RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction
By Janki Mehta, Angshul Majumdar. 2017
64 citations recorded by the source corpus.
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 l1-norm instead. The ensuing problem is solved using the Split Bregman method.
Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging
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In Biomedical Engineering
In Radiology, Nuclear Medicine and Imaging
- Muhammad Bilal
- Ala Al-Fuqaha
- Junaid Qadir
- Adnan Qayyum
- Shakked Noy
- Whitney Zhang
- Louisa H.Y. Tsi
- Cecilia Ka Yuk Chan
Citations in the corpus
- Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “models is drastically increasing and various DL models such as CNNs [54] and autoencoders [6] have been extensively used for the reconstruction of MRI and CT images.”