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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 276–300 of 1207 papers

TitleStatusHype
CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion ApproachCode0
Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation—0
Automated neuroradiological support systems for multiple cerebrovascular disease markers -- A systematic review and meta-analysis—0
Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT—0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
On a Hidden Property in Computational Imaging—0
Regression Conformal Prediction under BiasCode0
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction—0
Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images—0
Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal AblationCode0
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons—0
Medical Image Segmentation with SAM-generated Annotations—0
High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models—0
MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning—0
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings—0
AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble—0
Automated detection of underdiagnosed medical conditions via opportunistic imaging—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Integrating Deep Unfolding with Direct Diffusion Bridges for Computed Tomography Reconstruction—0
Autoregressive Sequence Modeling for 3D Medical Image Representation—0
MAISI: Medical AI for Synthetic ImagingCode0
AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution—0
Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection—0
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images—0
Separation of Body and Background in Radiological Images. A Practical Python CodeCode0
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