Sparse Bayesian Learning for Label Efficiency in Cardiac Real-Time MRI
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Cardiac real-time magnetic resonance imaging (MRI) is an emerging technology
that images the heart at up to 50 frames per second, offering insight into the
respiratory effects on the heartbeat. However, this method significantly
increases the number of images that must be segmented to derive critical health
indicators. Although neural networks perform well on inner slices, predictions
on outer slices are often unreliable.
This work proposes sparse Bayesian learning (SBL) to predict the ventricular
volume on outer slices with minimal manual labeling to address this challenge.
The ventricular volume over time is assumed to be dominated by sparse
frequencies corresponding to the heart and respiratory rates. Moreover, SBL
identifies these sparse frequencies on well-segmented inner slices by
optimizing hyperparameters via type -II likelihood, automatically pruning
irrelevant components. The identified sparse frequencies guide the selection of
outer slice images for labeling, minimizing posterior variance.
This work provides performance guarantees for the greedy algorithm. Testing
on patient data demonstrates that only a few labeled images are necessary for
accurate volume prediction. The labeling procedure effectively avoids selecting
inefficient images. Furthermore, the Bayesian approach provides uncertainty
estimates, highlighting unreliable predictions (e.g., when choosing suboptimal
labels).
