Barry D. Van Veen

Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique.

PURPOSE Breast density measurement has the potential to play an important role in individualized breast cancer risk assessment and prevention decisions. Routine evaluation of breast density will require the availability of a low-cost, nonionizing, three-dimensional (3-D) tomographic imaging modality that exploits a strong properties contrast between dense fibroglandular tissue and less dense adipose tissue. The purpose of this computational study is to investigate the performance of 3-D tomography using low-power microwaves to reconstruct the spatial distribution of breast tissue dielectric properties and to evaluate the modality for application to breast density characterization. METHODS State-of-the-art 3-D numerical breast phantoms that are realistic in both structural and dielectric properties are employed. The test phantoms include one sample from each of four classes of mammographic breast density. Since the properties of these phantoms are known exactly, these testbeds serve as a rigorous benchmark for the imaging results. The distorted Born iterative imaging method is applied to simulated array measurements of the numerical phantoms. The forward solver in the imaging algorithm employs the finite-difference time-domain method of solving the time-domain Maxwells equations, and the dielectric profiles are estimated using an integral equation form of the Helmholtz wave equation. A multiple-frequency, bound-constrained, vector field inverse scattering solution is implemented that enables practical inversion of the large-scale 3-D problem. Knowledge of the frequency-dependent characteristic of breast tissues at microwave frequencies is exploited to obtain a parametric reconstruction of the dispersive dielectric profile of the interior of the breast. Imaging is performed on a high-resolution voxel basis and the solution is bounded by a known range of dielectric properties of the constituent breast tissues. The imaging method is validated using a breast phantom with a single, high-contrast interior scattering target in an otherwise homogeneous interior. The method is then used to image a set of realistic numerical breast phantoms of varied fibroglandular tissue density. RESULTS Imaging results are presented for each numerical phantom and show robustness of the method relative to tissue density. In each case, the distribution of fibroglandular tissues is well represented in the resulting images. The resolution of the images at the frequencies employed is wider than the feature dimensions of the normal tissue structures, resulting in a smearing of their reconstruction. CONCLUSIONS The results of this study support the utility of 3-D microwave tomography for imaging the distribution of normal tissues in the breast, specifically, dense fibroglandular tissue versus less dense adipose tissue, and suggest that further investigation of its use for volumetric evaluation of breast density is warranted.

Space–time event sparse penalization for magneto-/electroencephalography

This article presents a new spatio-temporal method for M/EEG source reconstruction based on the assumption that only a small number of events, localized in space and/or time, are responsible for the measured signal. Each space-time event is represented using a basis function expansion which reflects the most relevant (or measurable) features of the signal. This model of neural activity leads naturally to a Bayesian likelihood function which balances the model fit to the data with the complexity of the model, where the complexity is related to the number of included events. A novel Expectation-Maximization algorithm which maximizes the likelihood function is presented. The new method is shown to be effective on several MEG simulations of neurological activity as well as data from a self-paced finger tapping experiment.

3D computational study of non-invasive patient-specific microwave hyperthermia treatment of breast cancer

Non-invasive microwave hyperthermia treatment of breast cancer is investigated using three-dimensional (3D) numerical breast phantoms with anatomical and dielectric-properties realism. 3D electromagnetic and thermal finite-difference time-domain simulations are used to evaluate the focusing and selective heating efficacy in four numerical breast phantoms with different breast tissue densities. Beamforming is used to design and focus the signals transmitted by an antenna array into the breast. We investigate the use of propagation models of varying fidelity and complexity in the design of the transmitted signals. An ideal propagation model that is exactly matched to the actual patients breast is used to establish a best-performance baseline. Simpler patient-specific propagation models based on a homogeneous breast interior are also explored to evaluate the robustness of beamforming in practical clinical settings in which an ideal propagation model is not available. We also investigate the performance of the beamformer as a function of operating frequency and compare single-frequency and multiple-frequency focusing strategies. Our study suggests that beamforming is a robust method of non-invasively focusing microwave energy at a tumor site in breasts of varying volume and breast tissue density.

Reversal of cortical information flow during visual imagery as compared to visual perception.

The role of bottom-up and top-down connections during visual perception and the formation of mental images was examined by analyzing high-density EEG recordings of brain activity using two state-of-the-art methods for assessing the directionality of cortical signal flow: state-space Granger causality and dynamic causal modeling. We quantified the directionality of signal flow in an occipito-parieto-frontal cortical network during perception of movie clips versus mental replay of the movies and free visual imagery. Both Granger causality and dynamic causal modeling analyses revealed an increased top-down signal flow in parieto-occipital cortices during mental imagery as compared to visual perception. These results are the first direct demonstration of a reversal of the predominant direction of cortical signal flow during mental imagery as compared to perception.

Contrast-enhanced microwave breast imaging

Tomographic maps of the dielectric distribution of breast tissue can be made at microwave frequencies by applying nonlinear optimization techniques to the electromagnetic inverse scattering problem. There is a mismatch between the resolution of UHF band microwaves and the feature size of fibroconnective and glandular breast tissues which fundamentally limits the ability of such imaging systems to clearly resolve these structures. Tumor detection is further challenged by the small intrinsic contrast between the dielectric properties of normal and malignant glandular tissues. The use of contrast agents to preferentially alter the properties of malignant tissues is a potential approach to improving detection performance. In this paper, we explore the information available to contrast-enhanced imaging of realistic numerical breast phantoms at microwave frequencies. Differential images are produced using three-dimensional tomographic reconstructions of the dielectric profiles before and after the introduction of a contrast agent to a malignant inclusion.

