Keywords: Image Reconstruction, Image Reconstruction, Deep learning, Self-supervised
Motivation: For low SNR training data, such as from low-field scanners, sub-sampled images reconstructed via deep learning can be susceptible to errors due to measurement noise.
Goal(s): To evaluate the performance of the proposed Robust Self-Supervised Learning via Data Undersampling (Robust SSDU), which removes corruptions due to aliasing and measurement noise in an entirely self-supervised manner.
Approach: On the fastMRI dataset and low-field dataset M4Raw, Robust SSDU was compared with a number of benchmarks including supervised training.
Results: Robust SSDU exhibited a substantially higher fidelity image restoration than standard SSDU and sharper reconstructions than competing methods that remove measurement noise.
Impact: This study demonstrates that high quality image reconstruction with deep learning is achievable when only sub-sampled, low SNR data is available for training. The proposed method could particularly impact the diagnostic potential of images acquired from low field scanners.
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