Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
arXiv:1805.03300
Abstract
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.
arXiv comment9 pages, 10 figures
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Cited by in corpus (6)
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- Unsupervised MRI Reconstruction with Generative Adversarial Networks
- Deep Learning for Biomedical Image Reconstruction: A Survey
- Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
- Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
- Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges