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Abstract #0264

An MR based PET attenuation correction using a deep learning approach and evaluation in prostate cancer patients

Andrii Pozaruk1,2, Kamlesh Pawar1,3, Shenpeng Li1,4, Alexandra Carey1,5, Yen-Cheng Henry Pan6, Viswanath P Sudarshan1,7, Marian Cholewa2, Jeremy Grummet6, Zhaolin Chen1,4, and Gary Egan1,3

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, 4Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia, 5Monash Imaging, Monash Health, Clayton, Australia, 6Department of Surgery, Monash University, Melbourne, Australia, 7Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India

Accurate Magnetic Resonance (MR) imaging based attenuation correction is crucial for quantitative Positron Emission Tomography (PET) in simultaneous MR/PET imaging. However, due to a lack of robust MR bone imaging methods, MR based attenuation correction remains a critical issue in MR/PET image reconstruction. In this work, we developed and evaluated a deep learning (DL) based MR based attenuation correction method for improved MR/PET quantification accuracy in prostatic cancer imaging.

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