Adaptive three-dimensional (3d) denoising of positron emission tomography (pet) images

A system comprising

• one or more processors and
• one or more non-transitory computer readable media storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• providing a low-dose positron emission tomography (PET) image to a pretrained vision-language model to receive a dose embedding and an anatomy embedding sequence, wherein the dose embedding semantically corresponds to a dose text prompt and the anatomy embedding sequence semantically corresponds to an anatomical text prompt;
• generating a modified time step embedding by mapping the dose embedding into a timestep embedding;
• generating a feature map embedding sequence based on the modified time step embedding and an input feature map;
• providing the feature map embedding sequence and the anatomy embedding sequence to a cross-attention block of a transformer model to receive a cross-attention output;
• generating an output feature map based on the cross-attention output and the feature map embedding sequence;
• and
• providing an input image to a diffusion model to receive a reconstructed image based on the output feature map.

One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• providing a low-dose positron emission tomography (PET) image to a pretrained vision-language model to receive a dose embedding and an anatomy embedding sequence, wherein the dose embedding semantically corresponds to a dose text prompt and the anatomy embedding sequence semantically corresponds to an anatomical text prompt;
• generating a modified time step embedding by mapping the dose embedding into a timestep embedding;
• generating a feature map embedding sequence based on the modified time step embedding and an input feature map;
• providing the feature map embedding sequence and the anatomy embedding sequence to a cross-attention block of a transformer model to receive a cross-attention output;
• generating an output feature map based on the cross-attention output and the feature map embedding sequence;
• and
• providing an input image to a diffusion model to receive a reconstructed image based on the output feature map.