  Cardiac magnetic resonance imaging is a valuable non-invasive tool for
identifying cardiovascular diseases. For instance, Cine MRI is the benchmark
modality for assessing the cardiac function and anatomy. On the other hand,
multi-contrast (T1 and T2) mapping has the potential to assess pathologies and
abnormalities in the myocardium and interstitium. However, voluntary
breath-holding and often arrhythmia, in combination with MRI's slow imaging
speed, can lead to motion artifacts, hindering real-time acquisition image
quality. Although performing accelerated acquisitions can facilitate dynamic
imaging, it induces aliasing, causing low reconstructed image quality in Cine
MRI and inaccurate T1 and T2 mapping estimation. In this work, inspired by
related work in accelerated MRI reconstruction, we present a deep learning
(DL)-based method for accelerated cine and multi-contrast reconstruction in the
context of dynamic cardiac imaging. We formulate the reconstruction problem as
a least squares regularized optimization task, and employ vSHARP, a
state-of-the-art DL-based inverse problem solver, which incorporates
half-quadratic variable splitting and the alternating direction method of
multipliers with neural networks. We treat the problem in two setups; a 2D
reconstruction and a 2D dynamic reconstruction task, and employ 2D and 3D deep
learning networks, respectively. Our method optimizes in both the image and
k-space domains, allowing for high reconstruction fidelity. Although the target
data is undersampled with a Cartesian equispaced scheme, we train our model
using both Cartesian and simulated non-Cartesian undersampling schemes to
enhance generalization of the model to unseen data. Furthermore, our model
adopts a deep neural network to learn and refine the sensitivity maps of
multi-coil k-space data. Lastly, our method is jointly trained on both,
undersampled cine and multi-contrast data.
