Estimation of the hemodynamic response of fMRI Data using RBF neural network

Functional magnetic resonance imaging (fMRI) is an important technique for neuroimaging. The conventional system identification methods used in fMRI data analysis assume a linear time-invariant system with the impulse response described by the hemodynamic responses (HDR). However, the measured blood oxygenation level-dependent (BOLD) signals to a particular processing task (for example, rapid event-related fMRI design) show nonlinear properties and vary with different brain regions and subjects. In this paper, radial basis function (RBF) neural network (a powerful technique for modelling nonlinearities) is proposed to model the dynamics underlying the fMRI data. The equivalence of the proposed method to the existing Volterra series method has been demonstrated. It is shown that the first- and second-order Volterra kernels could be deduced from the parameters of the RBF neural network. Studies on both simulated (using Balloon model) as well as real event-related fMRI data show that the proposed method can accurately estimate the HDR of the brain and capture the variations of the HDRs as a function of the brain regions and subjects.     

Extracting Brain Connectivity from Diffusion MRI [Life Sciences]

This article gives a short introduction to dMRI tractography methods. Diffusion magnetic resonance imaging (dMRI) is an MRI modality that has gained tremendous popularity the past five years and is especially promising for imaging the white matter in brain. The dMRI technique has raised hopes in the neuroscience community for a better understanding of the fiber tract anatomy of the human brain. Various methods have been proposed to visualize the anatomy of fiber pathways and to derive connectivity between different parts of the brain in vivo. While there are strong indications that dMRI reveals information about the fiber pathways in the brain, it is important to stress that the explicit measurements are of water diffusion, and not of the axons themselves. This technique allows us to use fibers from multiple brains as input, and thereby obtain a simultaneous clustering and matching of the bundles in all brains.     

Volume rendering of multimodal images: application to MRI and PET imaging of the human brain

A procedure for combining and visualizing complementary structural and functional information from magnetic resonance imaging (MRI) and positron emission tomography (PET) is described. MR and PET images of the human brain were obtained and correlated to form three-dimensional volumes of image data. Volume rendering and solid-texturing concepts were combined to develop a new volume imaging technique for ;volume texture-mapping' brain glucose metabolism (from PET) onto brain anatomy (from MRI). The technique was used to produce sequences of three-dimensional views: these sequences were dynamically displayed in a ;cine-loop' to better visualize the three-dimensional relationship between brain structure and function. The techniques provide a means of presenting vast amounts of multidimensional data in a form that is easily understood, and the resulting images are essential to an understanding of the normal and pathologic states of the human brain.     

An Evaluation of Methods for Neuromagnetic Image Reconstruction

In this paper, we discuss several aspects of a potential new medical imaging modality for producing a quantitative three-dimensional map of neuron current densities associated with brain function. The neuromagnetic image is produced by reconstructing a current dipole field from external magnetic field measurements made with an array of superconducting quantum interference device (SQUID) detectors. This field is produced by numerical inversion of the Biot-Savart equation. The purpose of the work is to investigate fundamental limits on the feasibility of the proposed system under ideal conditions. The following problems are addressed: 1) What are the factors limiting resolution of the system? 2) What is a suitable model for neural activity in the brain? 3) What classes of algorithms are suitable for estimating the model parameters? The major conclusion of this work is that the inversion problem is severely ill-posed and the choice of model and estimation algorithm are crucial in obtaining reasonable solutions. A class of solutions, termed minimum dipole, is proposed as a means of obtaining more acceptable results.     

Spatiotemporal forward solution of the EEG and MEG using network modeling

Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.     

High-Field Magnetic Resonance Imaging With Reduced Field/Tissue RF Artefacts—A Modeling Study Using Hybrid MoM/FEM and FDTD Technique

