TY - CHAP
T1 - Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures
AU - Luo, Yimin
AU - Toth, Daniel
AU - Jiang, Kui
AU - Pushparajah, Kuberan
AU - Rhode, Kawal
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The continuous development and prolonged use of X-ray fluoroscopic imaging in cardiac catheter-based procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for low-dose X-ray images are needed to improve safety and reliability. Previous X-ray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised X-ray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultra-dense denoising network (UDDN) within the CNN framework for X-ray image denoising in cardiac catheter-based procedures. After patch-based iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) when compared to previous CNN architectures.
AB - The continuous development and prolonged use of X-ray fluoroscopic imaging in cardiac catheter-based procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for low-dose X-ray images are needed to improve safety and reliability. Previous X-ray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised X-ray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultra-dense denoising network (UDDN) within the CNN framework for X-ray image denoising in cardiac catheter-based procedures. After patch-based iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) when compared to previous CNN architectures.
UR - http://www.scopus.com/inward/record.url?scp=85081903074&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39074-7_4
DO - 10.1007/978-3-030-39074-7_4
M3 - Conference paper
AN - SCOPUS:85081903074
SN - 9783030390730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 31
EP - 42
BT - Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
A2 - Pop, Mihaela
A2 - Sermesant, Maxime
A2 - Camara, Oscar
A2 - Zhuang, Xiahai
A2 - Li, Shuo
A2 - Young, Alistair
A2 - Mansi, Tommaso
A2 - Suinesiaputra, Avan
PB - SPRINGER
T2 - 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -