Manual palpation of human tissues and organs has been used for a long time as a preliminary diagnostic tool. It is known that development of certain pathologies causes tissue to harden, that is, the elasticity modulus, E, is a highly variable and disease-sensitive physical parameter of soft tissues [Sarvazyan A. P. Elastic properties of soft tissue.—In: Handbook of Elastic Properties of Solids, Liquids and Gases, Volume III, Chapter 5, eds. Levy, Bass and Stern, Academic Press, 2001, 107-127]. The range of variation of E for different soft tissues can cover four orders of magnitude: from less than 1 kPa to more than 10 MPa. Even within the same tissue sample, E may change by thousands of percent during such processes as tumor development or even ordinary muscle contraction. Diseases involving fatty and/or collagenous deposits may significantly increase or decrease tissue elasticity. This substantial dependence of E on structural changes in the tissue is the basis for the palpatory diagnosis of various diseases in such human organs as prostate, thyroid, breast, liver and pelvic floor. Palpation to seek a hard lesion in soft tissue is still a widely used technique for cancer detection.
Therefore, a method that mimics manual palpation but with enhanced sensitivity and specificity might consequently lead to better diagnostics of numerous diseases. Imaging tissue elasticity using mechanical means mimicking manual palpation has been described before. For example, see an article by A. Sarvazyan, entitled “Mechanical Imaging: A new technology for medical diagnostics.” Int. J. Med. Inf., 1998, 49, 195-216, which is incorporated herein in its entirety by reference. Also, elasticity imaging procedures and implementations are described in more detail in various patents of the prior art, see for example U.S. Pat. No. 5,524,636 to Sarvazyan and Skovoroda; U.S. Pat. No. 5,785,663 to Sarvazyan; U.S. Pat. No. 5,833,633 to Sarvazyan; U.S. Pat. No. 5,836,894 to Sarvazyan; U.S. Pat. No. 5,860,934 to Sarvazyan; U.S. Pat. No. 5,989,199 to Cundari et al.; U.S. Pat. No. 5,922,018 to Sarvazyan; U.S. Pat. No. 6,063,031 to Cundari et al.; U.S. Pat. No. 6,091,981 to Cundari et al.; U.S. Pat. No. 6,142,959 to Sarvazyan and Egorov; U.S. Pat. No. 6,179,790 to Cundari et al.; U.S. Pat. No. 6,468,231 to Sarvazyan and Egorov; U.S. Pat. No. 6,500,119 to West et al.; U.S. Pat. No. 6,569,108 to Sarvazyan and Egorov; U.S. Pat. No. 6,595,933 to Sarvazyan and Egorov; and U.S. Pat. No. 6,620,115 to Sarvazyan and Egorov.
A common feature of various technologies presented in these inventions is assessment of internal structure of soft tissue by measuring the surface stress patterns using a pressure sensor array pressed against the tissue. The changes in the surface stress patterns plotted as a function of displacement, applied load, and time provide information about elastic composition and geometry of the underlying tissue structures. A common name for this method and its various embodiments is “stress imaging” because this term reflects the main physical characteristic employed for visualizing tissue structures, similar to “ultrasound imaging” or “X-ray imaging”.
High sensitivity of non-invasive stress imaging to pathological changes in tissue may be exploited to reduce the number of unnecessary tissue biopsies currently being performed. At the present time, several non-invasive clinical diagnostic and screening modalities such as X-ray and ultrasound imaging, magnetic resonance imaging and positron emission tomography (PET) are used for making a decision about performing a biopsy at suspicious tissue sites. In the United States alone, more than 1 million breast biopsies are performed annually and many of these biopsies appear to be unnecessary. Approximately 80% of these biopsy findings are benign. The use of stress imaging as an adjunct to other non-invasive screening and diagnostic procedures could reduce substantially this benign biopsy rate.
Various specific tissue features are used in tissue characterization depending on measured characteristics and diagnosed organ or disease. For example, ultrasonic radio frequency backscatter spectral data is used for tissue characterization as described in U.S. Pat. No. 6,238,342 to Feleppa. Ultrasonic data together with histological results of corresponding biopsy sites are stored in a database and used to train a classifier suitable for real-time tissue classification and imaging. A further method of differentiating malignant and normal tissues is based on measuring tissue impedance and comparing it with reference tissue impedance of the normal tissue as disclosed in U.S. Pat. No. 6,832,111 to Tu and Quijano. Another classifier based on a method of multivariate classification in medical diagnostics is disclosed in U.S. Pat. No. 6,868,342 issued to Mutter. Raw data is first generated by analysis of the variables and then transformed by application or appropriate algorithms to scaleless rank differentials between the variables. The rank orders of variables are used to classify tissues based on readily observable user interfaces, such as a graphical (visual) user interface or an auditory user interface.
Several other types of data classifiers are also known in the prior art, e.g. Bayesian classifiers, neural network classifiers and rule-based classifiers. A classifier is typically trained on a series of examples for a particular task. After the classifier has been so trained, new examples are presented to it for classification. The classifier can be trained either using a supervised method or an unsupervised method. For example, Parra et al. in U.S. Pat. No. 6,208,983 describe a neural network classifier for predicting a breast cancer malignancy when given patient information and skin potentials of other patients as inputs. Zhou et al. in another example teach the unified Bayesian framework for shape registration in U.S. Pat. No. 7,292,737.
One common drawback of the tissue elasticity imaging methods, including the stress imaging technique, is that they do not make full use of the wealth of diagnostic information contained in tissue mechanical features and patient-relevant data to characterize and differentiate the tissue, e.g. discriminate benign and malignant lesions. An aim of the present invention is to alleviate this drawback.