Due to complex field/tissue interactions, high-field magnetic resonance (MR) images suffer significant image distortions that result in compromised diagnostic quality. A new method that attempts to remove these distortions is proposed in this paper and is based on the use of transceiver-phased arrays. The proposed system uses, in the examples presented herein, a shielded four-element transceive-phased array head coil and involves performing two separate scans of the same slice with each scan using different excitations during transmission. By optimizing the amplitudes and phases for each scan, antipodal signal profiles can be obtained, and by combining both the images together, the image distortion can be reduced several fold. A combined hybrid method of moments (MoM)/finite element method (FEM) and finite-difference time-domain (FDTD) technique is proposed and used to elucidate the concept of the new method and to accurately evaluate the electromagnetic field (EMF) in a human head model. In addition, the proposed method is used in conjunction with the generalized auto-calibrating partially parallel acquisitions (GRAPPA) reconstruction technique to enable rapid imaging of the two scans. Simulation results reported herein for 11-T (470-MHz) brain imaging applications show that the new method with GRAPPA reconstruction theoretically results in improved image quality and that the proposed combined hybrid MoM/FEM and FDTD technique is suitable for high-field magnetic resonance imaging (MRI) numerical analysis     

Reconstruction of conductivity and current density images using only one component of magnetic field measurements

Magnetic resonance current density imaging (MRCDI) is to provide current density images of a subject using a magnetic resonance imaging (MRI) scanner with a current injection apparatus. The injection current generates a magnetic field that we can measure from MR phase images. We obtain internal current density images from the measured magnetic flux densities via Ampere's law. However, we must rotate the subject to acquire all of the three components of the induced magnetic flux density. This subject rotation is impractical in clinical MRI scanners when the subject is a human body. In this paper, we propose a way to eliminate the requirement of subject rotation by careful mathematical analysis of the MRCDI problem. In our new MRCDI technique, we need to measure only one component of the induced magnetic flux density and reconstruct both cross-sectional conductivity and current density images without any subject rotation.     

True energy-minimal and finite-size biplanar gradient coil designfor MRI

Finite-sized high-performance planar magnetic field gradient coils in today's open configuration magnetic resonance imaging (MRI) systems have always been desirable for ever demanding imaging applications. The authors present a Lagrange multiplier technique for designing a minimum-energy gradient coil under a finite-size planar geometry constraint in addition to a set of magnetic field constraints. In this new design methodology, the surface current density on a finite size plane is represented by a two-dimensional (2-D) Fourier series expansion. Following the standard approach, the authors construct a functional F in terms of the stored magnetic energy and a set of field constraint points which are chosen over the desired imaging volume. Minimizing F, the authors obtain the continuous current density distribution for the finite-size planar gradient coil. Applying the stream function technique to the resulting continuous current distribution, the discrete current pattern can be generated. Employing the Biot-Savart law to the discrete current loops, the gradient magnetic field has been re-evaluated in order to validate the theory. Using this approach, the authors have been able to design a finite-size biplanar z-gradient coil which is capable of generating a gradient field of 40 mT /m @ 266 A. The excellent agreement between the analytical and numerical results has been achieved     

Functional Imaging of Spinal Cord Electrical Activity From Its Evoked Magnetic Field

This paper investigates dynamic source imaging of the spinal cord electrophysiological activity from its evoked magnetic field by applying the spatial filter version of standardized low-resolution brain electromagnetic tomography (sLORETA). Our computer simulation shows that the sLORETA-based spatial filter can reconstruct the four current sources typically associated with the elicitation of the spinal cord evoked magnetic field (SCEF). The results from animal experiments show that significant changes in the latency and intensity of the reconstructed volume current arise near the location of the artificial incomplete conduction block. The results from the human SCEF show that the SCEF source imaging can visualize the dynamics of the volume currents and other nerve electrical activity propagating along the human spinal cord. These experimental results demonstrate the potential of SCEF source imaging as a future clinical tool for diagnosing cervical spinal cord disorders.     

Treatment planning of brain implants using vascular information anda new template technique

A new template technique has been developed for implanting hyperthermia catheters in the treatment of brain tumors. The technique utilizes an imaging template and a drill template which can be rigidly secured to the head with three skull screws. The anatomic and vascular information needed for hyperthermia treatment planning may be assessed with three-dimensional magnetic resonance (MR) imaging and angiography acquisitions which use a surface coil. In the companioning treatment planning system the catheter positions and lengths and the electrodes in the catheter can be interactively manipulated relative to the anatomy and vasculature. The visualization of the blood vessels relative to the template allows the minimization of the risk on intracranial hemorrhages. This template technique is useful for any brain tumor implants, especially when a large number of catheters are involved. A phantom test has shown that this procedure has an accuracy in the order of 1 mm provided that the MR-related geometry distortions are minimized     

