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 …
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 …
[HTML][HTML] A gentle introduction to deep learning in medical image processing
This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …
proceeding from theoretical foundations to applications. We first discuss general reasons for …
Photorealistic style transfer via wavelet transforms
Recent style transfer models have provided promising artistic results. However, given a
photograph as a reference style, existing methods are limited by spatial distortions or …
photograph as a reference style, existing methods are limited by spatial distortions or …
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 …
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 …
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
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 …
Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT
R Singh, SR Digumarthy, VV Muse… - American Journal of …, 2020 - Am Roentgen Ray Soc
OBJECTIVE. The objective of this study was to compare image quality and clinically
significant lesion detection on deep learning reconstruction (DLR) and iterative …
significant lesion detection on deep learning reconstruction (DLR) and iterative …
Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction
Recently, a number of approaches to low-dose computed tomography (CT) have been
developed and deployed in commercialized CT scanners. Tube current reduction is perhaps …
developed and deployed in commercialized CT scanners. Tube current reduction is perhaps …