Patent ID: 11915401
Assignee: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 4:
5. The method according to claim 1, wherein the source modal image data is a positron emission tomography (PET) image, the target modal image is a computed tomography (CT) image and a magnetic resonance image (MRI) image, and the apriori guidance network is trained taking a specified joint loss function as an optimization objective by:
collecting a PET/CT data set and a PET/MRI data set, denoting a domain mark of the PET/CT data set as a first domain, and denoting a domain mark of the PET/MRI data set as a second domain;
mixing the PET/CT data set and the PET/MRI data set for constructing a mixed data set having the first domain mark and the second domain mark, and then determining a corresponding apriori feature according to a corresponding domain mark of the mixed data set; and
storing, by using the apriori guidance module, a parameter representative of a non-repetitive part between a CT image generation task and an MRI image generation task;
wherein when the PET image having different domain marks is inputted, a parameter of the apriori guidance module is updated, and an alternate training from a PET modality image to a CT modality image and from the PET modality image to an MRI modality image is achieved.