Computationally efficient deep neural network for computed tomography image reconstruction
Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …
promising performance on low‐dose CT imaging in recent years. However, most existing …
BCD-Net for low-dose CT reconstruction: Acceleration, convergence, and generalization
Obtaining accurate and reliable images from low-dose computed tomography (CT) is
challenging. Regression convolutional neural network (CNN) models that are learned from …
challenging. Regression convolutional neural network (CNN) models that are learned from …
Artificial intelligence in image reconstruction: the change is here
Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …
the hardware and software domain. The range and speed of CT scanning improved from the …
CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels
W Xia, Z Lu, Y Huang, Y Liu, H Chen… - … on Medical Imaging, 2021 - ieeexplore.ieee.org
The current mainstream computed tomography (CT) reconstruction methods based on deep
learning usually need to fix the scanning geometry and dose level, which significantly …
learning usually need to fix the scanning geometry and dose level, which significantly …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
PYRO‐NN: Python reconstruction operators in neural networks
Purpose Recently, several attempts were conducted to transfer deep learning to medical
image reconstruction. An increasingly number of publications follow the concept of …
image reconstruction. An increasingly number of publications follow the concept of …
[HTML][HTML] Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network
Q Li, S Li, R Li, W Wu, Y Dong, J Zhao… - … Imaging in Medicine …, 2022 - ncbi.nlm.nih.gov
Background Computed tomography (CT) is widely used in medical diagnoses due to its
ability to non-invasively detect the internal structures of the human body. However, CT scans …
ability to non-invasively detect the internal structures of the human body. However, CT scans …
[HTML][HTML] The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
Noise characteristics modeled unsupervised network for robust CT image reconstruction
Deep learning (DL)-based methods show great potential in computed tomography (CT)
imaging field. The DL-based reconstruction methods are usually evaluated on the training …
imaging field. The DL-based reconstruction methods are usually evaluated on the training …