Paired PET‐MRI Deep Learning Model for Translating [ ¹¹ C]PiB to [ ¹⁸ F]Florbetaben Amyloid Images

Background Amyloid PET imaging has been extensively employed in the noninvasive assessment of amyloid‐beta accumulation in Alzheimer's disease. Various amyloid radiotracers are commonly used in clinical settings; however, the limited interchangeability among these radiotracers hinders the feasibility of long‐term clinical trials and multicenter comparisons. The Centiloid method was proposed for standardization, though providing a single score per image; voxel‐wise translation remains a formidable task. Purpose This paper proposes a U‐Net model based on a deformable convolution network (DCNv3‐based U‐Net) for []‐Pittsburgh compound B‐to‐[]‐florbetaben image translation to augment existing datasets for large‐scale model training and provide image information when inconsistencies between visual assessments and the Centiloid scale occur. Methods The DCNv3‐based U‐Net combined the benefits of deformable convolution that captures long‐range dependencies with efficient computation and the encoder–decoder architecture with skip connections for local–global feature learning and image synthesis. Results The prediction images presented increased homogeneity to other previous models, closely resembling the texture of []‐florbetaben. Conclusions The DCNv3‐based U‐Net demonstrated high performance in metrics measurement and statistical analyses for the PET image‐to‐image translation task. This work also justified the importance of MR images in providing structural information.