Brain Imaging Applications Research Articles

Active electrode IC for EEG and electrical impedance tomography with continuous monitoring of contact impedance.

The IC presented integrates the front-end for EEG and Electrical Impedance Tomography (EIT) acquisition on the electrode, together with electrode-skin contact impedance monitoring and EIT current generation, so as to improve signal quality and integration of the two techniques for brain imaging applications. The electrode size is less than 2 cm(2) and only 4 wires connect the electrode to the back-end. The readout circuit is based on a Differential Difference Amplifier and performs single-ended amplification and frequency division multiplexing of the three signals that are sent to the back-end on a single wire which also provides power supply. Since the system's CMRR is a function of each electrode's gain accuracy, an analysis is performed on how this is influenced by mismatches in passive and active components. The circuit is fabricated in 0.35 μm CMOS process and occupies 4 mm(2), the readout circuit consumes 360 μW, the input referred noise for bipolar EEG signal acquisition is 0.56 μVRMS between 0.5 and 100 Hz and almost halves if only EEG signal is acquired.

CMOS Time-Domain Imager for Functional Brain Imaging Using Gated Near-Infrared Spectroscopy

Non-invasive imaging of brain activity enables novel studies in neuroscience and provides an alternative modality for clinical monitoring applications. Near-infrared spectroscopic imaging (NIRSI) is non-invasive, cheap, portable, and immune to electro-magnetic interference. NIRSI is also superior in terms of spatial or temporal resolution when compared to electro-encephalography (EEG) or magnetic resonance imaging (MRI), respectively. Utilizing the time-domain (TD) technique offers the richest information at the cost of being the most complex[1]. TD NIRS imagers utilize time-correlated single-photon-counting (TCSPC) measurements which require detectors with single-photon sensitivity like single-photon avalanche diodes (SPADs)[2] or Silicon photomultipliers (SiPMs) and very fast time-discriminators like time-discriminator circuits (TDCs). Implementing these circuits in well-established and mature CMOS technologies[3] is advantageous.The main challenges in designing a TD NIRSI sensor deal with four issues: spectral responsivity, noise, fill-factor, and throughput. The spectral responsivity and noise are shaped by the technology process and structure (see figure 2) of the SPAD[4]. The fill-factor is determined by the ancillary circuitry needed to maintain the TD operation of the SPAD[4] (i.e., frontend, gating, and TDC). The throughput is dependent on the readout architecture of the array of pixels.We are proposing a fully integrated scalable array of time-gated actively-quenched SPADs with shared time-gated ring-oscillator-based TDCs following H-tree-based architecture and a 3-transistor active-pixel sensor (3T APS) readout scheme with in-pixel storage capability to be implemented using standard deep sub-micron CMOS technology. For example, using 130 nm CMOS technology SPADs can be built with an area of 50 um2 which exhibit a dark count rate (DCR) of 18 KHz[5]. At a wavelength around 600 nm and an excess bias of 2 V, their photon detection probability (PDP) could reach 22%. These SPADs breakdown at 20 V and could resolve down to 90 ps for wavelengths around 654 nm. With the same technology a 0.04 mm2 TDC can be designed with a resolution or least significant bit (LSB) of 6 ps for a range of 11-bits[6]. Moreover, SPAD structure variations, novel gating schemes, smart resource sharing, and efficient array architecture a CMOS TD NIRSI sensor can be realized to meet requirements of functional human brain imaging applications.

7 Tesla MR Imaging: Opportunities and Challenges

The urge to increase magnetic field strength is driven by a number of potentially beneficial physical changes, possibly resulting in improved MR diagnostics. With the successful introduction of in-vivo ultra-high-field MR imaging, by means of 7 Tesla MRI, the focus of scientific research has been set on compiling different applications of brain and body imaging. This review presents an overview on the current status of 7 T MR imaging, investigating the opportunities as well as challenges associated with ultra-high-field MRI. Citation Format: • Umutlu L, Ladd ME, Forsting M et al. 7 Tesla MRImaging: Opportunities and Challenges. Fortschr Röntgenstr 2014; 186: 121 - 129.

A MRI Rotary Phased Array Head Coil

A new rotary phased array (RPA) head coil that can provide homogenous brain images comparable to volumetric radiofrequency coils is proposed for magnetic resonance brain imaging applications. The design of the RPA head coil is a departure from conventional circumferential array design method, as coil elements of the RPA head coil have a "paddle-like" structure consisting of a pair of main conductors located on opposite sides, inserted equi-angularly around and over the head. A prototype 2T receive-only 4-element RPA head coil was constructed and experimentally tested against a conventional receive-only 4-element phased array head coil and a commercial receive-only quadrature birdcage head coil. Homogenous phantom images acquired by the RPA head coil show that signal intensity deep at the center of the phantom was improved as compared to the conventional phased array head coil and this improvement allow the RPA head coil to acquire homogenous brain images similar to brain images acquired with the birdcage head coil. In addition, partial parallel imaging was used in conjunction with the RPA head coil to enable rapid imaging.

