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16 March 2020 Deep learning-based model observers that replicate human observers for PET imaging
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Abstract
Model observers that replicate human observers are useful tools for assessing image quality based on detection tasks. Linear model observers including nonprewhitening matched filters (NPWMFs) and channelized Hotelling observers (CHOs) have been widely studied and applied successfully to evaluate and optimize detection performance. However, there is still room for improvement in predicting human observer responses in detection tasks. In this study, we used a convolutional neural network to predict human observer responses in a two-alternative forced choice (2AFC) task for PET imaging. Lesion-absent and lesion-present images were reconstructed from clinical PET data with simulated lesions added to the liver and lungs and were used for the 2AFC task. We trained the convolutional neural network to discriminate images that human observers chose as lesion-present and lesion-absent in the 2AFC task. We evaluated the performance of the trained network by calculating the concordance between human observer responses and predicted responses from the network output and compared it to those of NPWMF and CHO. The trained network showed better agreement with human observers than the linear NPWMF and CHO model observers. The results demonstrate the potential for convolutional neural networks as model observers that better predict human performance. Such model observers can be used for optimizing scanner design, imaging protocols, and image reconstruction to improve lesion detection in PET imaging.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fenglei Fan, Sangtae Ahn, Bruno De Man, Kristen A. Wangerin, Scott D. Wollenweber, Craig K. Abbey, and Paul E. Kinahan "Deep learning-based model observers that replicate human observers for PET imaging", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160E (16 March 2020); https://doi.org/10.1117/12.2547505
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