Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction
CM Sandino, P Lai, SS Vasanawala… - Magnetic Resonance …, 2021 - Wiley Online Library
Purpose To propose a novel combined parallel imaging and deep learning‐based
reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI …
reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI …
[HTML][HTML] CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of
cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular …
cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular …
Spatio-temporal deep learning-based undersampling artefact reduction for 2D radial cine MRI with limited training data
In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial
cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D …
cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D …
Complementary time‐frequency domain networks for dynamic parallel MR image reconstruction
Purpose To introduce a novel deep learning‐based approach for fast and high‐quality
dynamic multicoil MR reconstruction by learning a complementary time‐frequency domain …
dynamic multicoil MR reconstruction by learning a complementary time‐frequency domain …
Free-breathing accelerated cardiac MRI using deep learning: validation in children and young adults
EJ Zucker, CM Sandino, A Kino, P Lai, SS Vasanawala - Radiology, 2021 - pubs.rsna.org
Background Obtaining ventricular volumetry and mass is key to most cardiac MRI but
challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and …
challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and …
CMRxRecon: an open cardiac MRI dataset for the competition of accelerated image reconstruction
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for
cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes …
cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes …
Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM
S Biswas, HK Aggarwal, M Jacob - Magnetic resonance in …, 2019 - Wiley Online Library
Purpose To introduce a novel framework to combine deep‐learned priors along with
complementary image regularization penalties to reconstruct free breathing & ungated …
complementary image regularization penalties to reconstruct free breathing & ungated …
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Inspired by recent advances in deep learning, we propose a framework for reconstructing
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
[HTML][HTML] From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction
Cardiac cine magnetic resonance imaging (MRI) reconstruction is challenging due to spatial
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …