Experimental study of photon-counting CT neural network material decomposition under conditions of pulse pileup.

Content Provider	Europe PMC
Author	Jenkins, Parker J. B. Schmidt, Taly Gilat
Copyright Year	2021
Abstract	Abstract.Purpose: We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions.Approach: Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth.Results: There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively.Conclusions: Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7797008&blobtype=pdf
Page Count	14
ISSN	23294302
Journal	Journal of Medical Imaging [J Med Imaging (Bellingham)]
Volume Number	8
PubMed Central reference number	PMC7797008
Issue Number	1
PubMed reference number	33447645
e-ISSN	23294310
DOI	10.1117/1.jmi.8.1.013502
Language	English
Publisher	Society of Photo-Optical Instrumentation Engineers
Publisher Date	2021-01-09
Access Restriction	Open
Rights License	Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. © 2021 The Authors
Subject Keyword	neural networks machine learning photon counting computed tomography pulse pileup spectral CT
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging