30 citations · 51 across the 6 of their papers we have counts for
7 papers
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…
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…