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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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12 results for “Angiography”

Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolutioneess.IV

New unsupervised deep learning method improves temporal resolution in tMRA.

problem Limited temporal resolution in tMRA due to fixed view-sharing scheme.

method Optimal transport driven cycle-consistent generative adversarial network (cycleGAN) without fully sampled k-space reference data.

result Can generate high quality reconstructions at various temporal resolutions.

MR angiography unsupervised learning time-resolved cycleGAN optimal transport

2020-03-29abs ↗pdf ↗

k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiographycs.CV

Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sha…

2018-06-03abs ↗pdf ↗

Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learningeess.IV

Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.

problem Reduced scan quality in 6x6-mm OCTA angiograms due to undersampling.

method Deep-learning-based high-resolution angiogram reconstruction network (HARNet) trained on 3x3-mm and 6x6-mm angiogram data.

result Reconstructed 6x6-mm angiograms have lower noise and better vascular connectivity.

OCTA Angiography Deep learning Noise reduction Vascular connectivity

2020-04-19abs ↗pdf ↗

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Modelphysics.med-ph

Study improves CAD diagnosis accuracy by selecting significant features.

problem Improving accuracy of CAD diagnosis through feature selection.

method Integrated machine learning approach using random trees (RTs), C5.0, SVM, and CHAID.

result Random trees model outperforms other models in CAD diagnosis.

CAD diagnosis feature selection random trees machine learning medical imaging

2020-01-16abs ↗pdf ↗

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image serieseess.IV

Neural network improves aneurysm classification accuracy in MRI images.

problem Classifying aneurysm status in MRI images with varying radiologist expertise.

method Constructed neural network trained on radiologists' annotations, compared to a control model.

result Proposed model had a higher area under the curve (0.845 vs. 0.793).

neural networks MRI neuroradiology classification accuracy deep learning

2020-02-03abs ↗pdf ↗

Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiographycs.CV

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning tech…

2018-06-26abs ↗pdf ↗

Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinementcs.CV

Automated labeling of intracranial arteries improves accuracy and efficiency.

problem Challenges in accurately labeling intracranial arteries due to variations and limited datasets.

method Graph Neural Network (GNN) combined with hierarchical refinement for improved accuracy.

result Achieved 97.5% node labeling accuracy on a testing set of 105 scans.

Graph Neural Network Intracranial Arteries Hierarchical Refinement Magnetic Resonance Angiography Anatomical Labeling

2020-07-11abs ↗pdf ↗

Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomicseess.IV

Deep learning and radiomics methods assess coronary artery plaque from CT scans.

problem Improving patient management and clinical outcomes by assessing coronary artery plaque.

method Three machine learning approaches: radiomics, deep learning, and fusion of both.

result Methods achieve AUC scores of 0.84-0.88, comparable to FFR measurements.

coronary artery plaque assessment deep learning radiomics machine learning

2019-12-12abs ↗pdf ↗

Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scanseess.IV

Deep learning improves plaque prediction for coronary artery health.

problem Predicting coronary artery plaque health from CT scans.

method 3D RCNN, 2D multi-view ensemble, 2.5D approach.

result Improved prediction accuracy for revascularization decisions.

deep learning coronary arteries plaque characterization CT angiography fractional flow reserve

2019-12-13abs ↗pdf ↗

Generating retinal flow maps from structural optical coherence tomography with artificial intelligencecs.CV

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the…

2018-02-24abs ↗pdf ↗

Neural Hypernetwork Approach for Pulmonary Embolism diagnosisphysics.med-ph

This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing …

2014-09-19abs ↗pdf ↗

Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Dataeess.IV

Deep learning for TOF-MRA without matched training data.

problem High-quality reconstruction from undersampled TOF-MRA.

method Two-stage unsupervised deep learning approach using cycleGAN theory.

result Exceeds performance of state-of-the-art CS-based methods.

deep learning TOF-MRA unsupervised cycleGAN compressed sensing

2020-08-04abs ↗pdf ↗