Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection

Michael Goetz; Stephan Skornitzke; Christian Weber; Franziska Fritz; Philipp Mayer; Marco Koell; Wolfram Stiller; Klaus H. Maier-Hein

doi:10.1117/12.2216645

24 March 2016 Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection

Michael Goetz; Stephan Skornitzke; Christian Weber; Franziska Fritz; Philipp Mayer; Marco Koell; Wolfram Stiller; Klaus H. Maier-Hein

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851R (2016) https://doi.org/10.1117/12.2216645
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Perfusion CT is well-suited for diagnosis of pancreatic tumors but tends to be associated with a high radiation exposure. Dual-energy CT (DECT) might be an alternative to perfusion CT, offering correlating contrasts while being acquired at lower radiation doses. While previous studies compared intensities of Dual Energy iodine maps and CT-perfusion maps, no study has assessed the combined discriminative power of all information that can be generated from an acquisition of both functional imaging methods. We therefore propose the use of a machine learning algorithm for assessing the amount of information that becomes available by the combination of multiple images. For this, we train a classifier on both imaging methods, using a new approach that allows us to train only from small regions of interests (ROIs). This makes our study comparable to other - ROI-based analysis - and still allows comparing the ability of both classifiers to discriminate between healthy and tumorous tissue. We were able to train classifiers that yield DICE scores over 80% with both imaging methods. This indicates that Dual Energy Iodine maps might be used for diagnosis of pancreatic tumors instead of Perfusion CT, although the detection rate is lower. We also present tumor risk maps that visualize possible tumorous areas in an intuitive way and can be used during diagnosis as an additional information source.

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Michael Goetz, Michael Goetz, Stephan Skornitzke, Stephan Skornitzke, Christian Weber, Christian Weber, Franziska Fritz, Franziska Fritz, Philipp Mayer, Philipp Mayer, Marco Koell, Marco Koell, Wolfram Stiller, Wolfram Stiller, Klaus H. Maier-Hein, Klaus H. Maier-Hein, } "Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851R (24 March 2016); doi: 10.1117/12.2216645; https://doi.org/10.1117/12.2216645

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