Medical physics · 2025
Paired PET-MRI Deep Learning Model for Translating [11C]PiB to [18F]Florbetaben Amyloid Images.
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Tsai Cheng-Han, Huang Shao-Yi, Lin Yu-Nong, Wang Han-Wei, Hsiao Ing-Tsung, Chen Kevin T
Аннотация
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 [ 11 C $^{11}\textrm {C}$ ]-Pittsburgh compound B-to-[ 18 F $^{18}\textrm {F}$ ]-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 [ 18 F $^{18}\textrm {F}$ ]-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.
PURPOSE: This paper proposes a U-Net model based on a deformable convolution network (DCNv3-based U-Net) for [ 11 C $^{11}\textrm {C}$ ]-Pittsburgh compound B-to-[ 18 F $^{18}\textrm {F}$ ]-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 [ 18 F $^{18}\textrm {F}$ ]-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.