An Unsupervised Deep Learning Method for Parallel Cardiac MRI via Time-Interleaved Sampling

Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn the mapping from undersampled k-space to fully sampled images. Although these deep learning methods can improve reconstruction quality without complex parameter selection or a lengthy reconstruction time compared with iterative methods, the following issues still need to be addressed: 1) all of these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult for cardiac MRI; 2) All of these methods are only applicable for single-channel images without exploring coil correlation. In this paper, we propose an unsupervised deep learning method for parallel cardiac MRI via a time-interleaved sampling strategy. Specifically, a time-interleaved acquisition scheme is developed to build a set of fully encoded reference data by directly merging the k-space data of adjacent time frames. Then these fully encoded data can be used to train a parallel network for reconstructing images of each coil separately. Finally, the images from each coil are combined together via a CNN to implicitly explore the correlations between coils. The comparisons with classic k-t FOCUSS, k-t SLR and L+S methods on in vivo datasets show that our method can achieve improved reconstruction results in an extremely short amount of time.

[1]  Daniel Rueckert,et al.  Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[2]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[3]  A. J. Jerri.  The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[4]  Daniel K Sodickson,et al.  Assessment of the generalization of learned image reconstruction and the potential for transfer learning , 2019, Magnetic resonance in medicine.

[5]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[6]  Jian Sun,et al.  Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.

[7]  Dong Liang,et al.  k‐t ISD: Dynamic cardiac MR imaging using compressed sensing with iterative support detection , 2012, Magnetic resonance in medicine.

[8]  Jong Chul Ye,et al.  Improved k–t BLAST and k–t SENSE using FOCUSS , 2007, Physics in medicine and biology.

[9]  Daniel K Sodickson,et al.  Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components , 2015, Magnetic resonance in medicine.

[10]  Dong Liang,et al.  Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks , 2020, IEEE Signal Processing Magazine.

[11]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[12]  Mohammed Ouanan,et al.  Content-based color image retrieval based on the 2-D histogram and statistical moments , 2014, 2014 Second World Conference on Complex Systems (WCCS).

[13]  Dong Liang,et al.  DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training , 2018, NMR in biomedicine.

[14]  Bruce R. Rosen,et al.  Image reconstruction by domain-transform manifold learning , 2017, Nature.

[15]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[16]  Zhi-Pei Liang,et al.  SPATIOTEMPORAL IMAGINGWITH PARTIALLY SEPARABLE FUNCTIONS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  Mathews Jacob,et al.  MoDL: Model-Based Deep Learning Architecture for Inverse Problems , 2017, IEEE Transactions on Medical Imaging.

[18]  John W. Paisley,et al.  A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI , 2018, ECCV.

[19]  HyunWook Park,et al.  A parallel MR imaging method using multilayer perceptron , 2017, Medical physics.

[20]  Leslie Ying,et al.  Compressed Sensing Dynamic Cardiac Cine MRI Using Learned Spatiotemporal Dictionary , 2014, IEEE Transactions on Biomedical Engineering.

[21]  Steen Moeller,et al.  Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues , 2020, IEEE Signal Processing Magazine.

[22]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[23]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[24]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[25]  Thomas Pock,et al.  Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.

[26]  Leslie Ying,et al.  A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI , 2017, IEEE Transactions on Medical Imaging.

[27]  Won-Ki Jeong,et al.  Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.

[28]  Leslie Ying,et al.  Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery , 2018, IEEE Transactions on Medical Imaging.

[29]  John W. Paisley,et al.  Compressed Sensing MRI Using a Recursive Dilated Network , 2018, AAAI.

[30]  Jong Chul Ye,et al.  Deep learning with domain adaptation for accelerated projection‐reconstruction MR , 2018, Magnetic resonance in medicine.

[31]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[32]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Mathews Jacob,et al.  Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR , 2011, IEEE Transactions on Medical Imaging.

[35]  Daniel Rueckert,et al.  A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[36]  J. Finn,et al.  Cardiac MR imaging: state of the technology. , 2006, Radiology.

[37]  Guang Yang,et al.  DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.

[38]  Taeseong Kim,et al.  KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images , 2018, Magnetic resonance in medicine.

[39]  Xin Liu,et al.  Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning , 2019 .

[40]  Zhi-Pei Liang,et al.  Spatiotemporal Imaging with Partially Separable Functions , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.

[41]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[42]  D. O. Walsh,et al.  Adaptive reconstruction of phased array MR imagery , 2000, Magnetic resonance in medicine.

[43]  Dong Liang,et al.  Model Learning: Primal Dual Networks for Fast MR imaging , 2019, MICCAI.

[44]  Dong Liang,et al.  Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning , 2019, ArXiv.

[45]  Peter Kellman,et al.  Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA) , 2005, Magnetic resonance in medicine.