Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data

Abstract

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. We train the network on $2D$ spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two $2D$ and a $3D$ Deep Learning-based post processing methods and to three iterative reconstruction methods for dynamic cardiac MRI. Our method outperforms the $2D$ spatially trained U-net and the $2D$ spatio-temporal U-net. Compared to the $3D$ spatio-temporal U-net, our method delivers comparable results, but with shorter training times and less training data. Compared to the Compressed Sensing-based methods $kt$-FOCUSS and a total variation regularised reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to an iterative reconstruction method based on adaptive regularization with Dictionary Learning and total variation, while only requiring a small fraction of the computational time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial $2D$ U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required.

To be published in IEEE Transactions on Medical Imaging

References in corpus (9)

• A Survey on Deep Learning in Medical Image Analysis
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Image reconstruction by domain transform manifold learning
• Learned Primal-dual Reconstruction
• A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
• Solving ill-posed inverse problems using iterative deep neural networks
• A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
• Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
• Rotation equivariant vector field networks

Cited by in corpus (6)

• Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
• An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
• Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks
• HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination
• Self-supervised feature learning for cardiac Cine MR image reconstruction
• Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance