Tissue characterization by ultrasound: a study of tissue-mimicking materials and quantitative ultrasonics

Ultrasound applications to the fields of medicine, agriculture, and food are relatively recent developments. In medicine, ultrasound imaging techniques non-invasively obtain information about size and structure of the tissues, and functions of the organs of the body. The research presented in this dissertation involves two important aspects of the ultrasonic imaging, system calibration for quality assurance and tissue characterization. The first part presents the design of tissue-mimicking material for ultrasonic experiments and system calibration. The second part presents results on ultrasonic tissue characterization applied to quality grading of beef;Calibration of ultrasonic system with tissue-mimicking materials is an important part in quality assurance. Also, such materials aid researchers in developing new techniques for imaging and tissue characterization. A part of this research, presented in the first part of the dissertation, was to develop soft-tissue mimicking materials. A method of constructing gelatin based soft-tissue mimicking materials with desired ultrasonic properties was developed. Several materials in different proportions were tried in preliminary experiments for their usefulness as tissue mimicking phantoms. An optimum combination was then derived for the ultrasonic properties (velocity, attenuation and backscatter) in the ranges for the soft-tissues;Ultrasonic tissue characterization involves determination of propagation characteristics of ultrasonic energy in the tissues. In recent years, many ultrasonic parameters, including velocity, attenuation, and scattering, have been found to have potential for tissue characterization. Advanced signal processing and pattern recognition techniques are applied to extract information about particular parameters. As a part of a project on ultrasonic meat quality grading at Iowa State University, several tissue samples were scanned and data were analyzed. Some encouraging results are presented in the second part of the dissertation. This report describes efforts in ultrasonic evaluation of fat marbling in the rib-eye muscle of beef carcass. The development of a regression model for prediction of %fat is discussed. Also, a statistical pattern recognition approach used for classifying the grades of marbling is presented. A simple but accurate classification scheme using linear discriminant analysis has been derived for assigning the marbling grades to the rib-eye samples. This scheme employed easily calculated parameters from the spectrum of the backscattered ultrasonic signal. In the meat industry, this could be applied to differentiate (and ultimately, to grade) meat samples with varying contents and distribution of fat and muscle tissues. A similar approach could be applied for non-invasive characterization and differentiation of infiltrative diseases of organs

Non-invasive Diagnosis of Fatty Liver and Degree of Fatty Liver in Dairy Cows by Digital Analyses of Hepatic Ultrasonograms

The data demonstrate that digital analyses of liver ultrasonograms could diagnose fatty liver and degree of fatty liver (healthy liver, moderate fatty liver, and severe fatty liver with their ranges of 0–8, 8–12, \u3e12% lipids of liver wet weight) with an accuracy of over 90%. Total lipid concentrations could be predicted for liver samples \u3c8% of liver wet weight within 2% of wet weight. Therefore, ultrasound imaging is a reliable, non-invasive technique for determining liver lipid content and for diagnosing fatty liver in early lactation dairy cows to prevent loss of income for dairy farmers

HIFU Therapy Planning Using Pre‐treatment Imaging and Simulation

Current HIFU challenges include amount of tissue that can be destroyed by a single exposure, the inability to treat through bone, difficulty in monitoring therapy in real‐time, and difficulty in planning the strategy before therapy. Technological advances such as multi‐transducer or array beam generator, instrumentation and image‐based guidance of HIFU treatment promise to overcome many of these problems. However, there is limited work toward HIFU dosimetry and therapy planning. We present a systematic approach for developing pre‐treatment planning and HIFU dose calculations for specific target location using simulations and imaging data. We also present initial techniques and tools towards HIFU treatment planning (targeted for open‐skull brain tumor therapy) using patient‐specific pre‐therapy imaging (e.g., CT or MRI) similar to dosimetry and planning for radiation therapy. This work has potential to aid development of optimized high‐precision HIFU dosimetry and patient‐specific planning strategies for complex and sensitive applications such as in brain tumor HIFU therapy. If successful, it potentially could reduce the guess work on dosage parameters and thereby reducing the overall treatment duration and reduced exposure to non‐target tissues

Neural Network Application for Classifying Beef Intramuscular Fat Percentage

In the previous report, we have presented statistical pattern recognition and classification techniques to preclassify the ultrasonic images into the low- or high- IFAT groups (less than 8% and more than 8%). The classification tree was used in the previous report, and it provided overall classification accuracy of 90% for low- and high- IFAT groups of images. Here, we are presenting artificial neural network (ANN) as a pattern recognition tool to get better classification accuracy. ANNs provide a nonparametric approach for the nonlinear estimation of data. These models are trained to mimic the desired behavior using example data from the actual problem. The ANN model provided classification accuracy of 95% for 653 sample images

