Normal Dose

"Normal dose" in medical imaging research primarily refers to the standard radiation dose used in computed tomography (CT) scans, serving as a benchmark for comparison against low-dose techniques aimed at reducing patient radiation exposure. Current research focuses on developing deep learning models, particularly employing U-Net architectures and graph neural networks, for denoising low-dose CT images, often using unsupervised or self-supervised learning methods to overcome the limitations of requiring paired low-dose/normal-dose datasets. These advancements are significant because they could lead to improved image quality in low-dose CT scans, ultimately benefiting patients by reducing radiation exposure without compromising diagnostic accuracy.

Papers

February 2, 2024

DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks
Zehao Dong, Yixin Chen, Tianyu Zhao
Graph Neural Network Deep Learning Model System Performance Normal Dose Dose Volume Histogram

August 13, 2023

Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
Yuting Zhu, Qiang He, Yudong Yao, Yueyang Teng
Image Denoising Low Dose Computed Tomography Modular Network Normal Dose

July 23, 2023

ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
Zhihao Chen, Qi Gao, Yi Zhang, Hongming Shan
Supervised Contrastive Learning Low Dose Low Dose Computed Tomography Normal Dose

May 25, 2023

Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Xuan Liu, Yaoqin Xie, Jun Cheng, Songhui Diao, Shan Tan, Xiaokun Liang
Zero Shot Denoising Process Diffusion Prior Low Dose Low Dose Computed Tomography CT Denoising Normal Dose

March 1, 2023

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT
Md Ashequr Rahman, Zitong Yu, Barry A. Siegel, Abhinav K. Jha
Low Dose Normal Dose Myocardial Perfusion

February 26, 2023

Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment
Kecheng Chen, Jie Liu, Renjie Wan, Victor Ho-Fun Lee, Varut Vardhanabhuti, Hong Yan, Haoliang Li
Domain Adaptation Low Dose Low Dose Computed Tomography Normal Dose

February 8, 2023

QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Yuhui Ruan, Qiao Yuan, Chuang Niu, Chen Li, Yudong Yao, Ge Wang, Yueyang Teng
Low Dose Low Dose Computed Tomography Unpaired Data CT Denoising Local Similarity Artifact Disentanglement Network Normal Dose

November 3, 2022

Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence
Sutanu Bera, Prabir Kumar Biswas
Low Dose Invertible Neural Network Invertible Network Inter Slice Normal Dose

January 5, 2022

Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation
Eduardo Weber Wachter, Server Kasap, Sefki Kolozali, Xiaojun Zhai, Shoaib Ehsan, Klaus McDonald-Maier
Machine Learning Anomaly Detection Field Programmable Gate Array Radiation Detector System on Chip Normal Dose Radiation Source

December 1, 2021

Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism
Minghan Fu, Yanhua Duan, Zhaoping Cheng, Wenjian Qin, Ying Wang, Dong Liang, Zhanli Hu
Knowledge Transfer Whole Body Low Dose Low Dose Computed Tomography Contrastive Regularization CT Denoising Normal Dose

Normal Dose

Papers

DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks

Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising

ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment

QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data

Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence

Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation

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