A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
MJ Willemink, PB Noël - European radiology, 2019 - Springer
The first CT scanners in the early 1970s already used iterative reconstruction algorithms;
however, lack of computational power prevented their clinical use. In fact, it took until 2009 …
however, lack of computational power prevented their clinical use. In fact, it took until 2009 …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
CLEAR: comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging
X-ray computed tomography (CT) is of great clinical significance in medical practice
because it can provide anatomical information about the human body without invasion …
because it can provide anatomical information about the human body without invasion …
DOLCE: A model-based probabilistic diffusion framework for limited-angle ct reconstruction
Abstract Limited-Angle Computed Tomography (LACT) is a non-destructive 3D imaging
technique used in a variety of applications ranging from security to medicine. The limited …
technique used in a variety of applications ranging from security to medicine. The limited …
A survey on deep learning in medical image reconstruction
E Ahishakiye, M Bastiaan Van Gijzen… - Intelligent …, 2021 - mednexus.org
Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …
at minimal cost and risk to the patients. Deep learning and its applications in medical …
Applications and limitations of machine learning in radiation oncology
Machine learning approaches to problem-solving are growing rapidly within healthcare, and
radiation oncology is no exception. With the burgeoning interest in machine learning comes …
radiation oncology is no exception. With the burgeoning interest in machine learning comes …
Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions
Computed tomography (CT) is widely used in medical diagnosis and non-destructive
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
Coil: Coordinate-based internal learning for tomographic imaging
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL)
methodology for continuous representation of measurements. Unlike traditional DL methods …
methodology for continuous representation of measurements. Unlike traditional DL methods …
DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image
using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation …
using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation …