[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 …
State of the art in abdominal CT: the limits of iterative reconstruction algorithms
A Mileto, LS Guimaraes, CH McCollough, JG Fletcher… - Radiology, 2019 - pubs.rsna.org
The development and widespread adoption of iterative reconstruction (IR) algorithms for CT
have greatly facilitated the contemporary practice of radiation dose reduction during …
have greatly facilitated the contemporary practice of radiation dose reduction during …
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: 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 …
Domain progressive 3D residual convolution network to improve low-dose CT imaging
The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a
clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly …
clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly …
Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography
Purpose In multiphase coronary CT angiography (CTA), a series of CT images are taken at
different levels of radiation dose during the examination. Although this reduces the total …
different levels of radiation dose during the examination. Although this reduces the total …
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 …
Enhanced CNN for image denoising
Owing to the flexible architectures of deep convolutional neural networks (CNNs) are
successfully used for image denoising. However, they suffer from the following drawbacks:(i) …
successfully used for image denoising. However, they suffer from the following drawbacks:(i) …
A deep learning framework for single-sided sound speed inversion in medical ultrasound
M Feigin, D Freedman… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Objective: Ultrasound elastography is gaining traction as an accessible and useful
diagnostic tool for things such as, cancer detection and differentiation and thyroid disease …
diagnostic tool for things such as, cancer detection and differentiation and thyroid disease …