Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Deep learning (DL) based image enhancement requires paired data for supervised training. But separately acquired data pairs may encounter spatial mis-alignment that limits the model performance.
Goal(s): Incorporate synthetic data into the training set to address the mis-alignment issue and improve the quality and diversity of the training set.
Approach: Develop and validate the diffusion based image degrader to synthesize low quality images. Compare the performance of DL models trained with/without synthetic data.
Results: DL models trained with synthetic data can achieve similar performance compared to training with acquired pairs. Additional synthetic data can improve DL image enhancement.
Impact: Synthetic data allows building more diverse training sets to achieve multi-task DL models. How much faster the DL model can support and whether it can control the quality of output to meet different clinical preferences is worth further investigation.
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