Brain Imaging Applications Research Articles

Evaluation of MRI sequences for quantitative T1 brain mapping

T1 mapping constitutes a quantitative MRI technique finding significant application in brain imaging. It allows evaluation of contrast uptake, blood perfusion, volume, providing a more specific biomarker of disease progression compared to conventional T1-weighted images. While there are many techniques for T1-mapping there is a wide range of reported T1-values in tissues, raising the issue of protocols reproducibility and standardization. The gold standard for obtaining T1-maps is based on acquiring IR-SE sequence. Widely used alternative sequences are IR-SE-EPI, VFA (DESPOT), DESPOT-HIFI and MP2RAGE that speed up scanning and fitting procedures. A custom MRI phantom was used to assess the reproducibility and accuracy of the different methods. All scans were performed using a 3T Siemens Prisma scanner. The acquired data processed using two different codes. The main difference was observed for VFA (DESPOT) which grossly overestimated T1 relaxation time by 214 ms [126 270] compared to the IR-SE sequence. MP2RAGE and DESPOT-HIFI sequences gave slightly shorter time than IR-SE (~20 to 30ms) and can be considered as alternative and time-efficient methods for acquiring accurate T1 maps of the human brain, while IR-SE-EPI gave identical result, at a cost of a lower image quality.

Adaptive optical versus spherical aberration corrections for in vivo brain imaging.

Adjusting the objective correction collar is a widely used approach to correct spherical aberrations (SA) in optical microscopy. In this work, we characterized and compared its performance with adaptive optics in the context of in vivo brain imaging with two-photon fluorescence microscopy. We found that the presence of sample tilt had a deleterious effect on the performance of SA-only correction. At large tilt angles, adjusting the correction collar even worsened image quality. In contrast, adaptive optical correction always recovered optimal imaging performance regardless of sample tilt. The extent of improvement with adaptive optics was dependent on object size, with smaller objects having larger relative gains in signal intensity and image sharpness. These observations translate into a superior performance of adaptive optics for structural and functional brain imaging applications in vivo, as we confirmed experimentally.

Neuronal imaging with ultrahigh dynamic range multiphoton microscopy

Multiphoton microscopes are hampered by limited dynamic range, preventing weak sample features from being detected in the presence of strong features, or preventing the capture of unpredictable bursts in sample strength. We present a digital electronic add-on technique that vastly improves the dynamic range of a multiphoton microscope while limiting potential photodamage. The add-on provides real-time negative feedback to regulate the laser power delivered to the sample, and a log representation of the sample strength to accommodate ultrahigh dynamic range without loss of information. No microscope hardware modifications are required, making the technique readily compatible with commercial instruments. Benefits are shown in both structural and in-vivo functional mouse brain imaging applications.

Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations

Nonnegative matrix factorization is a key tool in many data analysis applications such as feature extraction, compression, and noise filtering. Many existing algorithms impose additional constraints to take into account prior knowledge and to improve the physical interpretation. This letter proposes a novel algorithm for nonnegative matrix factorization, in which the factors are modeled by nonnegative polynomials. Using a parametric representation of finite-interval nonnegative polynomials, we obtain an optimization problem without external nonnegativity constraints, which can be solved using conventional quasi-Newton or nonlinear least-squares methods. The polynomial model guarantees smooth solutions and may realize a noise reduction. A dedicated orthogonal compression enables a significant reduction of the matrix dimensions, without sacrificing accuracy. The overall approach scales well to large matrices. The approach is illustrated with applications in hyperspectral imaging and chemical shift brain imaging.

A numerical analysis of a semi-dry coupling configuration in photoacoustic computed tomography for infant brain imaging

In the application of photoacoustic human infant brain imaging, debubbled ultrasound gel or water is commonly used as a couplant for ultrasonic transducers due to their acoustic properties. The main challenge in using such a couplant is its discomfort for the patient. In this study, we explore the feasibility of a semi-dry coupling configuration to be used in photoacoustic computed tomography (PACT) systems. The coupling system includes an inflatable container consisting of a thin layer of Aqualene with ultrasound gel or water inside of it. Finite element method (FEM) is used for static and dynamic structural analysis of the proposed configuration to be used in PACT for infant brain imaging. The outcome of the analysis is an optimum thickness of Aqualene in order to meet the weight tolerance requirement with the least attenuation and best impedance match to recommend for an experimental setting.

