AI Medical Compendium Topic

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Angiography

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[Effect of Automatic Extraction Accuracy by Different Image Reconstruction Methods Using a Three-dimensional Image Analysis System for Pulmonary Segmentectomy Preoperative CT Angiography].

Nihon Hoshasen Gijutsu Gakkai zasshi
This study aimed to determine the optimal image reconstruction method for preoperative computed tomography (CT) angiography for pulmonary segmentectomy. This study enrolled 20 patients who underwent contrast-enhanced CT examination for pulmonary segm...

Angular super-resolution in X-ray projection radiography using deep neural network: Implementation on rotational angiography.

Biomedical journal
BACKGROUND: Rotational angiography acquires radiographs at multiple projection angles to demonstrate superimposed vasculature. However, this comes at the expense of the inherent risk of increased ionizing radiation. In this paper, building upon a suc...

Spatiotemporal absorption fluctuation imaging based on U-Net.

Journal of biomedical optics
SIGNIFICANCE: Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously.

Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.

Medical physics
PURPOSE: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful mo...

Automated detection of pulmonary embolism from CT-angiograms using deep learning.

BMC medical imaging
BACKGROUND: The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data.

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.

Scientific reports
Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a ...

[Research on inversion method of intravascular blood flow velocity based on convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Blood velocity inversion based on magnetoelectric effect is helpful for the development of daily monitoring of vascular stenosis, but the accuracy of blood velocity inversion and imaging resolution still need to be improved. Therefore, a convolutiona...

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

Translational vision science & technology
PURPOSE: Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fu...