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Abstract #2425

Bias correction for PET/MR attenuation correction using generative adversarial networks

Bendik Skarre Abrahamsen1, Tone Frost Bathen1,2, Live Eikenes1, and Mattijs Elschot1,2
1Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway, 2Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Attenuation correction is a challenge in PET/MRI. In this study we propose a novel attenuation correction method based on estimating the bias image between PET reconstructed using a 4-class attenuation correction map and PET reconstructed with an attenuation correction map where bone information is added from a co-registered CT image. A generative adversarial network was trained to estimate the bias between the PET images. The proposed method has comparable performance to other Deep Learning based attenuation correction methods where no additional MRI sequences are acquired. Bias estimation thus constitutes a viable alternative to pseudo-CT generation for PET/MR attenuation correction.

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