Letter by Bhupali and Labovitz Regarding Article, “Clinical Scores for Predicting Recurrence After Transient Ischemic Attack or Stroke: How Good Are They?”

Lemmens et al1 were unable to validate several prediction models of stroke recurrence after transient ischemic attack (TIA) or minor stroke and suggest that methodological differences between studies explain the variation. However, many of the prediction tools were evaluated in settings that differed from the ones in which they were originally validated. We suspect that this change in intended use is why the performance of the tools was inconsistent at best. One of the studies cited relies on application of brain and carotid imaging in …

Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play

In response to queries about whether brain imaging technology has reached the point where it is useful for making a clinical diagnosis and for helping to guide treatment selection, the American Psychiatric Association (APA) has recently written a position paper on the Clinical Application of Brain Imaging in Psychiatry. The following perspective piece is based on our contribution to this APA position paper, which specifically emphasized the application of neuroimaging in mood disorders. We present an introductory overview of the challenges faced by researchers in developing valid and reliable biomarkers for psychiatric disorders, followed by a synopsis of the extant neuroimaging findings in mood disorders, and an evidence-based review of the current research on brain imaging biomarkers in adult mood disorders. Although there are a number of promising results, by the standards proposed below, we argue that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders.

Investigation of crystal surface finish and geometry on single LYSO scintillator detector performance for depth-of-interaction measurement with silicon photomultipliers

Depth of Interaction (DOI) information can improve quality of reconstructed images acquired from Positron Emission Tomography (PET), especially in high resolution and compact scanners dedicated for breast, brain, or small animal imaging applications. Additionally, clinical scanners with time of flight capability can also benefit from DOI information. One of the most promising methods of determining DOI in a crystal involves reading the signal from two ends of a scintillation crystal, and calculating the signal ratio between the two detectors. This method is known to deliver a better DOI resolution with rough crystals compared to highly polished crystals. However, what is still not well studied is how much of a tradeoff is involved between spatial, energy, temporal, and DOI resolutions as a function of the crystal surface treatment and geometry with the use of Silicon Photomultipliers (SiPM) as the photo detectors. This study investigates the effects of different crystal surface finishes and geometries on energy, timing and DOI resolutions at different crystal depths. The results show that for LYSO scintillators of 1.5×1.5×20mm3 and 2×2×20mm3 with their surfaces finished from 0.5 to 30μm roughness, almost the same energy and coincidence timing resolutions were maintained, around 15% and 2.4ns, respectively across different crystal depths, while the DOI resolutions were steadily improved from worse than 5mm to better than 2mm. They demonstrate that crystal roughness, with proper surface preparing, does not have a significant effect on the energy and coincidence timing resolutions in the crystals examined, and there does not appear to be a tradeoff between improving DOI resolution and degrading other detector performances. These results will be valuable to guide the selection of crystal surface conditions for developing a DOI measurable PET detector with a full array of LYSO scintillators coupled to SiPM arrays.

Brain Imaging Analysis Can Identify Participants under Regular Mental Training

Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience.

Source localization approach for functional DOT using MUSIC and FDR control

In this paper, we formulate diffuse optical tomography (DOT) problems as a source localization problem and propose a MUltiple SIgnal Classification (MUSIC) algorithm for functional brain imaging application. By providing MUSIC spectra for major chromophores such as oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), we are able to investigate the spatial distribution of brain activities. Moreover, the false discovery rate (FDR) algorithm can be applied to control the family-wise error in the MUSIC spectra. The minimum distance between the center of mass in DOT and the Montreal Neurological Institute (MNI) coordinates of target regions in experiments was between approximately 6 and 18 mm, and the displacement of the center of mass in DOT and fMRI ranged between 12 and 28 mm, which demonstrate the legitimacy of the DOT-based imaging. The proposed brain mapping method revealed its potential as an alternative algorithm to monitor the brain activation.

Clear up this fuzzy thinking on brain scans

France has banned commercial applications of brain imaging. So why approve its use in court, asks Olivier Oullier.

