Unsupervised denoising is useful as it allows low-count PET image recovery without the need of paired training data(low-count/full-count). However, current unsupervised denoising models utilize Contrast-to-Noise Ratio as stopping criteria to optimize the image recovery process, which can be improved by considering structural information to maintain the integrity of gross anatomy. In this work, we proposed an MRI structural regularization loss function for low-count PET image recovery using an unsupervised learning model, which does not require paired training sets and demonstrated that the proposed method is superior in both qualitative and quantitative analyses for two radiotracers with very different physiological uptake.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords