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16 March 2020 Supervised learning of model observers for assessment of CT image reconstruction algorithms
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Given the wide variety of CT reconstruction algorithms currently available { from filtered back projection, to non- linear iterative algorithms, and now even deep learning approaches { there is a pressing need for reconstruction quality metrics that correlate well with task-specific goals. For detection tasks, metrics based on a model observer framework are an attractive option. In this framework, a reconstruction algorithm is assessed based on how well a statistically optimal "model observer" performs on a signal present/signal absent detection task. However, computing exact model observers requires a detailed description of the statistics of the reconstructed images, which are often unknown or computationally intractable to obtain, especially in the case of non-linear reconstruction algorithms. Instead, we study the feasibility of using supervised machine learning approaches to approximate model observers in a CT reconstruction setting. In particular, we show that we can well-approximate the Hotelling observer, i.e., the optimal linear classifier, for a signal-known-exactly/background-known-exactly task by training from labeled training images in the case of FBP reconstruction. We also investigate the feasibility of training multi-layer neural networks to approximate the ideal observer in the case of total variation constrained iterative reconstruction. Our results demonstrate that supervised machine learning methods achieve close to ideal performance in both cases.
Conference Presentation
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Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, and Xiaochuan Pan "Supervised learning of model observers for assessment of CT image reconstruction algorithms", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160B (16 March 2020);

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