What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. 
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

An overview of deep learning in medical imaging focusing on MRI

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Magnetic Resonance Imaging Physical Principles And Sequence Design

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deep-learning-based image reconstruction methods for MRI. Two types of deep-learningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

A potential epoxy resin, i.e. epoxidized soybean oil (ESO), was synthesized to toughen the tetrafunctional epoxy resins. The ESO was blended with the epoxy resins to obtain the modified network having ESO content from 0 to 20 wt.%. The neat epoxy resins and modified networks were characterized by the thermal and mechanical properties. As a result, thermal stability and glass transition temperature of the blends were slightly decreased with increasing the ESO content. This might be caused by the reduction of cross-linking density of the epoxy network, which could be attributed to the incomplete curing reaction in the blend systems. The critical stress intensity factor and flexural strength were increased up to 10 wt.% ESO content. This could be interpreted in terms of the addition of larger ESO molecule weight into the epoxy resins, resulting in increasing the flexible properties of the epoxy resins, or the final toughness of the blend systems.

Thermal and mechanical properties of tetrafunctional epoxy resin toughened with epoxidized soybean oil

Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet

Thermal decomposition kinetics of diglycidyl ether of bisphenol A (DGEBA)/4,4′-methylene dianiline (MDA) system with rubber-modified MDA was studied by the methods of Ozawa, Kissinger, and Friedman, and the kinetic parameters were compared. The thermal decomposition data of the cured epoxy resin were analyzed by thermogravimetric analysis (TGA) at different heating rates. TG curves showed that the thermal decomposition of the epoxy system occurred in one stage regardless of rubber-modified MDA content. The apparent activation energies for the DGEBA/MDA system with 10 phr of rubber-modified MDA, as determined by the Ozawa, Kissinger, and Friedman methods, are 184, 182, and 222 kJ/mol, respectively. The thermal stability of the epoxy system increased with the increasing content of rubber-modified MDA, which has four benzene rings with high thermal resistance due to the resonance structure. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 81: 479–485, 2001

Thermal decomposition kinetics of an epoxy resin with rubber-modified curing agent

Kinetic studies of an epoxy cure reaction by isothermal DSC analysis

Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI).

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Jingu Lee

Papers

Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet

Thermal decomposition kinetics of an epoxy resin with rubber-modified curing agent

Kinetic studies of an epoxy cure reaction by isothermal DSC analysis

Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI).

Deep Learning in MR Image Processing