AIMC Topic: Tomography, X-Ray Computed

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Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study.

Journal of forensic sciences
Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillary investigation and screening tool. One area of clinical CT imaging that has garnered a lot of resea...

A coordinate positioning puncture method under robot-assisted CT-guidance: phantom and animal experiments.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
PURPOSE: To evaluate the accuracy of the robot-assisted computed tomography (CT)-guided coordinate positioning puncture method by phantom and animal experiments.

A new multimodal, image-guided, robot-assisted, interstitial brachytherapy for the treatment of head and neck tumors-A preliminary study.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Interstitial brachytherapy (BT) is becoming an accepted treatment option for head and neck cancer patients for whom surgery poses high risks. Multimodal, image-guided, robotic surgery has the potential to allow precise seed implantation i...

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

Medical physics
PURPOSE: Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is a...

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

Medical physics
PURPOSE: To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

Journal of biomolecular structure & dynamics
Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small...

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

European radiology
OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection.

Medical & biological engineering & computing
This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed ...