AIMC Topic: Magnetic Resonance Imaging, Cine
Clear Filters Showing 111 to 120 of 175 articles
Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy.
Physics in medicine and biology
Aug 7, 2020
To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dos...
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.
Magnetic resonance in medicine
Jul 22, 2020
PURPOSE: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.
Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.
The international journal of cardiovascular imaging
Jul 16, 2020
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 year...
Humans
Aged
Aged, 80 and over
Young Adult
Adult
Retrospective Studies
Female
Reproducibility of Results
Feasibility Studies
Diagnosis, Computer-Assisted
Heart Ventricles
Deep Learning
Image Interpretation, Computer-Assisted
Heart Diseases
Male
Adolescent
Ventricular Function, Left
Predictive Value of Tests
Automation
Middle Aged
Magnetic Resonance Imaging, Cine
Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.
Medical physics
Jul 6, 2020
PURPOSE: Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and...
Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.
Radiology
Apr 14, 2020
Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To ex...
Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 10, 2020
Cardiovascular diseases can be effectively prevented from worsening through early diagnosis. To this end, various methods have been proposed to detect the disease source by analyzing cardiac magnetic resonance images (MRI), wherein left ventricular s...
A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 12, 2020
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventric...
Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis.
Heart (British Cardiac Society)
Mar 11, 2020
OBJECTIVE: To assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).
Adult
Male
Middle Aged
Prognosis
Germany
Predictive Value of Tests
Risk Assessment
Humans
Female
Time Factors
Tetralogy of Fallot
Diagnosis, Computer-Assisted
Registries
Risk Factors
Prospective Studies
Child
Magnetic Resonance Imaging, Cine
Deep Learning
Young Adult
Feasibility Studies
Electrocardiography
Adolescent
Image Interpretation, Computer-Assisted
Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Biomedical physics & engineering express
Feb 18, 2020
BACKGROUND: Magnetic resonance cine imaging is the accepted standard for cardiac functional assessment. Left ventricular (LV) segmentation plays a key role in volumetric functional quantification of the heart. Conventional manual analysis is time-con...
Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 6, 2020
Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variabilit...
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