LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

Abstract

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-the-art "fields of experts" based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a competitive performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 12, reducing the computational complexity of typical iterative algorithms by orders of magnitude.

arXiv comment18 pages, 15 figures, accepted by IEEE TMI

References in corpus (7)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
• Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT
• Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
• Learning a Variational Network for Reconstruction of Accelerated MRI Data

Cited by in corpus (5)

• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
• Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
• Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
• Learning from Pseudo-Randomness With an Artificial Neural Network - Does God Play Pseudo-Dice?