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Proceedings Paper

Evaluation of a machine learning based model observer for x-ray CT
Author(s): Felix K. Kopp; Marco Catalano; Daniela Pfeiffer; Ernst J. Rummeny; Peter B. Noël
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Paper 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.

Paper Details

Date Published: 7 March 2018
PDF: 7 pages
Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770S (7 March 2018); doi: 10.1117/12.2293582
Show Author Affiliations
Felix K. Kopp, Technische Univ. München (Germany)
Marco Catalano, Humanitas Clinical and Research Hospital (Italy)
Daniela Pfeiffer, Technische Univ. München (Germany)
Ernst J. Rummeny, Technische Univ. München (Germany)
Peter B. Noël, Technische Univ. München (Germany)


Published in SPIE Proceedings Vol. 10577:
Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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