A computational model for tracking subsurface tissue deformationduring stereotactic neurosurgery

Recent advances in the field of sterotactic neurosurgery have made it possible to coregister preoperative computed tomography (CT) and magnetic resonance (MR) images with instrument locations in the operating field. However, accounting for intraoperative movement of brain tissue remains a challenging problem. While intraoperative CT and MR scanners record concurrent tissue motion, there is motivation to develop methodologies which would be significantly lower in cost and more widely available. The approach the authors present is a computational model of brain tissue deformation that could be used in conjunction with a limited amount of concurrently obtained operative data to estimate subsurface tissue motion. Specifically, the authors report on the initial development of a finite element model of brain tissue adapted from consolidation theory. Validations of the computational mathematics in two and three dimensions are shown with errors of 1%-2% for the discretizations used. Experience with the computational strategy for estimating surgically induced brain tissue motion in vivo is also presented. While the predicted tissue displacements differ from measured values by about 15%, they suggest that exploiting a physics-based computational framework for updating preoperative imaging databases during the course of surgery has considerable merit. However, additional model and computational developments are needed before this approach can become a clinical reality     

磁共振成像中磁场不均匀性的计算机模拟技术

本文提出了一种在不均匀场中对磁共振成进行计算机模拟的技术以及图像恢复算 磁场以及梯度场的误差场分布可以是任意的,计算机模拟的结果表明了这一算地于模拟有误差场存在时的傅里叶－轭式断层成像和自旋卷绕法成像都是非常有效的。     

On Approximation of Orientation Distributions by Means of Spherical Ridgelets

Visualization and analysis of the micro-architecture of brain parenchyma by means of magnetic resonance imaging is nowadays believed to be one of the most powerful tools used for the assessment of various cerebral conditions as well as for understanding the intracerebral connectivity. Unfortunately, the conventional diffusion tensor imaging (DTI) used for estimating the local orientations of neural fibers is incapable of performing reliably in the situations when a voxel of interest accommodates multiple fiber tracts. In this case, a much more accurate analysis is possible using the high angular resolution diffusion imaging (HARDI) that represents local diffusion by its apparent coefficients measured as a discrete function of spatial orientations. In this note, a novel approach to enhancing and modeling the HARDI signals using multiresolution bases of spherical ridgelets is presented. In addition to its desirable properties of being adaptive, sparsifying, and efficiently computable, the proposed modeling leads to analytical computation of the orientation distribution functions associated with the measured diffusion, thereby providing a fast and robust analytical solution for q-ball imaging.      

基于金刚石NV色心的带状线芯片微波场成像

为了满足集成微波器件进行高分辨率微波近场测量的需求,本论文提出了一种基于金刚石氮空位（Nitrogen-Vacancy,NV）色心的微波近场成像技术.该技术可用于查找芯片等集成微波器件的干扰源和信号串扰.此微波近场成像方法采用金刚石NV色心颗粒作为场传感器,其中金刚石颗粒固定在锥形光纤的末端.由于塞曼效应,NV色心的光探测磁共振（Optical Detection Magnetic Resonance,ODMR）谱在外部静磁场环境中会分裂成为8个峰,通过测量共振峰频点的Rabi振荡谱,能够得到Rabi频率,接着通过2.8MHz/Gauss换算得出该处的微波场强度,最后通过将所测得所有数据点进行二维图像处理即可得到所测芯片和集成微波器件的表面微波场近场图像.     

Imaging elastic properties of biological tissues by low-frequency harmonic vibration

The elastic properties of soft tissues are closely related to their structure, biological conditions, and pathology. For years, physicians have used palpation as a crude elasticity measurement tool to diagnose diseases in the human body. Based on this simple concept, but using modern technology, several elasticity imaging schemes have been developed during the past two decades. In this paper, we present two elasticity imaging methods that use a low-frequency (Hz to kHz range) harmonic force to excite the tissue. The first method, called magnetic resonance elastography (MRE), uses a phase sensitive magnetic resonance technique to detect tissue motion. Excitation is usually performed with a mechanical actuator on the surface of the body, although other excitation methods are possible. In the second method, called vibro-acoustography, the radiation force from focused ultrasound is used for excitation in a limited region within the tissue. Tissue motion is detected by measuring the acoustic field emitted by the object in response to the vibration. The resulting images in both methods can be related to the dynamics of the object at the excitation frequency. The spatial resolution of MRE and vibro-acoustography images is in the millimeter and sub-millimeter ranges, respectively. Here, we present the theory and physical principles of MRE and vibro-acoustography and describe their performances. We also present results of experiments on various human tissues, including breast, brain, and vessels. Finally, we discuss potential clinical application of these two imaging methods.     

A Nonresonant Perturbation Theory

This paper presents a theory for a nonresonant perturbation technique for the measurement of electric and magnetic field strengths within a device. Most presently employed perturbation field strength measurements require the use of a resonance technique. In the technique discussed here, reflection coefficient measurements are made at the same frequency with, and without, a perturbing object placed at the point at which the field strength is to be measured. By these data, and by the equations derived and presented in this paper, the desired field strength can be calculated. The technique can be used for cavities that are too lossy to support resonance, and is suitable for cavities for which the resonant field configuration differs from the field configuration to be measured. In addition, this technique has the advantage that it permits the measurement of the phase, as well as the amplitude of the field.     

Analysis of fMRI data using improved self-organizing mapping and spatio-temporal metric hierarchical clustering

The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.      

Measurement of AC magnetic field distribution using magneticresonance imaging

Electric currents are applied to body in numerous applications in medicine such as electrical impedance tomography, cardiac defibrillation, electrocautery, and physiotherapy. If the magnetic field within a region is measured, the currents generating these fields can be calculated using the curl operator. In this study, magnetic fields generated within a phantom by currents passing through an external wire is measured using a magnetic resonance imaging (MRI) system. A pulse sequence that is originally designed for mapping static magnetic field inhomogeneity is adapted. AC current in the form of a burst sine wave is applied synchronously with the pulse sequence. The frequency of the applied current is in the audio range with an amplitude of 175-mA rms. It is shown that each voxel value of sequential images obtained by the proposed pulse sequence is modulated similar to a single-tone broadband frequency modulated (FM) waveform with the AC magnetic field strength determining the modulation index. An algorithm is developed to calculate the AC magnetic field intensity at each voxel using the frequency spectrum of the voxel signal. Experimental results show that the proposed algorithm can be used to calculate AC magnetic field distribution within a conducting sample that is placed in an MRI system     

Inhomogeneity and multiple dimension considerations in magnetic resonance imaging with time-varying gradients

The inhomogeneity of the main magnetic field is a significant factor limiting the performance and increasing the cost of commercial magnetic resonance imaging (MRI) and spectroscopy machines. This is particularly true where shielding is employed to limit fringing fields. In this paper, we investigate the performance of a recently introduced MRI technique using time-varying gradients in the presence of such inhomogeneity. It is shown that the time-varying gradient imaging system can accommodate considerable inhomogeneities (1000 ppm in typical imaging application). The problem of selection of the gradient frequencies for simultaneous multiple dimension imaging in the presence of inhomogeneity is formulated and solved.     

A unified computational framework for deconvolution to reconstruct multiple fibers from diffusion weighted MRI

Diffusion magnetic resonance imaging (MRI) is a relatively new imaging modality which is capable of measuring the diffusion of water molecules in biological systems noninvasively. The measurements from diffusion MRI provide unique clues for extracting orientation information of brain white matter fibers and can be potentially used to infer the brain connectivity in vivo using tractography techniques. Diffusion tensor imaging (DTI), currently the most widely used technique, fails to extract multiple fiber orientations in regions with complex microstructure. In order to overcome this limitation of DTI, a variety of reconstruction algorithms have been introduced in the recent past. One of the key ingredients in several model-based approaches is deconvolution operation which is presented in a unified deconvolution framework in this paper. Additionally, some important computational issues in solving the deconvolution problem that are not addressed adequately in previous studies are described in detail here. Further, we investigate several deconvolution schemes towards achieving stable, sparse, and accurate solutions. Experimental results on both simulations and real data are presented. The comparisons empirically suggest that nonnegative least squares method is the technique of choice for the multifiber reconstruction problem in the presence of intravoxel orientational heterogeneity.