AIMC Topic: Tomography, X-Ray Computed

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MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and n...

A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images.

Computer methods and programs in biomedicine
This paper proposes an encoder-decoder architecture for kidney segmentation. A hyperparameter optimization process is implemented, including the development of a model architecture, selecting a windowing method and a loss function, and data augmentat...

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.

European radiology
OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.

Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography.

European radiology
OBJECTIVES: Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaqu...

An improved CNN-based architecture for automatic lung nodule classification.

Medical & biological engineering & computing
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening throu...

Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.

Computational intelligence and neuroscience
PURPOSE: The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow ...

Incidentalomas in chest CT.

The British journal of radiology
Advances in imaging technology have dramatically increased the resolution of CT and improved detection of disease; these advances also have led to an increase in incidentalomas or incidental findings that often do not represent significant disease. I...

Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction.

European journal of radiology
PURPOSE: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantifi...

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A ...