Work

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

Year: 2025

Type: article

Abstract: 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: ]-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: ]-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.

Source: Medical Physics

DOI: 10.1002/mp.70168

Authors: Cheng‐Han Tsai, Shao‐Yi Huang, Yu‐Nong Lin, Han‐Wei Wang, Ing‐Tsung Hsiao, Kevin T. Chen

Institutions: National Taiwan University, Chang Gung University

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,360