Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

  title={Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data},
  author={In{\^e}s Machado and Esther Puyol-Ant{\'o}n and Kerstin Hammernik and Gast{\~a}o Cruz and Devran Ugurlu and Bram Ruijsink and Miguel Castelo‐Branco and Alistair A. Young and Claudia Prieto and Julia Anne Schnabel and Andrew P. King},
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI acquisition. However, the undersampling factor is commonly fixed to conservative values before acquisition to ensure diagnostic image quality… 

Figures and Tables from this paper


From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction
An overview of the recent developments in the area of artificial intelligence for CMR image reconstruction is provided, focusing on approaches that exploit neural networks as implicit or explicit priors for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
A framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process is proposed and it is demonstrated that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches.
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images
It is demonstrated that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.
Reconstruction techniques for cardiac cine MRI
A general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies is provided.
Real-time Prediction of Segmentation Quality
This work states that being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis.
UK Biobank’s cardiovascular magnetic resonance protocol
The CMR protocol applied in UK Biobank’s pilot phase is described, which will be extended into the main phase with three centres using the same equipment and protocols.
Low-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI
  • J. Haldar
  • Mathematics, Computer Science
    IEEE Transactions on Medical Imaging
  • 2014
A novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS) and enables calibrationless use of phase constraints, while calibration-based support and phase constraints are commonly used in existing methods.
Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort
This study is the largest to provide CMR specific reference ranges for left ventricular, right ventricular), left atrial and right atrial structure and function derived from truly healthy Caucasian adults aged 45–74.
Learning a variational network for reconstruction of accelerated MRI data
To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deep