AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Blood Flow Velocity

Showing 21 to 30 of 68 articles

Clear Filters

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

European radiology
OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to ...

Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Accurate quantification of cardiac valve regurgitation jets is fundamental for guiding treatment. Cardiac ultrasound is the preferred diagnostic tool, but current methods for measuring the regurgitant volume (RVol) are limited by low accuracy and hig...

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.

Magnetic resonance imaging
BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure it...

Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

The international journal of cardiovascular imaging
Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can ...

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

Magma (New York, N.Y.)
OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is requi...

Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.

The international journal of cardiovascular imaging
PURPOSE: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow.

Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Proceedings of the National Academy of Sciences of the United States of America
Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocit...

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

Journal of biomedical optics
SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static...

Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure.

Medical image analysis
The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field ma...