# A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

arXiv: https://arxiv.org/abs/2101.07613 · HTML: https://mlsanity.com/paper/2101.07613/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (2016)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT](https://mlsanity.com/paper/1708.08333/) (2017)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal](https://mlsanity.com/paper/1808.10848/) (2018)
- [ViDeNN: Deep Blind Video Denoising](https://mlsanity.com/paper/1904.10898/) (2019)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion](https://mlsanity.com/paper/1711.10388/) (2017)
- [Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network](https://mlsanity.com/paper/1607.08707/) (2016)
- [Non-Local Video Denoising by CNN](https://mlsanity.com/paper/1811.12758/) (2018)
- [JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data](https://mlsanity.com/paper/1812.00510/) (2018)
- [Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems](https://mlsanity.com/paper/1707.00372/) (2017)

## Built on by (most cited first, 2 of 2)

- [Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2211.13926/) (2022)
- [Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction](https://mlsanity.com/paper/2111.14831/) (2021)

## Abstract (arXiv)

Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4deg, 8deg and 16deg, which appears to be a challenging sparsity that few scholars have attempted before. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.