基于带通滤波的深度神经网络的高度可伸缩图像重建
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
摘要：
为了提高深度神经网络用于图像重建的灵活性和可扩展性，提出了一种基于带通滤波的框架。对于许多应用，间接地执行感测测量。例如，在磁共振成像中，在频域中对数据进行采样。带通滤波的引入使得能够利用已知的成像物理学，同时确保最终重建与实际测量一致以保持重建精度。我们展示了这种灵活的架构，用于重建MRI扫描的子采样数据集。由此产生的高二次采样率提高了MRI采集的速度，并实现了快速血流动力学的可视化。
英文摘要：
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering. For many applications, sensing measurements are performed indirectly. For example, in magnetic resonance imaging, data are sampled in the frequency domain. The introduction of bandpass filtering enables leveraging known imaging physics while ensuring that the final reconstruction is consistent with actual measurements to maintain reconstruction accuracy. We demonstrate this flexible architecture for reconstructing subsampled datasets of MRI scans. The resulting high subsampling rates increase the speed of MRI acquisitions and enable the visualization rapid hemodynamics.
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