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 …
Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems
In this paper, we present a new deep learning framework for 3-D tomographic
reconstruction. To this end, we map filtered back-projection-type algorithms to neural …
reconstruction. To this end, we map filtered back-projection-type algorithms to neural …
Improve angular resolution for sparse-view CT with residual convolutional neural network
K Liang, H Yang, K Kang, Y **ng - Medical Imaging 2018 …, 2018 - spiedigitallibrary.org
Sparse-view CT imaging has been a hot topic in the medical imaging field. By decreasing
the number of views, dose delivered to patients can be significantly reduced. However …
the number of views, dose delivered to patients can be significantly reduced. However …
Simulation-based deep artifact correction with convolutional neural networks for limited angle artifacts
Non-conventional scan trajectories for interventional three-dimensional imaging promise
low-dose interventions and a better radiation protection to the personnel. Circular …
low-dose interventions and a better radiation protection to the personnel. Circular …
Deep neural networks for non-linear model-based ultrasound reconstruction
Ultrasound reflection tomography is widely used to image large complex specimens that are
only accessible from a single side, such as well systems and nuclear power plant …
only accessible from a single side, such as well systems and nuclear power plant …
Variational deep learning for low-dose computed tomography
In this work, we propose a learning-based variational network (VN) approach for
reconstruction of low-dose 3D computed tomography data. We focus on two methods to …
reconstruction of low-dose 3D computed tomography data. We focus on two methods to …
Apparatus and method for dual-energy computed tomography (ct) image reconstruction using sparse kvp-switching and deep learning
J Zhou, Y Liu, Z Yu - US Patent 10,945,695, 2021 - Google Patents
(57) ABSTRACT A deep learning (DL) network reduces artifacts in computed tomography
(CT) images based on complementary sparse view projection data generated from a sparse …
(CT) images based on complementary sparse view projection data generated from a sparse …
[책][B] Consistency Conditions, Compressed Sensing and Machine Learning for Limited Angle Tomography
Y Huang - 2020 - search.proquest.com
Cone-beam computed tomography (CBCT) is a widely used imaging technology for medical
diagnosis and interventions nowadays. Compared with conventional 2-D X-ray images, 3-D …
diagnosis and interventions nowadays. Compared with conventional 2-D X-ray images, 3-D …
Non-uniformity Correction for Photon-counting Detectors Using Neural Network
Non-uniformity correction is a big challenge for practical use of photon-counting detectors. If
not corrected, this inhomogeneity sensitivity among detector elements will lead to strip …
not corrected, this inhomogeneity sensitivity among detector elements will lead to strip …
[PDF][PDF] Dynamic cardiac chamber imaging in c-arm computed tomography
O Taubmann - 2018 - researchgate.net
Kardiovaskuläre Erkrankungen, dh Funktionsstörungen des Herz-Kreislauf-Systems,
gehören zu den Hauptursachen für Mortalität in Industrieländern. Viele dieser Störungen …
gehören zu den Hauptursachen für Mortalität in Industrieländern. Viele dieser Störungen …