# Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering

arXiv: https://arxiv.org/abs/1805.03300 · HTML: https://mlsanity.com/paper/1805.03300/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 4 of 4)

- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction](https://mlsanity.com/paper/2205.03883/) (2022)
- [Automatic Cell Counting in Flourescent Microscopy Using Deep Learning](https://mlsanity.com/paper/2103.01141/) (2021)
- [Explicit Use of Fourier Spectrum in Generative Adversarial Networks](https://mlsanity.com/paper/2208.01265/) (2022)

## Abstract (arXiv)

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.