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Abstract #2726

Deep learning-based whole head segmentation for simultaneous PET/MR attenuation correction

Jakub Baran1,2, Kamlesh Pawar1,3, Nicholas Ferris1,4, Sharna Jamadar1,3,5, Marian Cholewa2, Zhaolin Chen1,6, and Gary Egan1,3,5

1Monash Biomedical Imaging, Monash University, Clayton, Australia, 2Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Rzeszow, Poland, 3Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Clayton, Australia, 4Monash Imaging, Monash Health, Clayton, Australia, 5Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Australia, 6Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia

Estimation of an accurate PET attenuation correction factor is crucial for quantitative PET imaging, and is an active area of research in simultaneous PET/MR. In this work, we propose a deep learning-based image segmentation method to improve the accuracy of PET attenuation correction for simultaneous PET/MR imaging of the human head. We compare segmentation methods for accurate tissue segmentation and attenuation map generation. We demonstrate improved PET image reconstruction accuracy using the proposed deep learning-based method.

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