photon-counting spectral CT", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109481Y (1 March 2019); https://doi.org/10.1117/12.2513447

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