LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT

By Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Wei-hua Zhang, Huaiqiang Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang. IEEE Transactions on Medical Imaging, 2017

408 citations recorded by the source corpus.

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses 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 the 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 superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the 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 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d2071690de482b778bc6fd781dbc1ef1f605bb71

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yunjie Yang
2. Jun Shu
3. Lei Xing
4. John M. Pauly
5. Deyu Meng
6. Yonggui Dong
7. Jinxi Xiang
8. Zongben Xu

In Radiology, Nuclear Medicine and Imaging

1. Shreyas S. Vasanawala
2. Zongben Xu
3. John M. Pauly
4. Lei Xing
5. Yunjie Yang
6. Jun Shu
7. Deyu Meng
8. Yonggui Dong

Citations in the corpus

• FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging (2020, background): “and upsampling of UNet and the image quality could be suboptimal, as commented by [64].”
• Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI (2018, background): “In another approach, [1], [4], [6], [10], [12], and [29] use deep neural networks to learn image priors, or, sparsifying transforms from historical data.”
• Small Sample Learning in Big Data Era (2018, methodology): “…applied into dynamic MR image reconstruction (Schlemper et al., 2018; Qin et al., 2017), sparse view/data Computed Tomography(CT) reconstruction (Gupta et al., 2018; Chen et al., 2017; Adler and Öktem, 2018), compressive image reconstruction (Metzler et al., 2017; Zhang and Ghanem, 2018).”