AIMC Topic: Aorta

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CIS-UNet: Multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to ...

Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment.

Academic radiology
OBJECTIVE: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorit...

A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers.

Magnetic resonance imaging
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening rout...

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Annals of biomedical engineering
Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, wh...

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data int...

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

IEEE journal of biomedical and health informatics
Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to ...

Physics-informed neural networks for parameter estimation in blood flow models.

Computers in biology and medicine
BACKGROUND: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available. This is especially ...

Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

International journal of computer assisted radiology and surgery
PURPOSE: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CH...

Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.

Cardiovascular engineering and technology
PURPOSE: Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algo...

Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks a...