Deep learning with adaptive hyper-parameters for low-dose CT image reconstruction
Low-dose CT (LDCT) imaging is preferred in many applications to reduce the object's
exposure to X-ray radiation. In recent years, one promising approach to image …
exposure to X-ray radiation. In recent years, one promising approach to image …
A model-based unsupervised deep learning method for low-dose CT reconstruction
Low-dose CT (LDCT) is of great significance due to the concern about the potential radiation
risk. With the fast development of deep learning, neural networks have become powerful …
risk. With the fast development of deep learning, neural networks have become powerful …
A deep RNN for CT image reconstruction
J Zhang, H Zuo - Medical Imaging 2020: Physics of Medical …, 2020 - spiedigitallibrary.org
Filtered back projection (FBP) reconstruction is simple and computationally efficient and is
used in many commercial CT (tomography) imaging products. However, higher Poisson …
used in many commercial CT (tomography) imaging products. However, higher Poisson …
Learning deconvolutional deep neural network for high resolution medical image reconstruction
Super resolution reconstruction can be used to recover a high resolution image from a low
resolution image and is particularly beneficial for clinically significant medical images in …
resolution image and is particularly beneficial for clinically significant medical images in …
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 …
Rbp-dip: High-quality ct reconstruction using an untrained neural network with residual back projection and deep image prior
Z Shu, A Entezari - arXiv preprint arXiv:2210.14416, 2022 - arxiv.org
Neural network related methods, due to their unprecedented success in image processing,
have emerged as a new set of tools in CT reconstruction with the potential to change the …
have emerged as a new set of tools in CT reconstruction with the potential to change the …
Semi-supervised learning for low-dose CT image restoration with hierarchical deep generative adversarial network (HD-GAN)
In the absence of duplicate high-dose CT data, it is challenging to restore high-quality
images based on deep learning with only low-dose CT (LDCT) data. When different …
images based on deep learning with only low-dose CT (LDCT) data. When different …
DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction
Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
Deep Transfer Learning for COVID‐19 Detection and Lesion Recognition Using Chest CT Images
S Zhang, GC Yuan - Computational and mathematical methods …, 2022 - Wiley Online Library
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID‐
19) is continuously expanding and has caused several millions of deaths worldwide. Fast …
19) is continuously expanding and has caused several millions of deaths worldwide. Fast …
Learning image from projection: A full-automatic reconstruction (FAR) net for computed tomography
G Ma, Y Zhu, X Zhao - IEEE access, 2020 - ieeexplore.ieee.org
The x-ray computed tomography (CT) is essential for medical diagnosis and industrial
nondestructive testing. The aim of CT is to recover or reconstruct image from projection data …
nondestructive testing. The aim of CT is to recover or reconstruct image from projection data …