Abstract #0892

Evaluation of deep learning high-resolution Dixon PET/MR attenuation correction using 16-channel head-neck and 32-channel head coils

Chunwei Ying¹, Yasheng Chen², Matthew R. Brier², Shaney Flores¹, Richard Laforest¹, Tammie L. S. Benzinger^1,2,3, and Hongyu An^1,2

¹Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States, ²Department of Neurology, Washington University School of Medicine, St Louis, MO, United States, ³Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, United States

Synopsis

Keywords: PET/MR, Brain, attenuation correctionWe evaluated the accuracy of a deep learning-based PET/MR attenuation correction (AC) method with vendor-provided high-resolution Dixon in- and opp-phase images as inputs (DL-HiRes). We found that the DL-HiRes AC method significantly outperformed the vendor-provided skull model AC method for both 16-channel head-neck coil and 32-channel head coil (p<0.001). Moreover, the DL-HiRes method had similar AC accuracy using different head coils.

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