Keywords: Heart, Machine Learning/Artificial IntelligenceLow field MRI systems are promising to increase the accessibility of cardiac MRI, at the expense of lower SNR. Here, we present the application of a g-factor-savvy denoising for cine imaging at 0.55T to increase useable acceleration rate. We used a model that combines convolutional neural networks and transformer model. The denoising network uses complex 2D+time images in SNR-units and g-factor maps as inputs and was trained with 3T data. The percent mean myocardial SNR gain at 0.55T across 9 healthy volunteers was 97±31% (R=2) and 122±22% (R=3), with no indication of overt temporal or spatial smoothing.
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