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Computed Tomography (CT)

The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.

( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )

Papers

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Showing 701–725 of 1207 papers

TitleStatusHype
Temporal and volumetric denoising via quantile sparse image prior—0
Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting—0
Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography—0
Texture-Aware StarGAN for CT data harmonisation—0
Texture Matching GAN for CT Image Enhancement—0
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons—0
A Rigid Registration Method in TEVAR—0
The establishment of static digital humans and the integration with spinal models—0
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study—0
Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction—0
TomoGRAF: A Robust and Generalizable Reconstruction Network for Single-View Computed Tomography—0
Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine—0
TomoSLAM: factor graph optimization for rotation angle refinement in microtomography—0
Topic Modeling for Classification of Clinical Reports—0
Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images—0
Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects—0
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels—0
Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection—0
Towards Patient-Specific Surgical Planning for Bicuspid Aortic Valve Repair: Fully Automated Segmentation of the Aortic Valve in 4D CT—0
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning—0
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines—0
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT—0
Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion—0
Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection—0
Transfer learning for multi-center classification of chronic obstructive pulmonary disease—0
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