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 …
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 …
Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning
In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand
for the reconstruction of high-quality images from a limited number of radio-frequency (RF) …
for the reconstruction of high-quality images from a limited number of radio-frequency (RF) …
Efficient and accurate inversion of multiple scattering with deep learning
Image reconstruction under multiple light scattering is crucial in a number of applications
such as diffraction tomography. The reconstruction problem is often formulated as a …
such as diffraction tomography. The reconstruction problem is often formulated as a …
Deep back projection for sparse-view CT reconstruction
DH Ye, GT Buzzard, M Ruby… - 2018 IEEE Global …, 2018 - ieeexplore.ieee.org
Filtered back projection (FBP) is a classical method for image reconstruction from sinogram
CT data. FBP is computationally efficient but produces lower quality reconstructions than …
CT data. FBP is computationally efficient but produces lower quality reconstructions than …
Learning bone suppression from dual energy chest X-rays using adversarial networks
DY Oh, ID Yun - arXiv preprint arXiv:1811.02628, 2018 - arxiv.org
Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve
pathologies classification. These bones can interfere with a broad range of diagnostic tasks …
pathologies classification. These bones can interfere with a broad range of diagnostic tasks …
A mathematical framework for deep learning in elastic source imaging
An inverse elastic source problem with sparse measurements is our concern. A generic
mathematical framework is proposed which extends a low-dimensional manifold …
mathematical framework is proposed which extends a low-dimensional manifold …
Can deep learning outperform modern commercial CT image reconstruction methods?
Commercial iterative reconstruction techniques on modern CT scanners target radiation
dose reduction but there are lingering concerns over their impact on image appearance and …
dose reduction but there are lingering concerns over their impact on image appearance and …