RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction. (March 2017)

• Record Type: Journal Article
• Authors: Mehta, Janki; Majumdar, Angshul
• Is Part Of: Pattern recognition. Volume 63(2017:Mar.)
• Page Start: 499
• Page End: 510
• Publication Date: 2017-03
• Subjects: Autoencoder -- MRI -- Compressed sensing -- CT reconstruction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.09.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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). 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/operation, our method requires only a few matrix vector products and hence is significantly faster than CS based methods. In fact, for MRI 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; for CT our reconstruction speed is slightly slower than required for real-time reconstruction. 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 l 1 -norm instead. The ensuing problem is solved using the Split Bregman method. Highlights: We address the problem of real-time reconstruction of MRI and CT images from sub-sampled data. Compressed Sensing is the de facto standard for solving such problems. This work proposes an alternate approach; instead of designing/formulating the inversion, we 'learn' it. Robust autoencoder has been proposed in this work, to learn the inversion. Results show, our method is only slightly worse than CS methods but is capable of achieving real-time speed.