Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Deep learning image reconstruction for CT: technical principles and clinical prospects
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …
[HTML][HTML] 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 is a new frontier of machine learning
Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …
generated overwhelming research interest and attracted unprecedented public attention. As …
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 …
LEARN: Learned experts' assessment-based reconstruction network for sparse-data CT
Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for …
sparsely collected data or under-sampled measurements, which are practically important for …
PET image reconstruction using deep image prior
Recently, deep neural networks have been widely and successfully applied in computer
vision tasks and have attracted growing interest in medical imaging. One barrier for the …
vision tasks and have attracted growing interest in medical imaging. One barrier for the …
CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising
Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
Deep learning reconstruction at CT: phantom study of the image characteristics
Objectives Noise, commonly encountered on computed tomography (CT) images, can
impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning …
impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning …
Deep convolutional framelet denosing for low-dose CT via wavelet residual network
Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography
(CT) are computationally expensive. To address this problem, we recently proposed a deep …
(CT) are computationally expensive. To address this problem, we recently proposed a deep …