A Computational Study of Time Reversal Techniques for Ultra-Wideband Microwave Hyperthermia Treatment of Breast Cancer

We present a computational study of the application of time reversal (TR) principles to microwave hyperthermia treatment of breast cancer. A wideband source is excited at the tumor location (the desired focus) in an electromagnetic (EM) simulation based on the finite-difference time-domain (FDTD) method and the transmitted wave is recorded at multiple antenna locations. The FDTD-computed signals are time reversed for transmission into the breast. The same set of FDTD-computed signals is also used in a comparative investigation of a space-time beamforming technique, which has been previously studied for microwave hyperthermia. We discuss the relation between these two approaches, and compare the focusing efficacy and heating selectivity of the TR and beamforming approaches using FDTD EM and thermal simulations with anatomically realistic numerical breast phantoms. Promising results from both methods are obtained.

Efficient methods for identification of Volterra filter models

Abstract A major drawback of the truncated Volterra series or ‘Volterra filter’ for system identification is the large number of parameters required by the standard filter structure. The corresponding estimation problem requires the solution of a large system of simultaneous linear equations. Two methods for simplifying the estimation problem are discussed in this paper. First, a Kronecker product structure for the Volterra filter is reviewed. In this approach the inverse of the large correlation matrix is expressed as a Kronecker product of small matrices. Second, parallel decomposition of the Volterra filter based on uncorrelated, symmetric inputs is introduced. Here the Volterra filter is decomposed into a parallel combination of smaller orthogonal ‘subfilters’. It is shown that each subfilter is much smaller than the full Volterra filter and hence the parallel decomposition offers many advantages for estimating the Volterra kernels. Simulations illustrate application of the parallel structure with random and pseudorandom excitations. Input conditions that guarantee the existence of a unique estimate are also reviewed.

Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

The cingulate cortex is regarded as the backbone of structural and functional connectivity of the brain. While its functional connectivity has been intensively studied, little is known about its effective connectivity, its modulation by behavioral states, and its involvement in cognitive performance. Given the previously reported effects on cingulate functional connectivity, we investigated how eye-closure and sleep deprivation changed cingulate effective connectivity, estimated from resting-state high-density electroencephalography (EEG) using a novel method to calculate Granger Causality directly in source space. Effective connectivity along the cingulate cortex was dominant in the forward direction. Eyes-open connectivity in the forward direction was greater compared to eyes-closed, in well-rested participants. The difference between eyes-open and eyes-closed connectivity was attenuated and no longer significant after sleep deprivation. Individual variability in the forward connectivity after sleep deprivation predicted subsequent task performance, such that those subjects who showed a greater increase in forward connectivity between the eyes-open and the eyes-closed periods also performed better on a sustained attention task. Effective connectivity in the opposite, backward, direction was not affected by whether the eyes were open or closed or by sleep deprivation. These findings indicate that the effective connectivity from posterior to anterior cingulate regions is enhanced when a well-rested subject has his eyes open compared to when they are closed. Sleep deprivation impairs this directed information flow, proportional to its deleterious effect on vigilance. Therefore, sleep may play a role in the maintenance of waking effective connectivity.

Sensitivity of the Distorted Born Iterative Method to the Initial Guess in Microwave Breast Imaging

The distorted Born iterative method (DBIM) has been explored recently for microwave breast imaging. DBIM is an iterative method; thus, it requires an initial guess of the dielectric properties of the breast. In this paper, we study the sensitivity of DBIM imaging accuracy and convergence speed to the properties of a homogeneous initial guess. We conduct this investigation for a multifrequency formulation of DBIM, wherein the dispersive breast tissue properties are described with a Debye model. The parameters of the Debye model for the initial guess are linearly linked to reflect typical breast tissue dielectric properties. The static permittivity characterizing the properties is swept over an appropriate range of initial guesses and DBIM is used to obtain imaging results for each guess. Image quality and the number of iterations required for convergence is evaluated using three-dimensional (3-D) anatomically realistic numerical breast phantoms. This investigation not only vividly illustrates the sensitivity of DBIM to the initial guess but also definitively demonstrates that the use of initial values close to the average properties of the breast yields near-optimal performance. Finally, we present and evaluate a practical algorithm for estimating the average properties to be used as the initial guess.

Multivariate autoregressive models with exogenous inputs for intracerebral responses to direct electrical stimulation of the human brain

A multivariate autoregressive (MVAR) model with exogenous inputs (MVARX) is developed for describing the cortical interactions excited by direct electrical current stimulation of the cortex. Current stimulation is challenging to model because it excites neurons in multiple locations both near and distant to the stimulation site. The approach presented here models these effects using an exogenous input that is passed through a bank of filters, one for each channel. The filtered input and a random input excite a MVAR system describing the interactions between cortical activity at the recording sites. The exogenous input filter coefficients, the autoregressive coefficients, and random input characteristics are estimated from the measured activity due to current stimulation. The effectiveness of the approach is demonstrated using intracranial recordings from three surgical epilepsy patients. We evaluate models for wakefulness and NREM sleep in these patients with two stimulation levels in one patient and two stimulation sites in another resulting in a total of 10 datasets. Excellent agreement between measured and model-predicted evoked responses is obtained across all datasets. Furthermore, one-step prediction is used to show that the model also describes dynamics in pre-stimulus and evoked recordings. We also compare integrated information—a measure of intracortical communication thought to reflect the capacity for consciousness—associated with the network model in wakefulness and sleep. As predicted, higher information integration is found in wakefulness than in sleep for all five cases.