Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal heart. The ability of deep learning methods to recover undersampled data could help to optimise the kt-SENSE acquisition strategy and improve non-gated kt-SENSE reconstruction quality. In this work, we explore supervised deep learning networks for reconstruction of kt-SENSE style acquired data using an extensive in vivo dataset. Having access to fully-sampled low-resolution multi-coil fetal cardiac MRI, we study the performance of the networks to recover fully-sampled data from undersampled data. We consider model architectures together with training strategies taking into account their application in the real clinical setup used to collect the dataset to enable networks to recover prospectively undersampled data. We explore a set of modifications to form a baseline performance evaluation for dynamic fetal cardiac MRI on real data. We systematically evaluate the models on coil-combined data to reveal the effect of the suggested changes to the architecture in the context of fetal heart properties. We show that the best-performers recover a detailed depiction of the maternal anatomy on a large scale, but the dynamic properties of the fetal heart are under-represented. Training directly on multi-coil data improves the performance of the models, allows their prospective application to undersampled data and makes them outperform CTFNet introduced for adult cardiac cine MRI. However, these models deliver similar qualitative performances recovering the maternal body very well but underestimating the dynamic properties of fetal heart. This dynamic feature of fast change of fetal heart that is highly localised suggests both more targeted training and evaluation methods might be needed for fetal heart application.

翻译：动态自由呼吸胎儿心脏MRI是最具挑战性的成像模态之一，需要高时空分辨率来描绘胎儿小心脏的快速变化。深度学习方法恢复欠采样数据的能力有助于优化kt-SENSE采集策略并改善非门控kt-SENSE重建质量。本研究利用广泛的在体数据集，探索了用于kt-SENSE风格采集数据重建的监督式深度学习网络。通过获取全采样低分辨率多线圈胎儿心脏MRI数据，我们研究了网络从欠采样数据中恢复全采样数据的能力。我们考虑了模型架构及训练策略，并兼顾其在临床实际采集环境中的应用，使网络能够恢复前瞻性欠采样数据。我们探索了一系列改进措施，为真实数据上的动态胎儿心脏MRI建立基线性能评估。通过对线圈组合数据进行系统性模型评估，我们揭示了在胎儿心脏特性背景下建议的架构改进效果。结果表明，性能最优的模型能大规模恢复母体解剖结构的细节，但胎儿心脏的动态特性未能充分体现。直接对多线圈数据进行训练可提升模型性能，使其能够前瞻性应用于欠采样数据，并超越针对成人心脏电影MRI提出的CTFNet。然而，这些模型在定性表现上相似——能很好恢复母体结构，但低估了胎儿心脏的动态特性。这种胎儿心脏快速变化且高度局部化的动态特征表明，针对胎儿心脏应用可能需要更精准的训练与评估方法。