IEEE Transactions on Medical Imaging· 2017Q1
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
- 1,840citations
- Q1SCImago
- 2017year
Short summary
A novel Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) effectively suppresses noise and preserves structural details in low-dose CT images, outperforming current state-of-the-art methods.
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Key points
- A new RED-CNN architecture is proposed for low-dose CT imaging.
- The RED-CNN processes reconstructed images directly, unlike methods requiring raw data.
- The network combines autoencoder, deconvolution, and shortcut connections.
- RED-CNN achieves competitive performance in noise suppression, structural preservation, and lesion detection.
- The method shows promise in both simulated and clinical low-dose CT cases.
AI-generated from the title and abstract; the full text is not read.
Abstract
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.
The authors' abstract, as published at the source. IEEE Transactions on Medical Imaging, 2017 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine