Neel Dey

Longitudinal whole-body PET/CT registration has relied mainly on raw image intensities or sparse segmentation labels to establish correspondence; intensities are fragile under cross-modal appearance change, and labels assign only coarse identities to fine anatomy. This work instead uses per-voxel semantic representations from the 3D foundation model anatomix, combined with a hand-crafted MIND-SSC descriptor and segmentation labels, to drive the FireANTs solver through two optimization stages with lesion-volume preservation constraints. On the 2026 Learn2Reg PSMAReg Challenge test set the system ranked first without training on the challenge training set, with MTV error 1.78% and TLG error 1.39%.

Longitudinal whole-body PET/CT registration has relied mainly on raw image intensities or sparse segmentation labels to establish correspondence; intensities are fragile under cross-modal appearance change, and labels assign only coarse identities to fine anatomy. This work instead uses per-voxel semantic representations from the 3D foundation model anatomix, combined with a hand-crafted MIND-SSC descriptor and segmentation labels, to drive the FireANTs solver through two optimization stages with lesion-volume preservation constraints. On the 2026 Learn2Reg PSMAReg Challenge test set the system ranked first without training on the challenge training set, with MTV error 1.78% and TLG error 1.39%.