7T: Physics, safety, and potential clinical applications.

4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1573-1589.

Examining the clinical utility of neuroimaging on an inpatient psychiatric unit

IntroductionRecent developments in neuroimaging have revolutionized medicine and aided in our understanding of how biological abnormalities may contribute to clinical presentation. While such advances have begun to enhance our knowledge about the timing of abnormalities, it remains unclear at this time how neuroimaging impacts the clinical course of the patient. In addition, much debate exists regarding the clinical necessity of neuroimaging for psychiatric conditions, and there are contradictory reports and guidelines for the application of conventional brain imaging (MRI and CT) in the evaluation of patients with mental illness.ObjectiveWe aim to review the clinical utility of neuroimaging in an acute psychiatric setting, and hypothesize that there will be no significant differences between the outcome of neuroimaging and clinical course for patients.MethodWe conducted a retrospective chart review of adult patients who were diagnosed and treated for psychiatric conditions on an inpatient psychiatric service over a period of 36 months July 1, 2013–June 30, 2016.ConclusionsWhile imaging advances have added to our understanding of biological abnormalities and can aid in ruling out organic causes of psychiatric illness, at this time it is not guiding clinical management for patients.Disclosure of interestThe authors have not supplied their declaration of competing interest.

创伤后应激障碍的脑功能磁共振成像研究进展

创伤后应激障碍(PTSD)是个体直接或间接接触自然灾害、战争、性侵害、严重交通事故、长期监禁与拷问等创伤性事件后所出现的应激相关障碍，对个体的身心健康和社会适应性造成严重损害的一类疾病。近年来，随着医学影像学技术的发展，人们对于创伤后应激障碍的发病机制的研究更加深入，更多的从影像神经学等层面去探讨其发生发展的机理，尤其是脑神经影像学研究(如功能磁共振成像)在创伤后应激障碍中的应用日益增多。脑功能磁共振成像技术不仅能够直观的观测到患者具体病变的脑区部位而且能够观测到其脑区神经回路的转变，对神经精神疾病的发病机制的研究具有重要作用。本文主要综述脑功能磁共振成像技术在创伤后应激障碍中的应用及进展，并提出今后可能的研究方向。 Post-traumatic stress disorder (PTSD) is stress related disorders by individual direct or indirect contact with natural disasters, wars, sexual abuse, serious traffic accidents, long-term imprison-ment and torture after a traumatic event, a disease causing serious damage to the individual's physical and mental health and social adaptability. In recent years, with the rapid development of medical imaging technology, research on the pathogenesis of post-traumatic stress disorder, more deeply, more from the perspective of imaging neuroscience to explore the mechanism of its occurrence and development, especially the study on brain imaging (such as functional magnetic resonance imaging) application in post-traumatic stress disorder is increasing. Functional magnetic resonance imaging technique can not only intuitively observe the brain areas in patients with specific lesions but also observe the changes of neural circuits in the brain area; it is very important to study the pathogenesis of neuropsychiatric disorders. This paper reviews the application and progress of brain functional magnetic resonance imaging in post-traumatic stress disorder, and puts forward the possible research directions in the future.

Role of Magnetic Resonance Imaging in the Diagnosis of Fetal Brain Anomalies

ABSTRACT Prenatal Ultrasonagraphy (US) is the mainstay modality to diagnose fetal abnormalities especially in early pregnancy. Fetal Magnetic Resonance Imaging (MRI) is a useful tool to confirm and to characterize a pathology that is suspected on US, especially in the detection of central nervous system pathologies. The use of ultrafast imaging tecniques gives additional importantant informations and optimal imaging quality, despite fetal motion, in clinical practice. Diffusion Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI), MR Spetroscopy and Functional studies have potential applications in the fetal brain imaging. Fetal MRI could recognize, in contradistinction to US, the development of fetal brain, the multilayered appearance of the cerebral parenchyma, the timing of sulci development and the myelination. The most common indications for fetal MRI are ventriculomegaly, midline anomalies, malformations of cerebral cortical development, posterior fossa anomalies, suspected haemorraghic-ischemic lesions, tumors. Fetal MRI is a safe and powerful complement to US for clinical management and prognostication. How to cite this article Resta M, Dicuonzo F, Resta M. Role of Magnetic Resonance Imaging in the Diagnosis of Fetal Brain Anomalies. Donald School J Ultrasound Obstet Gynecol 2017;11(4):328-340.

