Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-09-17
2003-01-21
Jaworski, Francis J. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
Reexamination Certificate
active
06508768
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Technical Field
This invention relates to diagnostic ultrasonic imaging in general and to ultrasonic imaging of the elastic properties of scanned tissue in particular.
2. Description of the Related Art
Tissue elastic properties convey important diagnostic information. Consequently, palpation—the pressing of tissue to feel for differences in elasticity—has been used since ancient times as a simple but effective diagnostic technique. Even to this day, for example, most breast cancers are discovered by self-examination using manual palpation, and physicians still rely on palpation to detect potential tumors of the liver and prostate.
The principle of manual palpation is accordingly well known and is based on the property that if a compressive force is applied to an elastic body, then it will deform. If a relatively stiffer, that is, less deformable, inclusion is located within a region of the body, then a constant compressive displacement will deform the region above the stiff object more than the adjacent regions. Because tissues are elastic, the more they are deformed, the greater counter force they generate; in other words, large stress leads to large deformation. If a diagnostician applies the pressure with her fingers, then she will often be able to feel the stress distribution above the palpated region. To sum up the procedure, if one presses on body tissue, then one can often feel “lumps.”
One property of tissue elasticity that is not as intuitive is that the distribution of stress that results from a compressive force applied to the top of the region under investigation is not uniform throughout the region. Rather, the stress difference that is large close to the target decays rapidly further from the target. In other words, differences in tissue stiffness are not as noticeable when the stiffer object is deeper within the body. Stress decay therefore limits the depth at which one can palpate tumors manually—if the tumor is too deep, then one cannot feel it at all.
Ultrasonic elasticity imaging is a technique that emulates palpation. According to this technique, an ultrasound transducer is used as a remote sensing device to scan an object within an interrogation region of the body both before and after a compression is applied. The 2-D displacement function is then estimated by comparing the pre- and post-compression scans. Object strain and/or elastic constants can then be estimated from the estimated displacement function.
Ultrasonic elasticity imaging has several advantages over palpation. One advantage is that it can provide information about tissue elasticity as deep as the ultrasound can penetrate, whereas manual palpation senses stress only near the surface. Another advantage is that ultrasonic elasticity imaging has relatively high sensitivity, although resolution and sensitivity can be reduced for deeper inclusions. Ultrasonic elasticity imaging can also provide a 2-D cross sectional view of the elastic properties of the object that are within the sound beam. For example, using axial strain images, one can often detect malignant lesions and estimate their location and geometry. One other obvious advantage of ultrasonic elasticity imaging is that the image acquisition process is non-invasive and poses no risk to patients.
With these advantages, ultrasonic elasticity imaging has found many applications such as tumor detection, assessment of early renal disease, and vascular disease diagnosis. Moreover, because the elastic properties of tissue play an important role in tissue characterization, many more clinical applications can be found once image quality can be reliably maintained. Examples of known applications of ultrasonic elasticity imaging are disclosed in:
M. Bilgen and M. F. Insana, “Deformation models and correlation analysis in 9 elastography,” J. Acoust. Soc. Am. 99(5): 3212-3224, 1996;
I. Cespedes, J. Ophir, H. Ponnekanti, and N. Maklad, “Elastography: Elasticity imaging using ultrasound with application to muscle and breast in vivo,” Ultrason. Imaging 15: 73-88, 1993;
E. J. Chen, R. S. Adler, P. L. Carson, W. K. Jenkins, and W. D. O'Brien, “Ultrasound tissue displacement imaging with application to breast cancer,” Ultrason. Med. Biol. 21(9): 1153-1162, 1995;
B. S. Garra, E. I. Cespedes, J. Ophir, S. R. Spratt, R. A. Zuurbier, C. M. Magnant, and M. F. Pennanen, “Elastography of breast lesions: initial clinical results,” Radiology 202(1):, 79-86, 1997;
T. J. Hall, P. Chaturvedi, M. F. Insana, J. G. Wood, H. Khant, and Y. Zhu, “Tracking progressive renal disease with quantitative ultrasonic imaging,” IEEE “Ultrasonics Symposium Proc. 98CH36102: 1769-1772, 1998;
S. H. Huang, “Principles of sonoelasticity imaging and its applications in hard tumor detection,” Ph.D. thesis, University of Rochester, Rochester, N.Y., 1990;
T. A. Krouskop, D. R. Dougherty, and S. F. Vinson, “A pulsed Doppler ultrasonic system for making non-invasive measurements of the mechanical properties of soft tissues,” J. Rehab Res Dev 24(2):1-8, 1987;
M. Krueger, A. Pesavento, H. Ermert, K. M. Hiltawsky, L. Heuser, H. Rosenthal, and A. Jensen, “Ultrasonic strain imaging of the female breast using phase root seeking and three-dimensional ‘optical flow’,” IEEE Ultrasonics Symposium Proc. 98CH36102: 1757-1760, 1998;
L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, “Imaging of the elastic properties of the tissue: A review,” Ultrason. Med. Biol. 22(8): 959-977, 1996;
K. Motoi, H. Morita, N. Fujita, Y. Takano, K. Muzushige, S. Senda, and S H. Matsuo, “Stiffness of human arterial wall assessed by intravascular ultrasound,” J. Cardio. 25: 189-197, 1995;
J. Ophir, E. I. Cespedes, H. Ponnekanti, Y. Yazdi, and x. Li, “Elastography: a quantitative method for imaging the elasticity of biological tissues,” Ultrasonic Imaging 13: 111-134, 1991;
A. P. Sarvazyan, A. R. Skovoroda, S. Y. Emelianov, J. B. Fowikes, J. G. Pipe, R. S. Adler, R. B. Buxton, and P. L. Carson, “Biophysical bases of elasticity imaging,” Acoust. Imaging 21:223-240, 995; and
M. Tristam, D. C. Barbosa, D. O. Cosgrove, D. K. Nassiri, J. C. Bamber, and C. R. Hill, “Ultrasonic study of in vivo kinetic characteristics of human tissues,” Ultrason. Med. Biol. 12(12): 927-937, 1986.
There are, accordingly, several displacement/strain estimation methods in the prior art. The algorithms underlying these known methods typically rely on cross-correlation, echo data modeling, block matching, direct strain estimation using adaptive local stretching algorithm, and the analysis of a deformable model. These known methods are outlined here.
Cross-correlation techniques have been widely used in sonar and radar systems since their inception in the 1940's. In sonar, marine vessels tow an array of acoustic sensors. A passive, 1-D sonar array then listens for externally generated sound while an active sonar system transmits sound pulses and listens for corresponding echoes. The time delays between signals received by different sensors in the array are then computed using cross-correlation. The relative distance and bearing of the echo source can then be computed from the estimated time delay.
Cross-correlation has also been applied to the problem of estimating the elastic properties of biological tissue. Published International Patent Application PCT/EP99/03769, “System for Rapidly Calculating Expansion Images from High-Frequency Ultrasonic Echo Signals,” Pesa Vento and Helmut Ermert, published Dec. 2, 1999. The displacement in time between at least two different echo signals is determined by iteratively evaluating the phases of a plurality of the complex values of the cross-correlation function. In order to achieve the desired speed, this method restricts evaluation to echo signals from points on the same A-line.
The general time delay estimation problem can be stated more rigorously as follows: A signal, s(t), which is generated by a remote source, is detected by two sensors. Because the distances between the sensors and the source are different, the detected signals from these sensors can be written as:
s
Hall Timothy J.
Zhu Yanning
Jaworski Francis J.
Slusher Jeffrey
University of Kansas Medical Center
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