@inproceedings{5c2989be9d42472b821771c347040869,
title = "Evaluation of a machine learning based model observer for x-ray CT",
abstract = "In the medical imaging domain, image quality assessment is usually carried out by human observers (HuO) performing a clinical task in reader studies. To overcome time-consuming reader studies numerical model observers (MO) were introduced and are now widely used in the CT research community to predict the performance of HuOs. In the recent years, machine learning based MOs showed promising results for SPECT. Therefore, we built a neural network, a socalled softmax regression model based on machine learning, as MO for x-ray CT. Performance was evaluated by comparing to one of the most prevalent MOs, the channelized Hotelling observer (CHO). CT image data labeled with confidence ratings assessed in a reader study for a detection-task of signals of different sizes, different noise levels and different reconstruction algorithms were used to train and test the MOs. Data was acquired with a clinical CT scanner. For each of four different x-ray radiation exposures, there were 208 repeated scans of a Catphan phantom. The neural network based MO (NN-MO) as well as the CHO showed good agreement with the performance in the reader study.",
keywords = "CHO, Image Quality, Machine Learning, Model Observer, Neural Network",
author = "Kopp, {Felix K.} and Marco Catalano and Daniela Pfeiffer and Rummeny, {Ernst J.} and No{\"e}l, {Peter B.}",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment ; Conference date: 11-02-2018 Through 12-02-2018",
year = "2018",
doi = "10.1117/12.2293582",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Samuelson, {Frank W.} and Nishikawa, {Robert M.}",
booktitle = "Medical Imaging 2018",
}