A Pilot Study of the Impact of Microwave Ablation on the Dielectric Properties of Breast Tissue.

Percutaneous microwave ablation (MWA) is a promising technology for patients with breast cancer, as it may help treat individuals who have less aggressive cancers or do not respond to targeted therapies in the neoadjuvant or pre-surgical setting. In this study, we investigate changes to the microwave dielectric properties of breast tissue that are induced by MWA. While similar changes have been characterized for relatively homogeneous tissues, such as liver, those prior results are not directly translatable to breast tissue because of the extreme tissue heterogeneity present in the breast. This study was motivated, in part by the expectation that the changes in the dielectric properties of the microwave antenna's operation environment will be impacted by tissue composition of the ablation target, which includes not only the tumor, but also its margins. Accordingly, this target comprises a heterogeneous mix of malignant, healthy glandular, and adipose tissue. Therefore, knowledge of MWA impact on breast dielectric properties is essential for the successful development of MWA systems for breast cancer. We performed ablations in 14 human ex-vivo prophylactic mastectomy specimens from surgeries that were conducted at the UW Hospital and monitored the temperature in the vicinity of the MWA antenna during ablation. After ablation we measured the dielectric properties of the tissue and analyzed the tissue samples to determine both the tissue composition and the extent of damage due to the ablation. We observed that MWA induced cell damage across all tissue compositions, and found that the microwave frequency-dependent relative permittivity and conductivity of damaged tissue are lower than those of healthy tissue, especially for tissue with high fibroglandular content. The results provide information for future developments on breast MWA systems.

Assessing recurrent interactions in cortical networks: Modeling EEG response to transcranial magnetic stimulation.

BACKGROUND: The basic mechanisms underlying the electroencephalograpy (EEG) response to transcranial magnetic stimulation (TMS) of the human cortex are not well understood. NEW METHOD: A state-space modeling methodology is developed to gain insight into the network nature of the TMS/EEG response. Cortical activity is modeled using a multivariariate autoregressive model with exogenous stimulation parameters representing the effect of TMS. An observation equation models EEG measurement of cortical activity. An expectation-maximization algorithm is developed to estimate the model parameters. RESULTS: The methodology is used to assess two different hypotheses for the mechanisms underlying TMS/EEG in wakefulness and sleep. The integrated model hypothesizes that recurrent interactions between cortical regions are the source of TMS/EEG, while the segregated model hypothesizes that the TMS/EEG results from excitation of independent cortical oscillators. The results show that the relatively simple EEG response to TMS recorded during non-rapid-eye-movement sleep is described equally well by either the integrated or segregated model. However, the integrated model fits the more complex TMS/EEG of wakefulness much better than the segregated model. COMPARISON WITH EXISTING METHOD(S): Existing methods are limited to small numbers of cortical regions of interest or do not represent the effect of TMS. Our results are consistent with previous studies contrasting the complexity of TMS/EEG in wakefulness and sleep. CONCLUSION: The new method strongly suggests that effective feedback connections between cortical regions are required to produce the TMS/EEG in wakefulness.

Modeling Touch and Palpation Using Autoregressive Models.

OBJECTIVE: The human haptic system uses a set of reproducible and subconscious hand maneuvers to identify objects. Similar subconscious maneuvers are used during medical palpation for screening and diagnosis. The goal of this work was to develop a mathematical model that can be used to describe medical palpation techniques. METHODS: Palpation data were measured using a two-dimensional array of force sensors. A novel algorithm for estimating the hand position from force data was developed. The hand position data were then modeled using multivariate autoregressive models. Analysis of these models provided palpation direction and frequency as well as palpation type. The models were tested and validated using three different data sets: simulated data, a simplified experiment in which participant followed a known pattern, and breast simulator palpation data. RESULTS: Simulated data showed that the minimal error in estimating palpation direction and frequency is achieved when the sampling frequency is five to ten times the palpation frequency. The classification accuracy was for the simplified experiment and for the breast simulator data. CONCLUSION: Proper palpation is one of the vital components of many hands-on clinical examinations. In this study, an algorithm for characterizing medical palpation was developed. The algorithm measured palpation frequency and direction for the first time and provided classification of palpation type. SIGNIFICANCE: These newly developed models can be used for quantifying and assessing clinical technique, and consequently, lead to improved performance in palpation-based exams. Furthermore, they provide a general tool for the study of human haptics.

Fetal QT Interval Estimation Using Sequential Hypothesis Testing.

Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement. Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation. Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms). Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference. Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement. Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation. Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms). Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference. Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.

