1 citations · 1 across the 3 of their papers we have counts for
8 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…
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…
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…