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State of the art: iterative CT reconstruction techniques
LL Geyer, UJ Schoepf, FG Meinel, JW Nance Jr… - Radiology, 2015 - pubs.rsna.org
Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have
become a clinically viable option in computed tomographic (CT) imaging. Substantial …
become a clinically viable option in computed tomographic (CT) imaging. Substantial …
Iterative reconstruction methods in X-ray CT
M Beister, D Kolditz, WA Kalender - Physica medica, 2012 - Elsevier
Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray
computed tomography (CT). They were successfully used in the early years of CT, but given …
computed tomography (CT). They were successfully used in the early years of CT, but given …
Image reconstruction by domain-transform manifold learning
Image reconstruction is essential for imaging applications across the physical and life
sciences, including optical and radar systems, magnetic resonance imaging, X-ray …
sciences, including optical and radar systems, magnetic resonance imaging, X-ray …
Plug-and-play priors for model based reconstruction
SV Venkatakrishnan, CA Bouman… - 2013 IEEE global …, 2013 - ieeexplore.ieee.org
Model-based reconstruction is a powerful framework for solving a variety of inverse
problems in imaging. In recent years, enormous progress has been made in the problem of …
problems in imaging. In recent years, enormous progress has been made in the problem of …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
[PDF][PDF] CT artifacts: causes and reduction techniques
FE Boas, D Fleischmann - Imaging Med, 2012 - Citeseer
Artifacts are commonly encountered in clinical computed tomography (CT), and may
obscure or simulate pathology. There are many different types of CT artifacts, including …
obscure or simulate pathology. There are many different types of CT artifacts, including …
Clinical impact of deep learning reconstruction in MRI
Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning
reconstruction (DLR) has recently emerged as a technology used in the image …
reconstruction (DLR) has recently emerged as a technology used in the image …
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 - ajronline.org
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 …
Electron tomography: a three‐dimensional analytic tool for hard and soft materials research
Three‐dimensional (3D) structural analysis is essential to understand the relationship
between the structure and function of an object. Many analytical techniques, such as X‐ray …
between the structure and function of an object. Many analytical techniques, such as X‐ray …
Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction
Metal additive manufacturing (AM) offers flexibility and cost-effectiveness for printing
complex parts but is limited to few alloys. Qualifying new alloys requires process parameter …
complex parts but is limited to few alloys. Qualifying new alloys requires process parameter …