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment. However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper, we propose a novel temporallyconstrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term thatrequires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term thatrequires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers.

Functional data analysis for density functions by transformation to a Hilbert space

Functional data that are nonnegative and have a constrained integral can be considered as samples of one-dimensional density functions. Such data are ubiquitous. Due to the inherent constraints, densities do not live in a vector space and, therefore, commonly used Hilbert space based methods of functional data analysis are not applicable. To address this problem, we introduce a transformation approach, mapping probability densities to a Hilbert space of functions through a continuous and invertible map. Basic methods of functional data analysis, such as the construction of functional modes of variation, functional regression or classification, are then implemented by using representations of the densities in this linear space. Representations of the densities themselves are obtained by applying the inverse map from the linear functional space to the density space. Transformations of interest include log quantile density and log hazard transformations, among others. Rates of convergence are derived for the representations that are obtained for a general class of transformations under certain structural properties. If the subject-specific densities need to be estimated from data, these rates correspond to the optimal rates of convergence for density estimation. The proposed methods are illustrated through simulations and applications in brain imaging.

Optical Coherence Tomography for Brain Imaging and Developmental Biology.

Optical coherence tomography (OCT) is a promising research tool for brain imaging and developmental biology. Serving as a three-dimensional optical biopsy technique, OCT provides volumetric reconstruction of brain tissues and embryonic structures with micrometer resolution and video rate imaging speed. Functional OCT enables label-free monitoring of hemodynamic and metabolic changes in the brain in vitro and in vivo in animal models. Due to its non-invasiveness nature, OCT enables longitudinal imaging of developing specimens in vivo without potential damage from surgical operation, tissue fixation and processing, and staining with exogenous contrast agents. In this paper, various OCT applications in brain imaging and developmental biology are reviewed, with a particular focus on imaging heart development. In addition, we report findings on the effects of a circadian gene (Clock) and high-fat-diet on heart development in Drosophila melanogaster. These findings contribute to our understanding of the fundamental mechanisms connecting circadian genes and obesity to heart development and cardiac diseases.

Quantifying Infinite-Dimensional Data: Functional Data Analysis in Action

Functional data analysis (FDA) is concerned with inherently infinite-dimensional data objects and therefore can be viewed as part of the methodology for big data. The size of functional data may vary from terabytes as encountered in functional magnetic resonance imaging (fMRI) and other applications in brain imaging to just a few kilobytes in longitudinal data with small or modest sample sizes. In this contribution, we highlight some applications of FDA methodology through various data illustrations. We briefly review some basic computational tools that can be used to accelerate implementations of FDA methodology. The analyses presented in this paper illustrate the principal analysis by conditional expectation (PACE) package for FDA, where our applications include both relatively simple and more complex functional data from the biomedical sciences. The data we discuss range from functional data that result from daily movement profile tracking and that are modeled as repeatedly observed functions per subject, to medfly longitudinal behavior profiles, where the goal is to predict remaining lifetime of individual flies. We also discuss the quantification of connectivity of fMRI signals that is of interest in brain imaging and the prediction of continuous traits from high-dimensional SNPs in genomics. The methods of FDA that we demonstrate for these analyses include functional principal component analysis, functional regression and correlation, the modeling of dependent functional data, and the stringing of high-dimensional data into functional data and can be implemented with the PACE package.

