# RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction

arXiv: https://arxiv.org/abs/1912.07519 · HTML: https://mlsanity.com/paper/1912.07519/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [The Significance of Machine Learning in Clinical Disease Diagnosis: A Review](https://mlsanity.com/paper/2310.16978/) (2023)
- [Predicting Disease Progress with Imprecise Lab Test Results](https://mlsanity.com/paper/2107.03620/) (2021)
- [Kyrtos: A methodology for automatic deep analysis of graphic charts with curves in technical documents](https://mlsanity.com/paper/2602.09337/) (2026)

## Abstract (arXiv)

In this work we address the problem of real-time dynamic medical MRI and X Ray CT image reconstruction from parsimonious samples Fourier frequency space for MRI and sinogram tomographic projections for CT. Today the de facto standard for such reconstruction is compressed sensing. CS produces high quality images (with minimal perceptual loss, but such reconstructions are time consuming, requiring solving a complex optimization problem. In this work we propose to learn the reconstruction from training samples using an autoencoder. Our work is based on the universal function approximation capacity of neural networks. The training time for the autoencoder is large, but is offline and hence does not affect performance during operation. During testing or operation, our method requires only a few matrix vector products and hence is significantly faster than CS based methods. In fact, it is fast enough for real-time reconstruction the images are reconstructed as fast as they are acquired with only slight degradation of image quality. However, in order to make the autoencoder suitable for our problem, we depart from the standard Euclidean norm cost function of autoencoders and use a robust l1-norm instead. The ensuing problem is solved using the Split Bregman method.