  Accelerating dynamic MRI is essential for enhancing clinical applications,
such as adaptive radiotherapy, and improving patient comfort. Traditional deep
learning (DL) approaches for accelerated dynamic MRI reconstruction typically
rely on predefined or random subsampling patterns, applied uniformly across all
temporal phases. This standard practice overlooks the potential benefits of
leveraging temporal correlations and lacks the adaptability required for
case-specific subsampling optimization, which holds the potential for
maximizing reconstruction quality. Addressing this gap, we present a novel
end-to-end framework for adaptive dynamic MRI subsampling and reconstruction.
Our pipeline integrates a DL-based adaptive sampler, generating case-specific
dynamic subsampling patterns, trained end-to-end with a state-of-the-art 2D
dynamic reconstruction network, namely vSHARP, which effectively reconstructs
the adaptive dynamic subsampled data into a moving image. Our method is
assessed using dynamic cine cardiac MRI data, comparing its performance against
vSHARP models that employ common subsampling trajectories, and pipelines
trained to optimize dataset-specific sampling schemes alongside vSHARP
reconstruction. Our results indicate superior reconstruction quality,
particularly at high accelerations.
