Pushing the limits of low‐cost ultra‐low‐field MRI by dual‐acquisition deep learning 3D superresolution

Recent development of ultra‐low‐field (ULF) MRI presents opportunities for low‐power, shielding‐free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large‐scale publicly available 3T brain data.

[1]  J. E. Iglesias,et al.  Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning. , 2022, Radiology.

[2]  Alex T. L. Leong,et al.  Robust Electromagnetic Interference (EMI) Elimination via Simultaneous Sensing and Deep Learning Prediction for RF Shielding-free MRI , 2022, 2210.06730.

[3]  N. Salameh,et al.  Deep learning for fast low-field MRI acquisitions , 2022, Scientific Reports.

[4]  A. Webb,et al.  Deep learning-based single image super-resolution for low-field MR brain images , 2022, Scientific Reports.

[5]  Jennifer A. Kim,et al.  Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke , 2022, Science advances.

[6]  Zhezhen Jin,et al.  ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. , 2022, Magnetic resonance imaging.

[7]  Alex T. L. Leong,et al.  A low-cost and shielding-free ultra-low-field brain MRI scanner , 2021, Nature Communications.

[8]  M. Sarracanie.  Fast Quantitative Low-Field Magnetic Resonance Imaging With OPTIMUM—Optimized Magnetic Resonance Fingerprinting Using a Stationary Steady-State Cartesian Approach and Accelerated Acquisition Schedules , 2021, Investigative radiology.

[9]  Alex T. L. Leong,et al.  Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks , 2021, Magnetic resonance in medicine.

[10]  T. O’Reilly,et al.  In vivo T1 and T2 relaxation time maps of brain tissue, skeletal muscle, and lipid measured in healthy volunteers at 50 mT , 2021, Magnetic resonance in medicine.

[11]  Jennifer A. Kim,et al.  Portable, bedside, low-field magnetic resonance imaging for evaluation of intracerebral hemorrhage , 2021, Nature Communications.

[12]  A. Maier,et al.  Robust partial Fourier reconstruction for diffusion‐weighted imaging using a recurrent convolutional neural network , 2021, Magnetic resonance in medicine.

[13]  Jean-Luc Starck,et al.  Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction , 2021, IEEE Transactions on Medical Imaging.

[14]  William A Grissom,et al.  External Dynamic InTerference Estimation and Removal (EDITER) for low field MRI , 2021, Magnetic resonance in medicine.

[15]  Polina Golland,et al.  Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast , 2020, NeuroImage.

[16]  M. Rosen,et al.  Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction , 2020, Scientific Reports.

[17]  Matthew S Rosen,et al.  A portable scanner for brain MRI , 2020, Nature Biomedical Engineering.

[18]  Zheng Xu,et al.  Use of 2.1 MHz MRI scanner for brain imaging and its preliminary results in stroke. , 2020, Journal of magnetic resonance.

[19]  Jennifer A. Kim,et al.  Assessment of Brain Injury Using Portable, Low-Field Magnetic Resonance Imaging at the Bedside of Critically Ill Patients. , 2020, JAMA neurology.

[20]  Thomas Witzel,et al.  Low-cost and portable MRI. , 2020, Journal of magnetic resonance imaging : JMRI.

[21]  W. Teeuwisse,et al.  In vivo 3D brain and extremity MRI at 50 mT using a permanent magnet Halbach array , 2020, Magnetic resonance in medicine.

[22]  Mathieu Sarracanie,et al.  Low-Field MRI: How Low Can We Go? A Fresh View on an Old Debate , 2020, Frontiers in Physics.

[23]  Alejandro F Frangi,et al.  The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions , 2020, Nature Communications.

[24]  Alexandros G. Dimakis,et al.  Deep Learning Techniques for Inverse Problems in Imaging , 2020, IEEE Journal on Selected Areas in Information Theory.

[25]  P. V. van Zijl,et al.  In vivo imaging of phosphocreatine with artificial neural networks , 2020, Nature Communications.

[26]  Shuai Wang,et al.  Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains , 2020, Medical Image Anal..

[27]  Florian Knoll,et al.  Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians , 2020, Journal of magnetic resonance imaging : JMRI.

[28]  Michael G. Rabbat,et al.  Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge , 2020, Magnetic resonance in medicine.

[29]  Daniel C. Alexander,et al.  Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator , 2019, MLMIR@MICCAI.

[30]  A G Webb,et al.  Three-dimensional MRI in a homogenous 27 cm diameter bore Halbach array magnet. , 2019, Journal of magnetic resonance.

[31]  Qianqian Zhang,et al.  MRI Gibbs‐ringing artifact reduction by means of machine learning using convolutional neural networks , 2019, Magnetic resonance in medicine.

[32]  Sairam Geethanath,et al.  Accessible magnetic resonance imaging: A review , 2019, Journal of magnetic resonance imaging : JMRI.

[33]  Yvonne W. Lui,et al.  Training a neural network for Gibbs and noise removal in diffusion MRI , 2019, Magnetic resonance in medicine.

[34]  Chen Change Loy,et al.  EDVR: Video Restoration With Enhanced Deformable Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[35]  Julia M. Huntenburg,et al.  A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults , 2019, Scientific Data.

[36]  A. Webb,et al.  Low‐field MRI: An MR physics perspective , 2019, Journal of magnetic resonance imaging : JMRI.

[37]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[38]  Chao Dong,et al.  Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[39]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

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

[41]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

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

[43]  E. Chekmenev,et al.  Low-field MRI can be more sensitive than high-field MRI. , 2013, Journal of magnetic resonance.

[44]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

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

[46]  Frank Resmer,et al.  Cryogenic receive coil and low noise preamplifier for MRI at 0.01T. , 2010, Journal of magnetic resonance.

[47]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[48]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[49]  R R Edelman,et al.  Magnetic resonance imaging (1). , 1993, The New England journal of medicine.

[50]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[51]  P A Rinck,et al.  Nuclear relaxation of human brain gray and white matter: Analysis of field dependence and implications for MRI , 1990, Magnetic resonance in medicine.

[52]  T. Foster,et al.  A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. , 1984, Medical physics.

[53]  D. Adams,et al.  Magnetic field dependence of 1/T1 of protons in tissue. , 1984, Investigative radiology.