Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: MRI guidance of an interventional procedure requires fast image reconstruction. A neural network(NN)-based approach can exploit the similarities between consecutive frames to improve iMRI image reconstruction.
Goal(s): We investigate if an LSTM can reconstruct images from just ten spokes per frame in a timeframe compatible with iMRI.
Approach: A convolutional (conv)LSTM was trained using the open-source ACDC dataset. Results were compared with Multi-domain convolutional neural network (MD-CNN) - a recently-published 3D NN-based method for undersampled MRI reconstruction.
Results: ConvLSTMs can reconstruct frames at ~226 fps (17x faster than MD-CNN ~13 fps). SSIM for the convLSTM was slightly lower than the MD-CNN (0.85 vs 0.89).
Impact: With our LSTM-based model, we have achieved a 17x speed-up in the iMRI acquisition process without significant loss in image quality. This suggests that an LSTM-based method could be used to improve iMRI image speed and quality.
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