A new family of high-resolution multivariate spectral estimators

In this paper, we extend the Beta divergence family to multivariate power spectral densities. Similarly to the scalar case, we show that it smoothly connects the multivariate Kullback-Leibler divergence with the multivariate Itakura-Saito distance. We successively study a spectrum approximation problem, based on the Beta divergence family, which is related to a multivariate extension of the THREE spectral estimation technique. It is then possible to characterize a family of solutions to the problem. An upper bound on the complexity of these solutions will also be provided. Simulations suggest that the most suitable solution of this family depends on the specific features required from the estimation problem

A globally convergent matricial algorithm for multivariate spectral estimation

In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as MATLAB's PEM and MATLAB's N4SID

Time and spectral domain relative entropy: A new approach to multivariate spectral estimation

The concept of spectral relative entropy rate is introduced for jointly stationary Gaussian processes. Using classical information-theoretic results, we establish a remarkable connection between time and spectral domain relative entropy rates. This naturally leads to a new spectral estimation technique where a multivariate version of the Itakura-Saito distance is employed}. It may be viewed as an extension of the approach, called THREE, introduced by Byrnes, Georgiou and Lindquist in 2000 which, in turn, followed in the footsteps of the Burg-Jaynes Maximum Entropy Method. Spectral estimation is here recast in the form of a constrained spectrum approximation problem where the distance is equal to the processes relative entropy rate. The corresponding solution entails a complexity upper bound which improves on the one so far available in the multichannel framework. Indeed, it is equal to the one featured by THREE in the scalar case. The solution is computed via a globally convergent matricial Newton-type algorithm. Simulations suggest the effectiveness of the new technique in tackling multivariate spectral estimation tasks, especially in the case of short data records.Comment: 32 pages, submitted for publicatio

High-Frequency Rapid B-Mode Ultrasound Imaging for Real-Time Monitoring of Lesion Formation and Gas Body Activity During High-Intensity Focused Ultrasound Ablation

Abstract: The goal of this study was to examine the ability of high-frame-rate, high-resolution imaging to monitor tissue necrosis and gas-body activities formed during high-intensity focused ultrasound (HIFU) application. Ex vivo porcine cardiac tissue specimens (n = 24) were treated with HIFU exposure (4.33 MHz, 77 to 130 Hz pulse repetition frequency (PRF), 25 to 50% duty cycle, 0.2 to 1 s, 2600 W/cm2). RF data from Bmode ultrasound imaging were obtained before, during, and after HIFU exposure at a frame rate ranging from 77 to 130 Hz using an ultrasound imaging system with a center frequency of 55 MHz. The time history of changes in the integrated backscatter (IBS), calibrated spectral parameters, and echo-decorrelation parameters of the RF data were assessed for lesion identification by comparison against gross sections. Temporal maximum IBS with +12 dB threshold achieved the best identification with a receiver-operating characteristic (ROC) curve area of 0.96. Frame-to-frame echo decorrelation identified and tracked transient gas-body activities. Macroscopic (millimetersized) cavities formed when the estimated initial expansion rate of gas bodies (rate of expansion in lateral-to-beam direction) crossed 0.8 mm/s. Together, these assessments provide a method for monitoring spatiotemporal evolution of lesion and gas-body activity and for predicting macroscopic cavity formation

High-Frequency Ultrasound M-Mode Imaging for Identifying Lesion and Bubble Activity During High-Intensity Focused Ultrasound Ablation

Effective real-time monitoring of high-intensity focused ultrasound (HIFU) ablation is important for application of HIFU technology in interventional electrophysiology. This study investigated rapid, high-frequency M-mode ultrasound imaging for monitoring spatiotemporal changes during HIFU application. HIFU (4.33 MHz, 1 kHz PRF, 50% duty cycle, 1 s, 2600 – 6100 W/cm2 ) was applied to ex-vivo porcine cardiac tissue specimens with a confocally and perpendicularly aligned high-frequency imaging system (Visualsonics Vevo 770, 55 MHz center frequency). Radiofrequency (RF) data from M-mode imaging (1 kHz PRF, 2 s × 7 mm) was acquired before, during, and after HIFU treatment (n = 12). Among several strategies, the temporal maximum integrated backscatter with a threshold of +12 dB change showed the best results for identifying final lesion width (receiver-operating characteristic curve area 0.91 ± 0.04, accuracy 85 ± 8%, as compared to macroscopic images of lesions). A criterion based on a line-to-line decorrelation coefficient is proposed for identification of transient gas bodies