The Preparation and Application of Dendrimer Modified CdTe/CdS Near Infrared Quantum Dots for Brain Cancer Cells Imaging

The most notable obstacle of brain cancer diagnosis is the inability of delivering imaging agents across the blood-brain barrier (BBB). Recently, quantum dots (QDs) has been demonstrated as an ideal image agent for brain imaging due to their ultra-small size for crossing BBB. The plolyamidoamine dendrimers modified CdTe/CdS core/shell near-infrared (NIR) region QDs was successfully synthesized in aqueous solution, and then was characterized by UV-vis absorption, photoluminescence (PL) spectroscopy, dynamic light scattering (DLS), X-ray powder diffraction (XRD) and high-resolution transmission electron microscopy (HR-TEM), etc. Our results reveal that the dendrimers modified CdTe/CdS QDs exhibits good water-dispersity and stable NIR fluorescence in various biological environments. In addition, this NIR QDs demonstrates a good biocompatibility and sensitive photoluminescence responses in brain tumor cell imaging. In a word, this type of dendrimers modified NIR CdTe/CdS QDs has huge potential applications in brain imaging.

Simultaneous <sup>99m</sup>tc/<sup>111</sup>in spect reconstruction using accelerated convolution-based forced detection monte carlo

Simultaneous multi-isotope Single Photon Emission Computed Tomography (SPECT) imaging has a number of applications in cardiac, brain, and cancer imaging. The major concern however, is the significant crosstalk contamination due to photon scatter between the different isotopes. The current study focuses on a method of crosstalk compensation between two isotopes in simultaneous dual isotope SPECT acquisition applied to cancer imaging using $^{99m}$ Tc and $^{111}$ In. We have developed an iterative image reconstruction technique that simulates the photon down-scatter from one isotope into the acquisition window of a second isotope. Our approach uses an accelerated Monte Carlo (MC) technique for the forward projection step in an iterative reconstruction algorithm. The MC estimated scatter contamination of a radionuclide contained in a given projection view is then used to compensate for the photon contamination in the acquisition window of other nuclide. We use a modified ordered subset-expectation maximization (OS-EM) algorithm named simultaneous ordered subset-expectation maximization (Sim-OSEM), to perform this step. We have undertaken a number of simulation tests and phantom studies to verify this approach. The proposed reconstruction technique was also evaluated by reconstruction of experimentally acquired phantom data. Reconstruction using Sim-OSEM showed very promising results in terms of contrast recovery and uniformity of object background compared to alternative reconstruction methods implementing alternative scatter correction schemes (i.e., triple energy window or separately acquired projection data). In this study the evaluation is based on the quality of reconstructed images and activity estimated using Sim-OSEM. In order to quantitate the possible improvement in spatial resolution and signal to noise ratio (SNR) observed in this study, further simulation and experimental studies are required.

Hot Topics in Research: Preventive Neuroradiology in Brain Aging and Cognitive Decline.

Preventive neuroradiology is a new concept supported by growing literature. The main rationale of preventive neuroradiology is the application of multimodal brain imaging toward early and subclinical detection of brain disease and subsequent preventive actions through identification of modifiable risk factors. An insightful example of this is in the area of age-related cognitive decline, mild cognitive impairment, and dementia with potentially modifiable risk factors such as obesity, diet, sleep, hypertension, diabetes, depression, supplementation, smoking, and physical activity. In studying this link between lifestyle and cognitive decline, brain imaging markers may be instrumental as quantitative measures or even indicators of early disease. The purpose of this article is to provide an overview of the major studies reflecting how lifestyle factors affect the brain and cognition aging. In this hot topics review, we will specifically focus on obesity and physical activity.

Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy-based brain imaging.

Test-retest reliability of neuroimaging measurements is an important concern in the investigation of cognitive functions in the human brain. To date, intraclass correlation coefficients (ICCs), originally used in interrater reliability studies in behavioral sciences, have become commonly used metrics in reliability studies on neuroimaging and functional near-infrared spectroscopy (fNIRS). However, as there are six popular forms of ICC, the adequateness of the comprehensive understanding of ICCs will affect how one may appropriately select, use, and interpret ICCs toward a reliability study. We first offer a brief review and tutorial on the statistical rationale of ICCs, including their underlying analysis of variance models and technical definitions, in the context of assessment on intertest reliability. Second, we provide general guidelines on the selection and interpretation of ICCs. Third, we illustrate the proposed approach by using an actual research study to assess interest reliability of fNIRS-based, volumetric diffuse optical tomography of brain activities stimulated by a risk decision-making protocol. Last, special issues that may arise in reliability assessment using ICCs are discussed and solutions are suggested.

