Abstract #0264

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

Andrii Pozaruk^1,2, Kamlesh Pawar^1,3, Shenpeng Li^1,4, Alexandra Carey^1,5, Yen-Cheng Henry Pan⁶, Viswanath P Sudarshan^1,7, Marian Cholewa², Jeremy Grummet⁶, Zhaolin Chen^1,4, and Gary Egan^1,3

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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