Public articles linked to the same research event.
arXiv The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.