Qualitative and Quantitative Evaluation of Blob-Based Time-of-Flight PET Image Reconstruction in Hybrid Brain PET/MR Imaging.

PurposeMany neurological diseases affect small structures in the brain and, as such, reliable visual evaluation and accurate quantification are required. Recent technological developments made the clinical use of hybrid positron emission tomography/magnetic resonance (PET/MR) systems possible, providing both functional and anatomical information in a single imaging session. Nevertheless, there is a trade-off between spatial resolution and image quality (contrast and noise), which is dictated mainly by the chosen acquisition and reconstruction protocols. Image reconstruction algorithms using spherical symmetric basis functions (blobs) for image representation have a number of additional parameters that impact both the qualitative and quantitative image characteristics. Hence, a detailed investigation of the blob-based reconstruction characteristics using different parameters is needed to achieve optimal reconstruction results.ProceduresThis work evaluated the impact of a range of blob parameters on image quality and quantitative accuracy of brain PET images acquired on the Ingenuity Time-of-Flight (TOF) PET/MR system. Two different phantoms were used to simulate brain imaging applications. Image contrast and noise characteristics were assessed using an image quality phantom. Quantitative performance in a clinical setting was investigated using the Hoffman 3D brain phantom at various count levels. Furthermore, the visual quality of four clinical studies was scored blindly by two experienced physicians to qualitatively evaluate the influence of different reconstruction protocols, hereby providing indications on parameters producing the best image quality.ResultsQuantitative evaluation using the image quality phantom showed that larger basis function radii result in lower contrast recovery (∼2 %) and lower variance levels (∼15 %). The brain phantom and clinical studies confirmed these observations since lower contrast was seen between anatomical structures. High and low count statistics gave comparable values. The qualitative evaluation of the clinical studies, based on the assessment performed by the physicians, showed a preference towards lower image variance levels with a slightly lower contrast, favoring higher radii and four iterations.ConclusionThis study systematically evaluated a number of basis function parameters and their quantitative and qualitative effect within PET image reconstruction in the context of brain imaging. A range of blob parameters can minimize error and provided optimal image quality, where the anatomical structures could be recognized but the exact delineation of these structures is simplified in scans with lower image variance levels and thus, higher radii should be preferred. With the optimization of blob parameters, the reconstructed images were found to be qualitatively improved (optimum parameters {d = 2.0375, alpha = 10.4101, radius = 3.9451}) as assessed by the physicians compared to the current clinical protocol. However, this qualitative improvement is task specific, depending on the desired image characteristics to be extracted. Finally, this work could be used as a guide for application-specific optimal parameter selection.Electronic supplementary materialThe online version of this article (doi:10.1007/s11307-015-0824-x) contains supplementary material, which is available to authorized users.

ABrIL - Advanced Brain Imaging Lab : a cloud based computation environment for cooperative neuroimaging projects.

Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.

Recent Progress in Voltage-Sensitive Dye Imaging for Neuroscience

Voltage-sensitive dye imaging (VSDi) enables visualization of information processing in different areas of the brain with reasonable spatial and temporal resolution. VSDi employs different chemical compounds to transduce neural activity directly into the changes in intrinsic optical signal. Physically, voltage-sensitive dyes (VSDs) are chemical probes that reside in the neural membrane and change their fluorescence or absorbance in response to membrane potential changes. Based on these features, VSDs can be divided into two groups-absorbance and fluorescence. The spatial and temporal resolution of the VSDi is limited mainly by the technical characteristics of the optical imaging setup (e.g., computer and light-sensitive device-charge-coupled device (CCD) camera or photodiode array). In this article, we briefly review the development of the VSD, technique of VSDi and applications in functional brain imaging.