RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction
This work addresses the need for real-time medical imaging reconstruction, which is incremental as it adapts existing autoencoder methods with a robust cost function for improved speed.
The authors tackled real-time dynamic medical MRI and CT image reconstruction from sparse samples by proposing a robust autoencoder that uses an l1-norm cost function, achieving significantly faster reconstruction than compressed sensing with only slight image quality degradation.
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.