Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease

2018/09/08 by Andreas Hauptmann, Simon Arridge, Felix Lucka +2 · 6 citations
Medicine · #Advanced MRI Techniques and Applications #Cardiac Imaging and Diagnostics #Cardiovascular Function and Risk Factors

paper · pdf · doi:10.1002/mrm.27480

openalex publication_date 2018/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real-time data in patients with congenital heart disease (CHD). METHODS: A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real-time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real-time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. RESULTS: It was feasible to train a CNN to remove artifact from highly undersampled radial real-time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. CONCLUSION: This article has demonstrated the potential for the use of a CNN for reconstruction of real-time radial data within the clinical setting. Clinical measures of ventricular volumes using real-time data with CNN reconstruction are not statistically significantly different from gold-standard, cardiac-gated, breath-hold techniques.

Citations

• Adam: A Method for Stochastic Optimization
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
• TensorFlow: A system for large-scale machine learning

Cited by

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• Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
• Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
• Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
• OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging
• Generalizable, sequence‐invariant deep learning image reconstruction for subspace‐constrained quantitative MRI

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