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

Prototype Development of an Image Capturing Device for Field Use

Real-time ultrasound technology is now being used by many researchers and technicians for evaluating livestock composition, especially in beef and swine. The digitally captured ultrasound images are analyzed for fat thickness, ribeye crosssectional area, and percentage intramuscular fat (IMFAT). ISU researchers and ultrasound technicians have realized that the current method of capturing images using a regular portable PC has many problems including frequent failures in the field. ISU has developed a prototype device, called ÒBlackBoxÓ, that allows one to capture and store images in the field without frequent problems encountered with the regular PC. The primary goals of the design were to use minimal components and an easy to use software to capture images in the field. For field use, the BlackBox is a rugged unit with easy push-button operation. It will meet the demands of seedstock ultrasound scanning for later analysis by the technician or by a centralized processing center. It should also prove to be a useful tool for feedlot chute-side application

Models to Predict Intramuscular Fat Percentage in Live Beef Animals Using Real-time Ultrasound and Image Parameters: Report on Data From 1991-1994

Data from 710 yearling bulls and steers collected from 1991 to 1994 were used to predict the percentage of intramuscular fat (PIFAT) by using real-time ultrasound (RTU) and imageprocessing parameters. Image-processing parameters included histogram, texture, and Fourier transformation parameters. Additionally, ultrasound fat thickness (UFAT) was included. Two multiple regression models Model1 excluding UFAT and Model2 including UFAT,were developed by using 392 images and validated with 318 independent images. These models were used to assess the accuracy of image parameters in predicting PIFAT and to determine whether including UFAT as an additional covariate parameter increases accuracy. Results indicated that for actual PIFAT values ranging from .5% to 13%, RTU and image-processing parameters can consistently predict PIFAT with a root mean square error (RMSE) of 1.43 and 1.41 and a coefficient of determination (R-square) of .59 and .6 for Model1 and Model2, respectively. Both models were unbiased with intercepts of .47 and .51 (p \u3e 0.1), respectively. RTU and image-processing parameters can accurately and without bias predict PIFAT without including UFAT in the prediction model

Chute-Side Application of Real-Time Ultrasound for Feedlot Cattle Marketing–A Pilot Project

The collaborative efforts of Sioux Center Coop and Iowa State University will determine if real-time ultrasound measurements on feedlot cattle can be used to develop marketing models to assist Iowa producers in the decision-making process at marketing time and at a projected marketing time

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

Predicting Tenderness in Beef Carcasses by Combining Ultrasound and Mechanical Techniques

• Meat tenderness is a concern today in the beef cattle industry, and it will become an even greater concern in the future. • Tenderness is a complex issue, and it is difficult to predict tenderness after cooking by examining raw beef or carcass beef. Unfortunately, tenderness must be evaluated at the carcass level to be a useful tool in the industry. • A star-shaped probe was attached to an InstronÒ machine. This attachment makes it possible to measure tenderness in both raw and cooked Longissimus dorsi steaks. The correlation between raw and cooked was 0.41. • The correlation between cooked star probe values and Warner Bratzler shear values (the standard tenderness measure) was 0.53. • The star-shaped probe applies pressure to beef tissue. This approach was then combined with ultrasound to evaluate firmness or softness of beef tissue. Ultrasound images were collected as increased pressure was applied (elastography). • A probe has been built that attaches to hot carcasses to evaluate this elastography procedure

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

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

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