Evaluation of a machine learning based model observer for x-ray CT

Felix K. Kopp; Marco Catalano; Daniela Pfeiffer; Ernst J. Rummeny; Peter B. Noël

doi:10.1117/12.2293582

7 March 2018 Evaluation of a machine learning based model observer for x-ray CT

Felix K. Kopp, Marco Catalano, Daniela Pfeiffer, Ernst J. Rummeny, Peter B. Noël

Proceedings Volume 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment; 105770S (2018) https://doi.org/10.1117/12.2293582
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

Citation Download Citation

Felix K. Kopp, Marco Catalano, Daniela Pfeiffer, Ernst J. Rummeny, and Peter B. Noël "Evaluation of a machine learning based model observer for x-ray CT", Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770S (7 March 2018); https://doi.org/10.1117/12.2293582

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