A Framework for Temperature Imaging using the Change in Backscattered Ultrasonic Signals

Hyperthermia is a cancer treatment that elevates tissue temperature to 40 to 43oC. It would benefit from a non-invasive, safe, inexpensive and convenient thermometry to monitor heating patterns. Ultrasound is a modality that meets these requirements. In our initial work, using both prediction and experimental data, we showed that the change in the backscattered energy: CBE) is a potential parameter for TI. CBE, however, was computed in a straightforward yet ad hoc manner. In this work, we developed and exploited a mathematical representation for our approach to TI to optimize temperature accuracy. Non-thermal effects of noise and motion confound the use of CBE. Assuming additive white Gaussian noise, we applied signal averaging and thresholding to reduce noise effects. Our motion compensation algorithms were also applied to images with known motion to evaluate factors affecting the compensation performance. In the framework development, temperature imaging was modeled as a problem of estimating temperature from the random processes resulting from thermal changes in signals. CBE computation was formalized as a ratio between two random variables. Mutual information: MI) was studied as an example of possible parameters for temperature imaging based on the joint distributions. Furthermore, a maximum likelihood estimator: MLE) was developed. Both simulations and experimental results showed that noise effects were reduced by signal averaging. The motion compensation algorithms proved to be able to compensate for motion in images and were improved by choosing appropriate interpolation methods and sample rates. For images of uniformly distributed scatterers, CBE and MI can be computed independent of SNR to improve the temperature accuracy. The application of the MLE also showed improvements in temperature accuracy compared to the energy ratio from the signal mean in simulations. The application of the framework to experimental data requires more work to implement noise reduction approaches in 3D heating experiments. The framework identified ways in which we were able to reduce the effects of both noise and motion. The framework formalized our approaches to temperature imaging, improved temperature accuracy in simulations, and can be applied to experimental data if the noise reduction approaches can be implemented for 3D experiments

Spectral Ultrasound Characterization of Tissues and Tissue Engineered Constructs.

Even though ultrasound imaging is widely used in clinical diagnosis and image-guided interventions, the field is far behind other areas of clinical quantitative image analysis, such as MRI, CT and X-ray mammography. In this thesis, non-destructive and non-invasive ultrasound characterization techniques were developed to study the tissue micro-structural details using high frequency spectral ultrasound imaging (SUSI). The techniques were explored in in-vitro conditions of acellular and cellular tissue engineered constructs and then on ex-vivo tissues for their characterization. SUSI was used to assess the amount of hydroxyl-apatite (HA) mineral, differentiate HA mineral types and study their distribution in acellular tissue engineered constructs. The process of mineral deposition from surrounding mineralizing media onto simple collagen constructs was also studied and characterized with SUSI. 3D morphological changes of the constructs with MC3t3 cells was monitored and characterized for the developmental changes such as net cell proliferation/apoptosis and cell differentiation process through mineral production by the early osteoblastic MC3t3-cell constructs in-situ. A novel method was introduced using SUSI to estimate the amount of mineral secreted by the differentiated osteoblast cells in a non-destructive method. Then, SUSI was investigated in ex-vivo cardiac tissues to monitor and characterize the cellular changes during high-intensity focused ultrasound ablation with high-frame-rate and high-resolution ultrasound imaging. The mechanistic hypotheses behind the improvement in lesion detection were investigated and best identification methods to assess lesion formation and transient gas body activities were proposed to provide a method for visualizing spatiotemporal evolution of lesion and gas–body activity and for predicting macroscopic cavity formation upon its implementation as a real-time monitoring technique with feedback control system for HIFU treatment of atrial fibrillation to improve the ablation process. Even though the results from the developed techniques show great promise in in-vitro and ex-vivo settings, additional work needs to be carried out to demonstrate the applicability of the techniques in in-vivo.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99788/1/msreddy_1.pd