Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling

To accelerate T2 mapping with highly sparse sampling by integrating deep learning image priors with low‐rank and sparse modeling.

[1]  Peter Gibbs,et al.  Comparison of quantitative T2 mapping and diffusion‐weighted imaging in the normal and pathologic prostate , 2001, Magnetic resonance in medicine.

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

[3]  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.

[4]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Gary H. Glover,et al.  Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging , 2003, IEEE Transactions on Medical Imaging.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  S. Meindl,et al.  Brain tumors: detection and typing by use of CPMG sequences and in vivo T2 measurements. , 1985, Radiology.

[8]  Ronald J. Killiany,et al.  Whole brain quantitative T2 MRI across multiple scanners with dual echo FSE: Applications to AD, MCI, and normal aging , 2010, NeuroImage.

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

[10]  Zhi-Pei Liang,et al.  Accelerated MR parameter mapping with low‐rank and sparsity constraints , 2015, Magnetic resonance in medicine.

[11]  Rajesh Kumar,et al.  Development of T2-relaxation values in regional brain sites during adolescence. , 2011, Magnetic resonance imaging.

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

[13]  J. Duerk,et al.  Magnetic Resonance Fingerprinting , 2013, Nature.

[14]  Danilo Comminiello,et al.  A Multimodal Deep Network for the Reconstruction of T2W MR Images , 2019, IIH-MSP.

[15]  Shaohua Kevin Zhou,et al.  Unsupervised Cross-Modal Synthesis of Subject-Specific Scans , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  R. Damadian.  Tumor Detection by Nuclear Magnetic Resonance , 1971, Science.

[17]  Zhi-Pei Liang,et al.  Anatomically constrained reconstruction from noisy data , 2008, Magnetic resonance in medicine.

[18]  Lei Yuan,et al.  Parallel imaging: some signal processing issues and solutions , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[19]  Joaquim Mullol,et al.  In vivo magnetic resonance imaging characterization of bilateral structural changes in experimental Parkinson’s disease: a T2 relaxometry study combined with longitudinal diffusion tensor imaging and manganese‐enhanced magnetic resonance imaging in the 6‐hydroxydopamine rat model , 2011, The European journal of neuroscience.

[20]  Kim Mouridsen,et al.  Elevated T2-values in MRI of stroke patients shortly after symptom onset do not predict irreversible tissue infarction. , 2012, Brain : a journal of neurology.

[21]  O. Simonetti,et al.  T2 quantification for improved detection of myocardial edema , 2009, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[22]  Mariya Doneva,et al.  Compressed sensing reconstruction for magnetic resonance parameter mapping , 2010, Magnetic resonance in medicine.

[23]  Lawrence L. Wald,et al.  Maximum likelihood reconstruction for magnetic resonance fingerprinting , 2016, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[24]  G. Barker,et al.  Variations in T1 and T2 relaxation times of normal appearing white matter and lesions in multiple sclerosis , 2000, Journal of the Neurological Sciences.

[25]  Bin Yang,et al.  MedGAN: Medical Image Translation using GANs , 2018, Comput. Medical Imaging Graph..

[26]  Z P Liang,et al.  A generalized series approach to MR spectroscopic imaging. , 1991, IEEE transactions on medical imaging.

[27]  Francesco Renna,et al.  On instabilities of deep learning in image reconstruction and the potential costs of AI , 2019, Proceedings of the National Academy of Sciences.

[28]  J. Pauly,et al.  Accelerating parameter mapping with a locally low rank constraint , 2015, Magnetic resonance in medicine.

[29]  Xavier Golay,et al.  Routine clinical brain MRI sequences for use at 3.0 Tesla , 2005, Journal of magnetic resonance imaging : JMRI.

[30]  Yong Chen,et al.  Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction , 2019, IEEE Transactions on Biomedical Engineering.

[31]  Kim Mouridsen,et al.  Quantitative t2 values predict time from symptom onset in acute stroke patients. , 2009, Stroke.

[32]  Kawin Setsompop,et al.  Echo planar time‐resolved imaging with subspace reconstruction and optimized spatiotemporal encoding , 2019, Magnetic resonance in medicine.

[33]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

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

[35]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[36]  Kawin Setsompop,et al.  Echo planar time‐resolved imaging (EPTI) , 2019, Magnetic resonance in medicine.

[37]  Ralf Deichmann,et al.  Quantitative in vivo T2 mapping using fast spin echo techniques – A linear correction procedure , 2017, NeuroImage.

[38]  T. Peters,et al.  High‐resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2 , 2005, Magnetic resonance in medicine.

[39]  Paaladinesh Thavendiranathan,et al.  Improved Detection of Myocardial Involvement in Acute Inflammatory Cardiomyopathies Using T2 Mapping , 2012, Circulation. Cardiovascular imaging.

[40]  Vikas Gulani,et al.  Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage , 2018, Magnetic resonance in medicine.

[41]  Justin P. Haldar,et al.  Spatiotemporal imaging with partially separable functions: A matrix recovery approach , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[42]  Justin P. Haldar,et al.  Image Reconstruction From Highly Undersampled $( {\bf k}, {t})$-Space Data With Joint Partial Separability and Sparsity Constraints , 2012, IEEE Transactions on Medical Imaging.

[43]  Nicole Seiberlich,et al.  Parallel MR imaging , 2012, Journal of magnetic resonance imaging : JMRI.

[44]  Zhi-Pei Liang,et al.  Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors , 2016, IEEE Transactions on Medical Imaging.

[45]  Subha V Raman,et al.  Improved detection of cardiac sarcoidosis using magnetic resonance with myocardial T2 mapping. , 2014, American journal of respiratory and critical care medicine.

[46]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[47]  Yrjö Hiltunen,et al.  Estimation of the Onset Time of Cerebral Ischemia Using T1&rgr; and T2 MRI in Rats , 2010, Stroke.

[48]  A. Alexander,et al.  Accelerating MR parameter mapping using sparsity‐promoting regularization in parametric dimension , 2013, Magnetic resonance in medicine.

[49]  Li Feng,et al.  MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping , 2019, Magnetic resonance in medicine.

[50]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.