A 3-D Level Set Method for Microwave Breast Imaging.

OBJECTIVE: Conventional inverse-scattering algorithms for microwave breast imaging result in moderate resolution images with blurred boundaries between tissues. Recent 2-D numerical microwave imaging studies demonstrate that the use of a level set method preserves dielectric boundaries, resulting in a more accurate, higher resolution reconstruction of the dielectric properties distribution. Previously proposed level set algorithms are computationally expensive, and thus, impractical in 3-D. In this paper, we present a computationally tractable 3-D microwave imaging algorithm based on level sets. METHODS: We reduce the computational cost of the level set method using a Jacobian matrix, rather than an adjoint method, to calculate Frechet derivatives. We demonstrate the feasibility of 3-D imaging using simulated array measurements from 3-D numerical breast phantoms. We evaluate performance by comparing full 3-D reconstructions to those from a conventional microwave imaging technique. We also quantitatively assess the efficacy of our algorithm in evaluating breast density. RESULTS: Our reconstructions of 3-D numerical breast phantoms improve upon those of a conventional microwave imaging technique. The density estimates from our level set algorithm are more accurate than those of the conventional microwave imaging, and the accuracy is greater than that reported for mammographic density estimation. CONCLUSION: Our level set method leads to a feasible level of computational complexity for full 3-D imaging, and reconstructs the heterogeneous dielectric properties distribution of the breast more accurately than conventional microwave imaging methods. SIGNIFICANCE: 3-D microwave breast imaging using a level set method is a promising low-cost, nonionizing alternative to current breast imaging techniques.

Context-specific differences in fronto-parieto-occipital effective connectivity during short-term memory maintenance.

Although visual short-term memory (VSTM) performance has been hypothesized to rely on two distinct mechanisms, capacity and filtering, the two have not been dissociated using network-level causality measures. Here, we hypothesized that behavioral tasks challenging capacity or distraction filtering would both engage a common network of areas, namely dorsolateral prefrontal cortex (dlPFC), superior parietal lobule (SPL), and occipital cortex, but would do so according to dissociable patterns of effective connectivity. We tested this by estimating directed connectivity between areas using conditional Granger causality (cGC). Consistent with our prediction, the results indicated that increasing mnemonic load (capacity) increased the top-down drive from dlPFC to SPL, and cGC in the alpha (8-14Hz) frequency range was a predominant component of this effect. The presence of distraction during encoding (filtering), in contrast, was associated with increased top-down drive from dlPFC to occipital cortices directly and from SPL to occipital cortices directly, in both cases in the beta (15-25Hz) range. Thus, although a common anatomical network may serve VSTM in different contexts, it does so via specific functions that are carried out within distinct, dynamically configured frequency channels.

Beamforming-Enhanced Inverse Scattering for Microwave Breast Imaging.

We present a focal-beamforming-enhanced formulation of the distorted Born iterative method (DBIM) for microwave breast imaging. Incorporating beamforming into the imaging algorithm has the potential to mitigate the effect of noise on the image reconstruction. We apply the focal-beamforming-enhanced DBIM algorithm to simulated array measurements from two MRI-derived, anatomically realistic numerical breast phantoms and compare its performance to that of the DBIM formulated with two non-focal schemes. The first scheme simply averages scattered field data from reciprocal antenna pairs while the second scheme discards reciprocal pairs. Images of the dielectric properties are reconstructed for signal-to-noise ratios (SNR) ranging from 35 dB down to 0 dB. We show that, for low SNR, the focal beamforming algorithm creates reconstructions that are of higher fidelity with respect to the exact dielectric profiles of the phantoms as compared to reconstructions created using the non-focal schemes. At high SNR, the focal and non-focal reconstructions are of comparable quality.

Characterizing touch using pressure data and auto regressive models.

Palpation plays a critical role in medical physical exams. Despite the wide range of exams, there are several reproducible and subconscious sets of maneuvers that are common to examination by palpation. Previous studies by our group demonstrated the use of manikins and pressure sensors for measuring and quantifying how physicians palpate during different physical exams. In this study we develop mathematical models that describe some of these common maneuvers. Dynamic pressure data was measured using a simplified testbed and different autoregressive models were used to describe the motion of interest. The frequency, direction and type of motion used were identified from the models. We believe these models can a provide better understanding of how humans explore objects in general and more specifically give insights to understand medical physical exams.

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.

Detection of T-wave alternans in fetal magnetocardiography using the generalized likelihood ratio test.