Diagnostics, rehabilitation and models of Parkinson’s disease

Diagnostics and rehabilitation of Parkinson’s disease (PD) presents the current information pertaining to etiology, early biomarkers for diagnostics, novel methods to evaluate symptoms, multidisciplinary rehabilitation, new applications of brain imaging and invasive methods to the study of PD. Researchers have only recently begun to focus on the non-motor symptoms of PD, which are poorly recognized and inadequately treated by clinicians. The non-motor symptoms of PD have a significant impact on patient quality of life and mortality, and include cognitive impairments, autonomic, gastrointestinal, and sensory symptoms. Indepth discussion of the use of imaging tools to study disease mechanisms is also provided, with emphasis on the abnormal network organization in parkinsonism. Deep brain stimulation management is a paradigm-shifting therapy for PD, essential tremor and dystonia. In the recent years, new approaches of early diagnostics, training programmes and treatments have vastly improved the lives of people with PD, substantially reducing symptoms and significantly delaying disability. PD results primarily from the death of dopaminergic neurons in the substantia nigra. Current PD medications treat symptoms; none halt or retard dopaminergic neuron degeneration. The main obstacle to developing neuroprotective therapies is a limited understanding of the key molecular mechanisms that provoke neurodegeneration. The discovery of PD genes has led to the hypothesis that misfolding of proteins and dysfunction of the ubiquitin-proteasome pathway are pivotal to PD pathogenesis. Previously implicated culprits in PD neurodegeneration, mitochondrial dysfunction and oxidative stress, may also act in part by causing the accumulation of misfolded proteins, in addition to producing other deleterious events in dopaminergic neurons. Neurotoxin-based models have been important in elucidating the molecular cas-cade of cell death in dopaminergic neurons. PD models based on the manipulation of PD genes should prove valuable in elucidating important aspects of the disease, such as selective vulnerability of substantia nigra dopaminergic neurons to the degenerative process.

Biophotonics techniques for structural and functional imaging, in vivo.

In vivo optical imaging is being conducted in a variety of medical applications, including optical breast cancer imaging, functional brain imaging, endoscopy, exercise medicine, and monitoring the photodynamic therapy and progress of neoadjuvant chemotherapy. In the past three decades, in vivo diffuse optical breast cancer imaging has shown promising results in cancer detection, and monitoring the progress of neoadjuvant chemotherapy. The use of near infrared spectroscopy for functional brain imaging has been growing rapidly. In fluorescence imaging, the difference between autofluorescence of cancer lesions compared to normal tissues were used in endoscopy to distinguish malignant lesions from normal tissue or inflammation and in determining the boarders of cancer lesions in surgery. Recent advances in drugs targeting specific tumor receptors, such as AntiBodies (MAB), has created a new demand for developing non-invasive in vivo imaging techniques for detection of cancer biomarkers, and for monitoring their down regulations during therapy. Targeted treatments, combined with new imaging techniques, are expected to potentially result in new imaging and treatment paradigms in cancer therapy. Similar approaches can potentially be applied for the characterization of other disease-related biomarkers. In this chapter, we provide a review of diffuse optical and fluorescence imaging techniques with their application in functional brain imaging and cancer diagnosis.

Modern Trends in Imaging IX: Biophotonics Techniques for Structural and Functional Imaging,In Vivo

In vivooptical imaging is being conducted in a variety of medical applications, including optical breast cancer imaging, functional brain imaging, endoscopy, exercise medicine, and monitoring the photodynamic therapy and progress of neoadjuvant chemotherapy. In the past three decades,in vivodiffuse optical breast cancer imaging has shown promising results in cancer detection, and monitoring the progress of neoadjuvant chemotherapy. The use of near infrared spectroscopy for functional brain imaging has been growing rapidly. In fluorescence imaging, the difference between autofluorescence of cancer lesions compared to normal tissues were used in endoscopy to distinguish malignant lesions from normal tissue or inflammation and in determining the boarders of cancer lesions in surgery. Recent advances in drugs targeting specific tumor receptors, such as AntiBodies (MAB), has created a new demand for developing non-invasivein vivoimaging techniques for detection of cancer biomarkers, and for monitoring their down regulations during therapy. Targeted treatments, combined with new imaging techniques, are expected to potentially result in new imaging and treatment paradigms in cancer therapy. Similar approaches can potentially be applied for the characterization of other disease-related biomarkers. In this chapter, we provide a review of diffuse optical and fluorescence imaging techniques with their application in functional brain imaging and cancer diagnosis.

