[HTML][HTML] A residual dense network assisted sparse view reconstruction for breast computed tomography
To develop and investigate a deep learning approach that uses sparse-view acquisition in
dedicated breast computed tomography for radiation dose reduction, we propose a …
dedicated breast computed tomography for radiation dose reduction, we propose a …
Deep learning reconstruction for 9-view dual energy CT baggage scanner
For homeland and transportation security applications, 2D X-ray explosive detection system
(EDS) have been widely used, but they have limitations in recognizing 3D shape of the …
(EDS) have been widely used, but they have limitations in recognizing 3D shape of the …
Sparse-view X-ray CT reconstruction with Gamma regularization
By providing fast scanning with low radiation doses, sparse-view (or sparse-projection)
reconstruction has attracted much research attention in X-ray computerized tomography …
reconstruction has attracted much research attention in X-ray computerized tomography …
An end-to-end deep network for reconstructing CT images directly from sparse sinograms
Recently, deep-learning based methods have been widely used for computed tomography
(CT) reconstruction. However, most of these methods need extra steps to convert the …
(CT) reconstruction. However, most of these methods need extra steps to convert the …
Modularized data‐driven reconstruction framework for nonideal focal spot effect elimination in computed tomography
Purpose High‐performance computed tomography (CT) plays a vital role in clinical decision‐
making. However, the performance of CT imaging is adversely affected by the nonideal focal …
making. However, the performance of CT imaging is adversely affected by the nonideal focal …
Mepnet: A model-driven equivariant proximal network for joint sparse-view reconstruction and metal artifact reduction in ct images
Sparse-view computed tomography (CT) has been adopted as an important technique for
speeding up data acquisition and decreasing radiation dose. However, due to the lack of …
speeding up data acquisition and decreasing radiation dose. However, due to the lack of …
Piner: Prior-informed implicit neural representation learning for test-time adaptation in sparse-view ct reconstruction
Recently, deep learning has been introduced to solve important medical image
reconstruction problems such as sparse-view CT reconstruction. However, the developed …
reconstruction problems such as sparse-view CT reconstruction. However, the developed …
Deep learning based spectral CT imaging
Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
Comparison of deep learning approaches to low dose CT using low intensity and sparse view data
T Humphries, D Si, S Coulter… - Medical imaging 2019 …, 2019 - spiedigitallibrary.org
Recently there has been considerable interest in using deep learning to improve the quality
of low dose CT (LDCT) images. LDCT may be achieved by reducing the beam intensity, or …
of low dose CT (LDCT) images. LDCT may be achieved by reducing the beam intensity, or …
Low-dose CT with deep learning regularization via proximal forward–backward splitting
Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This
work develops new image reconstruction methods with deep learning (DL) regularization for …
work develops new image reconstruction methods with deep learning (DL) regularization for …