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.

翻译：心脏磁共振成像是一种无创识别心血管疾病的重要工具。例如，Cine MRI是评估心脏功能和解剖结构的金标准影像学方法。另一方面，多对比度（T1和T2）映射可评估心肌及间质的病理异常。然而，受试者自主屏气配合度差及常见的心律失常问题，结合MRI较慢的成像速度，常导致运动伪影，阻碍实时采集的图像质量。虽然加速采集能促进动态成像，但会引发混叠效应，导致Cine MRI重建图像质量下降及T1/T2映射估计不准确。本研究受加速MRI重建相关工作启发，提出一种基于深度学习（DL）的方法，用于动态心脏成像中的加速Cine及多对比度重建。我们将重建问题建模为最小二乘正则化优化任务，采用vSHARP——一种结合半二次变量分裂和交替方向乘子法的先进深度学习逆问题求解器。我们分两种场景处理问题：二维重建任务和二维动态重建任务，分别采用2D和3D深度学习网络。该方法同时在图像域和k空间域进行优化，实现高保真重建。尽管目标数据采用笛卡尔等间距欠采样方案，我们通过使用笛卡尔和模拟非笛卡尔欠采样方案训练模型，增强其对未见数据的泛化能力。此外，模型采用深度神经网络学习并优化多线圈k空间数据的灵敏度图。最后，该方法在欠采样的Cine和多对比度数据上联合训练。