Contrast enhanced high-resolution diffuse optical tomography of the human brain using ICG

Non-invasive diffuse optical tomography (DOT) of the adult brain has recently been shown to improve the spatial resolution for functional brain imaging applications. Here we show that high-resolution (HR) DOT is also advantageous for clinical perfusion imaging using an optical contrast agent. We present the first HR-DOT results with a continuous wave near infrared spectroscopy setup using a dense grid of optical fibers and indocyanine green (ICG) as an exogenic contrast agent. We find an early arrival of the ICG bolus in the intracerebral tissue and a delayed arrival of the bolus in the extracerebral tissue, achieving the separation of both layers. This demonstrates the method’s potential for brain perfusion monitoring in neurointensive care patients.

Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles.

Segregation and integration are two general principles of the brain's functional architecture. Therefore, brain network analysis is of significant importance in understanding brain function. Critical to brain network construction and analysis is the identification of reliable, reproducible, and accurate network nodes, or Regions of Interest (ROIs). Task-based fMRI has been widely considered as a reliable approach to identify functionally meaningful ROIs in the brain. However, recent studies have shown that factors such as spatial smoothing could considerably shift the locations of detected activation peaks. As a result, structural and functional connectivity patterns can be significantly altered. Here, we propose a novel framework by which to optimize ROI sizes and locations, ensuring that differences between the structural connectivity profiles among a group of subjects is minimized. This framework is based on functional ROIs derived from task-based fMRI and diffusion tensor imaging (DTI) data. Accordingly, we present a new approach to describe and measure the fiber bundle similarity quantitatively within and across subjects which will facilitate the optimization procedure. Experimental results demonstrated that this framework improved the localizations of fMRI-derived ROIs. Through our optimization procedure, structural and functional connectivities were more consistent across different individuals. Overall, the ability to accurately localize network ROIs could facilitate many applications in brain imaging that rely on the accurate identification of ROIs.

Functional principal component model for high-dimensional brain imaging.

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

How Close Are We in Utilizing Functional Neuroimaging in Routine Clinical Diagnosis of Neuropathic Pain?

As with many disorders affecting the central nervous system, treatment of chronic pain is fraught with difficulties related to specific diagnosis and measures of treatment efficacy. Given the recent advances that brain-imaging techniques have contributed to our understanding of how chronic pain affects multiple aspects of brain function (including sensory, emotional, cognitive, and modulatory), opportunities to use these approaches in the clinic are clearly a focus of research laboratories around the world. The routine application of brain imaging as a clinical marker of disease state or therapeutic (drug) efficacy would significantly enhance the clinical process by providing objective measures for clinicians and patients.

Forward solving in Electrical Impedance Tomography with algebraic multigrid wavelet based preconditioners

Electrical Impedance Tomography is a soft-field tomography modality, where image reconstruction is formulated as a non-linear least-squares model fitting problem. The Newton-Rahson scheme is used for actually reconstructing the image, and this involves three main steps: forward solving, computation of the Jacobian, and the computation of the conductivity update. Forward solving relies typically on the finite element method, resulting in the solution of a sparse linear system. In typical three dimensional biomedical applications of EIT, like breast, prostate, or brain imaging, it is desirable to work with sufficiently fine meshes in order to properly capture the shape of the domain, of the electrodes, and to describe the resulting electric filed with accuracy. These requirements result in meshes with 100,000 nodes or more. The solution the resulting forward problems is computationally intensive. We address this aspect by speeding up the solution of the FEM linear system by the use of efficient numeric methods and of new hardware architectures. In particular, in terms of numeric methods, we solve the forward problem using the Conjugate Gradient method, with a wavelet-based algebraic multigrid (AMG) preconditioner. This preconditioner is faster to set up than other AMG preconditoiners which are not based on wavelets, it does use less memory, and provides for a faster convergence. We report results for a MATLAB based prototype algorithm an we discuss details of a work in progress for a GPU implementation.

Magnetic resonance elastography: a general overview of its current and future applications in brain imaging

Magnetic resonance elastography (MRE) has been developed over the last few years as a non-invasive means of evaluating the elasticity of biological tissues. The presence of the skull has always prevented semeiotic palpation of the brain, but MRE now offers the possibility of "palpating by imaging" in order to detect brain consistency under physiological and pathological conditions. The aim of this article is to review the current state-of-the-art of MRE imaging and discuss its possible future diagnostic applications in neuroscience.

Bildgebung der Reorganisation im Sprachsystem nach Schlaganfall

In recent years, application of functional brain imaging to aphasic stroke patients has broadened our understanding of the neural processes underlying recovery from aphasia. This article highlights recent findings from neuroimaging studies of aphasia, focusing on the dynamic process of reorganisation from acute to chronic stroke and points to recent developments which enable the investigation of language reorganisation within a framework of interconnected brain regions.