An Experimental Study of Effects of Overlaying Tissues on HIFU Lesion

Understanding the effect of overlaying tissues on HIFU lesion is crucial for estimating HIFU dose distribution at a target tissue. We have run a series of experiments to systematically observe the effects of the overlaying tissues on the HIFU beam and ultimately the lesion created in the target tissue. First, we mapped out the HIFU transducer beam (in low power) under water without and with different overlaying tissue layers. Then, we performed a series of experiments in high power to create lesions in target tissues (e.g., liver) without and with overlaying tissues (e.g. muscle). The lesions are characterized by slicing the tissues and reconstructing the 3D lesion from calibrated pictures of the target tissue slices. The low power beam measurements show significant effects in terms of severe beam wave‐field amplitude distortion due to phase aberration introduced by velocity inhomogeneity in the overlaying tissues. These results compare well qualitatively with the computational models. The results from the high power HIFU lesions in a similar setup using various tissues, including liver and muscle, provide understanding of the significance of phase aberration in overlaying tissues and could prove useful towards high precision HIFU therapy

A Study of Effects of Tissue Inhomogeneity on HIFU Beam

The potential of high‐intensity focused ultrasound (HIFU) will not be realized unless the effects of overlaying tissues are understood in such a way that allows for estimation of HIFU dose distribution at a target tissue. We employ computational models to examine the impact of phase aberration on tissue ablation. Thompson and Roberts have recently studied the effects of phase aberration on ultrasound focusing in aerospace engine materials such as titanium alloy, and have developed a computational model to examine these effects. The ultrasound beam observed after transmission through the fused quartz (homogeneous) and that observed after transmission through the titanium (inhomogeneous) demonstrate the severe beam wavefield amplitude distortion introduced by the velocity inhomogeneity‐induced phase aberration. We study applicability of this approach to model phase aberration in inhomogeneous tissues and its effect on HIFU dose distribution around the focus. It is hypothesized that the ill‐effects of phase aberration accumulate during propagation through intervening tissue in which field intensities are substantially lower than that in the focal zone, and it is therefore appropriate to use a linear acoustic model to describe the transport of energy from the transducer to the volume targeted for ablation. We present initial results of the simulation and experiments of beam measurements under water without and with different tissue layers. © 2006 American Institute of PhysicsCopyright 2006 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in AIP Conference Proceedings 829 (2006): 201–205 and may be found at http://dx.doi.org/10.1063/1.2205466.</p

HIFU Therapy Planning Using Pre‐treatment Imaging and Simulation

Current HIFU challenges include amount of tissue that can be destroyed by a single exposure, the inability to treat through bone, difficulty in monitoring therapy in real‐time, and difficulty in planning the strategy before therapy. Technological advances such as multi‐transducer or array beam generator, instrumentation and image‐based guidance of HIFU treatment promise to overcome many of these problems. However, there is limited work toward HIFU dosimetry and therapy planning. We present a systematic approach for developing pre‐treatment planning and HIFU dose calculations for specific target location using simulations and imaging data. We also present initial techniques and tools towards HIFU treatment planning (targeted for open‐skull brain tumor therapy) using patient‐specific pre‐therapy imaging (e.g., CT or MRI) similar to dosimetry and planning for radiation therapy. This work has potential to aid development of optimized high‐precision HIFU dosimetry and patient‐specific planning strategies for complex and sensitive applications such as in brain tumor HIFU therapy. If successful, it potentially could reduce the guess work on dosage parameters and thereby reducing the overall treatment duration and reduced exposure to non‐target tissues.Copyright 2006 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article is from AIP Conference Proceedings 829 (2006): 206–210 and may be found at http://dx.doi.org/10.1063/1.2205467.</p

An Experimental Study of Effects of Overlaying Tissues on HIFU Lesion

Understanding the effect of overlaying tissues on HIFU lesion is crucial for estimating HIFU dose distribution at a target tissue. We have run a series of experiments to systematically observe the effects of the overlaying tissues on the HIFU beam and ultimately the lesion created in the target tissue. First, we mapped out the HIFU transducer beam (in low power) under water without and with different overlaying tissue layers. Then, we performed a series of experiments in high power to create lesions in target tissues (e.g., liver) without and with overlaying tissues (e.g. muscle). The lesions are characterized by slicing the tissues and reconstructing the 3D lesion from calibrated pictures of the target tissue slices. The low power beam measurements show significant effects in terms of severe beam wave‐field amplitude distortion due to phase aberration introduced by velocity inhomogeneity in the overlaying tissues. These results compare well qualitatively with the computational models. The results from the high power HIFU lesions in a similar setup using various tissues, including liver and muscle, provide understanding of the significance of phase aberration in overlaying tissues and could prove useful towards high precision HIFU therapy.Copyright 2007 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in AIP Conference Proceedings 911 (2007): 237–241 which may be found at http://dx.doi.org/10.1063/1.2744279.</p