T-wave alternans (TWA) is an indicator of cardiac instability and is associated with life-threatening ventricular arrhythmias. Detection of TWA in the adult has been widely investigated and is used routinely for cardiac risk assessment. Detection of TWA in the fetus, however, is much more difficult due to the low amplitude and variable configuration of the signal, the presence of strong interferences, and the brevity of fetal TWA episodes. In this paper, we present a statistical detector based on the generalized likelihood ratio test that is designed for detection of TWA in the fetus. The performance of the detector is evaluated by constructing receiver-operator characteristic curves, using simulated data and real data from subjects with macroscopic TWA. The detector is capable of detecting TWA episodes as brief as 20 beats. The detection performance is improved significantly by modeling the fetal T-wave as a low-rank, low bandwidth signal, and using maximum likelihood estimation to estimate the model parameters. This approach enables all of the data to be used to estimate the noise statistics, providing highly effective suppression of interference, including maternal interference. The method is suitable for routine use because it can be applied to raw, unprocessed recordings, allowing automated analysis of extended fetal magnetocardiography recordings.

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.

Interhemispheric effective and functional cortical connectivity signatures of spina bifida are consistent with callosal anomaly.

The impact of the posterior callosal anomalies associated with spina bifida on interhemispheric cortical connectivity is studied using a method for estimating cortical multivariable autoregressive models from scalp magnetoencephalography data. Interhemispheric effective and functional connectivity, measured using conditional Granger causality and coherence, respectively, is determined for the anterior and posterior cortical regions in a population of five spina bifida and five control subjects during a resting eyes-closed state. The estimated connectivity is shown to be consistent over the randomly selected subsets of the data for each subject. The posterior interhemispheric effective and functional connectivity and cortical power are significantly lower in the spina bifida group, a result that is consistent with posterior callosal anomalies. The anterior interhemispheric effective and functional connectivity are elevated in the spina bifida group, a result that may reflect compensatory mechanisms. In contrast, the intrahemispheric effective connectivity is comparable in the two groups. The differences between the spina bifida and control groups are most significant in the θ and α bands.

MRI-Derived 3-D-Printed Breast Phantom for Microwave Breast Imaging Validation.

We propose a 3-D-printed breast phantom for use in preclinical experimental microwave imaging studies. The phantom is derived from an MRI of a human subject; thus, it is anthropomorphic, and its interior is very similar to an actual distribution of fibroglandular tissues. Adipose tissue in the breast is represented by the solid plastic (printed) regions of the phantom, while fibroglandular tissue is represented by liquid-filled voids in the plastic. The liquid is chosen to provide a biologically relevant dielectric contrast with the printed plastic. Such a phantom enables validation of microwave imaging techniques. We describe the procedure for generating the 3-D-printed breast phantom and present the measured dielectric properties of the 3-D-printed plastic over the frequency range 0.5-3.5 GHz. We also provide an example of a suitable liquid for filling the fibroglandular voids in the plastic.

Quantitative Microwave Imaging of Realistic Numerical Breast Phantoms Using an Enclosed Array of Multiband, Miniaturized Patch Antennas.

We present a 3-D microwave breast imaging study in which we reconstruct the dielectric profiles of MRI-derived numerical breast phantoms from simulated array measurements using an enclosed array of multiband, miniaturized patch antennas. The array is designed to overcome challenges relating to the ill-posed nature of the inverse scattering system. We use a multifrequency formulation of the distorted Born iterative method to image four normal-tissue breast phantoms, each corresponding to a different density class. The reconstructed fibroglandular distributions are very faithful to the true distributions in location and basic shape. These results establish the feasibility of using an enclosed array of miniaturized, multiband patch antennas for quantitative microwave breast imaging.

Dielectric characterization of PCL-based thermoplastic materials for microwave diagnostic and therapeutic applications.

We propose the use of a polycaprolactone (PCL)-based thermoplastic mesh as a tissue-immobilization interface for microwave imaging and microwave hyperthermia treatment. An investigation of the dielectric properties of two PCL-based thermoplastic materials in the frequency range of 0.5-3.5 GHz is presented. The frequency-dependent dielectric constant and effective conductivity of the PCL-based thermoplastics are characterized using measurements of microstrip transmission lines fabricated on substrates comprised of the thermoplastic meshes. We also examine the impact of the presence of a PCL-based thermoplastic mesh on microwave breast imaging. We use a numerical test bed comprised of a previously reported 3-D anatomically realistic breast phantom and a multi-frequency microwave inverse scattering algorithm. We demonstrate that the PCL-based thermoplastic material and the assumed biocompatible medium of vegetable oil are sufficiently well matched such that the PCL layer may be neglected by the imaging solution without sacrificing imaging quality. Our results suggest that PCL-based thermoplastics are promising materials as tissue immobilization structures for microwave diagnostic and therapeutic applications.

