RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction
作者：
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.
摘要
•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.
论文关键词：Autoencoder,MRI,Compressed sensing,CT reconstruction
论文评审过程：Received 25 January 2016, Revised 10 May 2016, Accepted 21 September 2016, Available online 28 September 2016, Version of Record 27 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.022
