Deep learning (DL)-based denoising is promising to achieve high resolution diffusion-weighted imaging (HR-DWI) by improving SNR without signal averaging. Training supervised DL-based algorithm, however, requires thousands of teaching data, which need long acquisition time. In this study, we propose to use noise2noise (N2N) theory to develop DL-based denoising algorithm, which does not need teaching data with high SNR. In the results, the proposed method (N2N-MRI-based algorithm) outperformed conventional ground-truth-based algorithm in terms of maximum peak SNRs on validation sets during training. The image quality of HR-DWI denoised by N2N-MRI-based algorithm was equivalent to that denoised by conventional algorithm.
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