I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

In the past decade, non-invasive and biocompatible mesoporous silica materials as efficient drug delivery systems have attracted special attention. Great progress in structure control and functionalization (magnetism and luminescence) design has been achieved for biotechnological and biomedical applications. This review highlights the most recent research progress on silica-based controlled drug delivery systems, including: (i) pure mesoporous silica sustained-release systems, (ii) magnetism and/or luminescence functionalized mesoporous silica systems which integrate targeting and tracking abilities of drug molecules, and (iii) stimuli-responsive controlled release systems which are able to respond to environmental changes, such as pH, redox potential, temperature, photoirradiation, and biomolecules. Although encouraging and potential developments have been achieved, design and mass production of novel multifunctional carriers, some practical biological application, such as biodistribution, the acute and chronic toxicities, long-term stability, circulation properties and targeting efficacy in vivo are still challenging.

Functionalized mesoporous silica materials for controlled drug delivery

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the dictionary is adapted to the particular image instance thereby favoring better sparsities and consequently much higher undersampling rates. The proposed alternating reconstruction algorithm learns the sparsifying dictionary, and uses it to remove aliasing and noise in one step, and subsequently restores and fills-in the k-space data in the other step. Numerical experiments are conducted on MR images and on real MR data of several anatomies with a variety of sampling schemes. The results demonstrate dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods. These improvements persist over a wide range of practical data signal-to-noise ratios, without any parameter tuning.

MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning

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

Chemical exchange saturation transfer (CEST) imaging is a relatively new magnetic resonance imaging contrast approach in which exogenous or endogenous compounds containing either exchangeable protons or exchangeable molecules are selectively saturated and after transfer of this saturation, detected indirectly through the water signal with enhanced sensitivity. The focus of this review is on basic magnetic resonance principles underlying CEST and similarities to and differences with conventional magnetization transfer contrast. In CEST magnetic resonance imaging, transfer of magnetization is studied in mobile compounds instead of semisolids. Similar to magnetization transfer contrast, CEST has contributions of both chemical exchange and dipolar cross-relaxation, but the latter can often be neglected if exchange is fast. Contrary to magnetization transfer contrast, CEST imaging requires sufficiently slow exchange on the magnetic resonance time scale to allow selective irradiation of the protons of interest. As a consequence, magnetic labeling is not limited to radio-frequency saturation but can be expanded with slower frequency-selective approaches such as inversion, gradient dephasing and frequency labeling. The basic theory, design criteria, and experimental issues for exchange transfer imaging are discussed. A new classification for CEST agents based on exchange type is proposed. The potential of this young field is discussed, especially with respect to in vivo application and translation to humans.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22761

Chemical exchange saturation transfer (CEST): what is in a name and what isn't?

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l 1 norm optimization for CS-MRI. Considering the recovered information ...

Iterative thresholding compressed sensing MRI based on contourlet transform

A synthetic method to prepare novel multifunctional core-shell-structured mesoporous silica nanoparticles for simultaneous magnetic resonance (MR) and fluorescence imaging, cell targeting and photosensitization treatment has been developed. Superparamagnetic magnetite nanoparticles and fluorescent dyes are co-encapsulated inside nonporous silica nanoparticles as the core to provide dual-imaging capabilities (MR and optical). The photosensitizer molecules, tetra-substituted carboxyl aluminum phthalocyanine (AlC4Pc), are covalently linked to the mesoporous silica shell and exhibit excellent photo-oxidation efficiency. The surface modification of the core-shell silica nanoparticles with folic acid enhances the delivery of photosensitizers to the targeting cancer cells that overexpress the folate receptor, and thereby decreases their toxicity to the surrounding normal tissues. These unique advantages make the prepared multifunctional core-shell silica nanoparticles promising for cancer diagnosis and therapy.

Synthesis of magnetic, fluorescent and mesoporous core-shell-structured nanoparticles for imaging, targeting and photodynamic therapy

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.

Purpose An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. Methods The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data. Results Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. Conclusion The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently.

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

A novel image encoding approach based on linear frequency-swept excitation has been recently proposed to overcome artifacts induced by various field perturbations in single-shot echo planar imaging. In this article, we develop a new super-resolved reconstruction method for it using the concepts of local k-space and partial Fourier transform. This method is superior to the originally developed conjugate gradient algorithm in convenience, image quality, and stability of solution. Reduced field-of-view is applied to the phase encoding direction to further enhance the spatial resolution and field perturbation immunity of the image obtained. Effectiveness of this new combined reconstruction method is demonstrated with a series of experiments on biological samples. Two single-shot sequences with different encoding features are tested. The results show that this reconstruction method maintains excellent field perturbation immunity and improves fidelity of the images. In vivo experiments on rat indicate that this solution is favorable for ultrafast imaging applications in which severe susceptibility heterogeneities around the tissue–air or tissue–bone interfaces, motion and oblique plane effects usually compromise the echo planar imaging image quality. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.24366

Congbo Cai

Papers

Iterative thresholding compressed sensing MRI based on contourlet transform

Synthesis of magnetic, fluorescent and mesoporous core-shell-structured nanoparticles for imaging, targeting and photodynamic therapy

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

Partial Fourier transform reconstruction for single-shot MRI with linear frequency-swept excitation.