AIMC Topic: Tomography, X-Ray Computed

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Segmentation of human aorta using 3D nnU-net-oriented deep learning.

The Review of scientific instruments
Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue f...

Technological advances in body CT: a primer for beginners.

European review for medical and pharmacological sciences
Many technological advances have entered the clinical routine of Computed Tomography (CT) imaging. The new CT scanners have specific solutions in gantry design to bear the mechanical solicitations. The X-ray tubes have been improved for faster acquis...

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

Medicine
BACKGROUND: To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspect...

Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease.

Medicine
BACKGROUND: Several recent studies have reported that deep learning reconstruction "TrueFidelity" (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in p...

Edge feature extraction-based dual CNN for LDCT denoising.

Journal of the Optical Society of America. A, Optics, image science, and vision
In low-dose computed tomography (LDCT) denoising tasks, it is often difficult to balance edge/detail preservation and noise/artifact reduction. To solve this problem, we propose a dual convolutional neural network (CNN) based on edge feature extracti...

Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy.

Seminars in radiation oncology
Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Th...

Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography.

American journal of respiratory and critical care medicine
Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA [Systematic Objective Fib...

Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.

Mathematical biosciences and engineering : MBE
Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surroun...