Methods and apparatus for reconstruction, adjustment and display of medical images based on displacement vector field data generated by a trained machine learning process

A non-transitory, computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• generating displacement vector field (DVF) data based on applying a trained machine learning process to positron emission tomography (PET) measurement data and modality measurement data, wherein the DVF data characterizes offsets between the PET measurement data and the modality measurement data;
• reconstructing a modality image based on the modality measurement data;
• adjusting the modality image based on the DVF data;
• reconstructing a PET image based on the PET measurement data and the adjusted modality image;
• adjusting the PET image based on the DVF data;
• and
• providing the adjusted PET image for display.

A system comprising:

• a memory storing instructions;
• and
• at least one processor communicatively coupled to the memory and configured to execute the instructions to:
• generating displacement vector field (DVF) data based on applying a trained machine learning process to positron emission tomography (PET) measurement data and modality measurement data, wherein the DVF data characterizes offsets between the PET measurement data and the modality measurement data;
• reconstruct a modality image based on the modality measurement data;
• adjust the modality image based on the DVF data;
• reconstruct a PET image based on the PET measurement data and the adjusted modality image;
• adjust the PET image based on the DVF data;
• and
• provide the adjusted PET image for display.