251 citations · 308 across the 16 of their papers we have counts for
18 papers
Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T
Julius Åkesson, Iulius Dragonu, Einar Heiberg +6
Purpose: Real-time (RT) bSSFP MRI enables fast free-breathing cardiovascular imaging but requires 10-16 slices for functional assessment, resulting in prolonged scan times. Simulta…
Training deep learning based dynamic MR image reconstruction using synthetic fractals
Anirudh Raman, Olivier Jaubert, Mark Wrobel +8
Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, li…
High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
Mark Wrobel, Michele Pascale, Tina Yao +5
Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine…
MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
Tina Yao, Nicole St. Clair, Madeline Gong +5
We present a deep learning framework with two models for automated segmentation and large-scale flow phenotyping in a registry of single-ventricle patients. MultiFlowSeg simultaneo…
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…
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…