# Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism

arXiv: https://arxiv.org/abs/2112.00729 · HTML: https://mlsanity.com/paper/2112.00729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 55 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Contrastive Learning for Compact Single Image Dehazing](https://mlsanity.com/paper/2104.09367/) (2021)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction](https://mlsanity.com/paper/2008.00406/) (2020)
- [Low-dose CT denoising with convolutional neural network](https://mlsanity.com/paper/1610.00321/) (2016)
- [Learning Deformable Kernels for Image and Video Denoising](https://mlsanity.com/paper/1904.06903/) (2019)
- [Adapting Models to Signal Degradation using Distillation](https://mlsanity.com/paper/1604.00433/) (2016)

## Built on by (most cited first, 3 of 3)

- [The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2404.10343/) (2024)
- [NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2404.09790/) (2024)
- [NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2404.14248/) (2024)

## Abstract (arXiv)

Reducing the radiation exposure for patients in Total-body CT scans has attracted extensive attention in the medical imaging community. Given the fact that low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. However, most of these methods only adopt Normal-dose CT (NDCT) images as ground truths to guide the training of the denoising network. Such simple restriction leads the model to less effectiveness and makes the reconstructed images suffer from over-smoothing effects. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images to assist the training process on LDCT images. The derived architecture is referred to as the Teacher-Student Consistency Network (TSC-Net), which consists of the teacher network and the student network with identical architecture. Through the supervision between intermediate features, the student network is encouraged to imitate the teacher network and gain abundant texture details. Moreover, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced.CRM performs to pull the restored CT images closer to the NDCT samples and push far away from the LDCT samples in the latent space. In addition, based on the attention and deformable convolution mechanism, we design a Dynamic Enhancement Module (DEM) to improve the network transformation capability.