Cardiac magnetic resonance imaging (MRI) requires reconstructing a real-time video of a beating heart from continuous highly under-sampled measurements. This task is challenging since the object to be reconstructed (the heart) is continuously changing during signal acquisition. In this paper, we propose a reconstruction approach based on representing the beating heart with an implicit neural network and fitting the network so that the representation of the heart is consistent with the measurements. The network in the form of a multi-layer perceptron with Fourier-feature inputs acts as an effective signal prior and enables adjusting the regularization strength in both the spatial and temporal dimensions of the signal. We study the proposed approach for 2D free-breathing cardiac real-time MRI in different operating regimes, i.e., for different image resolutions, slice thicknesses, and acquisition lengths. Our method achieves reconstruction quality on par with or slightly better than state-of-the-art untrained convolutional neural networks and superior image quality compared to a recent method that fits an implicit representation directly to Fourier-domain measurements. However, this comes at a relatively high computational cost. Our approach does not require any additional patient data or biosensors including electrocardiography, making it potentially applicable in a wide range of clinical scenarios.

翻译：心脏磁共振成像（MRI）需要从连续高度欠采样的测量数据中重建搏动心脏的实时视频。由于待重建目标（心脏）在信号采集过程中持续变化，该任务极具挑战性。本文提出一种基于隐式神经网络表征搏动心脏的重建方法，通过拟合网络使心脏的表征与测量数据保持一致。采用傅里叶特征输入的多层感知机网络结构可作为有效的信号先验，并能够在信号的时空维度上灵活调整正则化强度。我们针对二维自由呼吸心脏实时MRI在不同工作模式（包括不同图像分辨率、层厚及采集时长）下研究了该方法的性能。实验表明，该方法的重建质量与当前最优的无训练卷积神经网络相当或略优，且优于近期直接将隐式表征拟合至傅里叶域测量数据的方法，但计算成本相对较高。本方法无需额外患者数据或生物传感器（包括心电图），因此具有广泛的临床应用潜力。