Cross validation for selection of cortical interaction models from scalp EEG or MEG.

A cross-validation (CV) method based on state-space framework is introduced for comparing the fidelity of different cortical interaction models to the measured scalp electroencephalogram (EEG) or magnetoencephalography (MEG) data being modeled. A state equation models the cortical interaction dynamics and an observation equation represents the scalp measurement of cortical activity and noise. The measured data are partitioned into training and test sets. The training set is used to estimate model parameters and the model quality is evaluated by computing test data innovations for the estimated model. Two CV metrics normalized mean square error and log-likelihood are estimated by averaging over different training/test partitions of the data. The effectiveness of this method of model selection is illustrated by comparing two linear modeling methods and two nonlinear modeling methods on simulated EEG data derived using both known dynamic systems and measured electrocorticography data from an epilepsy patient.

A TSVD analysis of microwave inverse scattering for breast imaging.

A variety of methods have been applied to the inverse scattering problem for breast imaging at microwave frequencies. While many techniques have been leveraged toward a microwave imaging solution, they are all fundamentally dependent on the quality of the scattering data. Evaluating and optimizing the information contained in the data are, therefore, instrumental in understanding and achieving optimal performance from any particular imaging method. In this paper, a method of analysis is employed for the evaluation of the information contained in simulated scattering data from a known dielectric profile. The method estimates optimal imaging performance by mapping the data through the inverse of the scattering system. The inverse is computed by truncated singular-value decomposition of a system of scattering equations. The equations are made linear by use of the exact total fields in the imaging volume, which are available in the computational domain. The analysis is applied to anatomically realistic numerical breast phantoms. The utility of the method is demonstrated for a given imaging system through the analysis of various considerations in system design and problem formulation. The method offers an avenue for decoupling the problem of data selection from the problem of image formation from that data.

Causal Network Inference Via Group Sparse Regularization.

This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a "false connection score" ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach.

Microwave beamforming for non-invasive patient-specific hyperthermia treatment of pediatric brain cancer.

We present a numerical study of an array-based microwave beamforming approach for non-invasive hyperthermia treatment of pediatric brain tumors. The transmit beamformer is designed to achieve localized heating-that is, to achieve constructive interference and selective absorption of the transmitted electromagnetic waves at the desired focus location in the brain while achieving destructive interference elsewhere. The design process takes into account patient-specific and target-specific propagation characteristics at 1 GHz. We evaluate the effectiveness of the beamforming approach using finite-difference time-domain simulations of two MRI-derived child head models from the Virtual Family (IT'IS Foundation). Microwave power deposition and the resulting steady-state thermal distribution are calculated for each of several randomly chosen focus locations. We also explore the robustness of the design to mismatch between the assumed and actual dielectric properties of the patient. Lastly, we demonstrate the ability of the beamformer to suppress hot spots caused by pockets of cerebrospinal fluid (CSF) in the brain. Our results show that microwave beamforming has the potential to create localized heating zones in the head models for focus locations that are not surrounded by large amounts of CSF. These promising results suggest that the technique warrants further investigation and development.

Time-multiplexed beamforming for noninvasive microwave hyperthermia treatment.

A noninvasive microwave beamforming strategy is proposed for selective localized heating of biological tissue. The proposed technique is based on time multiplexing of multiple beamformers. We investigate the effectiveness of the time-multiplexed beamforming in the context of brain hyperthermia treatment by using a high-fidelity numerical head phantom of an adult female from the Virtual Family (IT'IS Foundation) as our testbed. An operating frequency of 1 GHz is considered to balance the improved treatment resolution afforded by higher frequencies against the increased penetration through the brain afforded by lower frequencies. The exact head geometry and dielectric properties of biological tissues in the head are assumed to be available for the creation of patient-specific propagation models used in beamformer design. Electromagnetic and thermal simulations based on the finite-difference time-domain method are used to evaluate the hyperthermia performance of time-multiplexed beamforming and conventional beamforming strategies. The proposed time-multiplexing technique is shown to reduce the unintended heating of healthy tissue without affecting the treatment temperature or volume. The efficacy of the method is demonstrated for target locations in three different regions of the brain. This approach has the potential to improve microwave-induced localized heating for cancer treatment via hyperthermia or heat-activated chemotherapeutic drug release.