251 citations · 338 across the 13 of their papers we have counts for
15 papers
Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network
Rebecca R. Baker, Vivek Muthurangu, Marilena Rega +2
23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction a…
Image2Flow: A hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data
Tina Yao, Endrit Pajaziti, Michael Quail +3
Computational fluid dynamics (CFD) can be used for evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and ti…
Investigating the use of publicly available natural videos to learn Dynamic MR image reconstruction
Olivier Jaubert, Michele Pascale, Javier Montalt-Tordera +6
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Materials and Methods: Learn…
Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry
Tina Yao, Nicole St. Clair, Gabriel F. Miller +13
Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-…
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images
Michele Pascale, Vivek Muthurangu, Javier Montalt Tordera +4
Three-dimensional (3D) imaging is popular in medical applications, however, anisotropic 3D volumes with thick, low-spatial-resolution slices are often acquired to reduce scan times…
HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination
Olivier Jaubert, Javier Montalt-Tordera, Daniel Knight +3
PURPOSE: Interactive cardiac magnetic resonance imaging is used for fast scan planning and MR guided interventions. However, the requirement for real-time acquisition and near real…
Automatic Segmentation of the Great Arteries for Computational Hemodynamic Assessment
Javier Montalt-Tordera, Endrit Pajaziti, Rod Jones +7
Background: Computational fluid dynamics (CFD) is increasingly used to assess blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific…
Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Endrit Pajaziti, Javier Montalt-Tordera, Claudio Capelli +4
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outco…
FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
Olivier Jaubert, Javier Montalt-Tordera, James Brown +4
Purpose: Real-time monitoring of cardiac output (CO) requires low latency reconstruction and segmentation of real-time phase contrast MR (PCMR), which has previously been difficult…
Machine Learning aided k-t SENSE for fast reconstruction of highly accelerated PCMR data
Grzegorz Tomasz Kowalik, Javier Montalt-Tordera, Jennifer Steeden +1
Purpose: We implemented the Machine Learning (ML) aided k-t SENSE reconstruction to enable high resolution quantitative real-time phase contrast MR (PCMR). Methods: A residual U-ne…
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Javier Montalt-Tordera, Vivek Muthurangu, Andreas Hauptmann +1
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20…
Rapid Whole-Heart CMR with Single Volume Super-resolution
Jennifer A. Steeden, Michael Quail, Alexander Gotschy +4
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have…
Memory reduced non-Cartesian MRI encoding using the mixed-radix tensor product on CPU and GPU
Jyh-Miin Lin, Grzegorz Kowalik, Jennifer A. Steeden +1
Multi-dimensional non-Cartesian MRI encoding using the precomputed interpolator can encounter the curse of dimensionality, in which the interpolator size exceeds the available memo…
Real-time Assessment of Right and Left Ventricular Volumes and Function in Children Using High Spatiotemporal Resolution Spiral bSSFP with Compressed Sensing
Jennifer A. Steeden, Grzegorz T. Kowalik, Oliver Tann +3
Background: Real-time (RT) assessment of ventricular volumes and function enables data acquisition during free-breathing. However, in children the requirement for high spatiotempor…
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
Andreas Hauptmann, Simon Arridge, Felix Lucka +2
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts ca…