S. Siebers

Investigation of the influence of blood flow rate on large vessel cooling in hepatic radiofrequency ablation / Untersuchung des Einflusses der Blutflussgeschwindigkeit auf die Gefäßkühlung bei der Radiofrequenzablation von Lebertumoren

Abstract Radiofrequency (RF) ablation using high-frequency current has become an important treatment method for patients with non-resectable liver tumors. Tumor recurrence is associated with tissue cooling in the proximity of large blood vessels. This study investigated the influence of blood flow rate on tissue temperature and lesion size during monopolar RF ablation at a distance of 10 mm from single 4- and 6-mm vessels using two different approaches: 1) an ex vivo blood perfusion circuit including an artificial vessel inserted into porcine liver tissue was developed; and 2) a finite element method (FEM) model was created using a novel simplified modeling technique for large blood vessels. Blood temperatures at the inflow/outflow of the vessel and tissue temperatures at 10 and 20 mm from the electrode tip were measured in the ex vivo set-up. Tissue temperature, blood temperature and lesion size were analyzed under physiological, increased and reduced blood-flow conditions. The results show that changes in blood flow rate in large vessels do not significantly affect tissue temperature and lesion size far away from the vessel. Monopolar ablation could not produce lesions surrounding the vessel due to the strong heat-sink effect. Simulated tissue temperatures correlated well with ex vivo measurements, supporting the FEM model.

Ultrasound elastography for the age determination of venous thrombi: Evaluation in an animal model of venous thrombosis

The exact age determination of venous thrombi is important if thrombolytic therapy or surgical thrombectomy is considered. Clinical symptoms as well as duplex-ultrasound and phlebography are unreliable in this respect and do not allow an exact age estimation. Ultrasound elastography can provide information about the elastic properties of thrombi. Since thrombus elasticity decreases with age due to the organisation process, it should be possible to use elastography to stage the degree of organisation and thereby determine the age of venous thrombi. Experimental venous thrombi aging 1, 3, 6, 9, 12 and 15 days were created in a porcine model by laparoscopic ligation of the infrarenal Vena cava in combination with transfemoral infusion of thrombin. The thrombosed iliac veins were explanted and embedded in gelatine, after that they underwent examination by ultrasound elastography. In addition, histological evaluation of the thrombi was performed. Elastography demonstrated a decline in thrombus elasticity between days 6 and 12 with the 12-day-old thrombi being about 3 times harder then the 6-day-old thrombi. This correlated with the histological findings, which demonstrated a marked increase in fibroblast and collagen production in the clots during this time, with the 12- and 15-day thrombi showing signs of advanced organisation. In conclusion, in an experimental setting, ultrasound elastography was helpful in determining the exact age of venous thrombi. The differences in elasticity were most pronounced between days 6 and 12, which is also the most relevant time frame when considering invasive therapies in human venous thrombosis.

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity; are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

Evaluation of ultrasonic texture and spectral parameters for coagulated tissue characterization

Radiofrequency ablation is a well established, minimally invasive approach for the treatment of tumors. However, at present there is a lack of suitable imaging modalities for accurate online monitoring of the coagulation process. The aim of this work is to evaluate the potential of various tissue characterizing ultrasonic parameters from spectral and spatial domain to differentiate between coagulated and noncoagulated tissue. The calculated parameters include first and second order texture parameters, estimates of attenuation coefficients, spectral parameters (slope, intercept and midband value) and coefficients of autoregressive spectral estimates. As a measure of selectivity of each parameter the area under the receiver operating characteristic (ROC) curve was utilized. The best performing parameters can be used to be processed by a classification system.

Classification of venous thrombosis combining ultrasound elastography and tissue characterization

Deep venous thrombosis (DVT) is the formation of a blood clot in one of the deep veins of the body, usually in the leg. Common treatment methods include medication with anticoagulants or surgical thrombectomy. Since treatment of DVT succeeds only during the first 7-10 days, exact age determination of DVT is of high importance for an appropriate treatment decision. However, the accuracy of available methods including sonography, phlebography, CT and MRT is often not sufficient. It has been reported that about 30% of all DVT are wrongly staged using common diagnostic modalities and therefore lead to inadequate therapeutic efforts. Therefore alternative and more accurate approaches for staging DVT are desired. Blood clots leading to venous thrombosis undergo an organization process with increasing age. It is known that changes in mechanical stiffness, acoustical properties and appearance in B-mode images accompany the organization process. Therefore several alternative diagnostic approaches, including elastography and ultrasonic tissue characterization, have been proposed in the past. In this work, 22 thrombi of defined age were induced in pigs. Ultrasonic measurements were carried out after surgical resection of the thrombosed vessel segments. Spectral and texture parameters as well as strain estimates obtained using elastography were used to classify thrombosed vessel segments in vitro and thus distinguish between thrombi of age /spl les/ 6 days and age > 6 days. A combination of the best performing parameters was processed by a classification system. Total crossvalidation over specimens was done using Euclidian, Mahalanobis, and maximum-likelihood classifiers. 90% of specimens could be classified correctly using maximum-likelihood classifiers.

Real Time Strain Imaging — a new Ultrasonic Method for Cancer Detection: First Study Results

Prostate tumors can have a higher mechanical hardness than the surrounding tissue. During the digital rectal exam this can be used not only to detect the hypertrophy but also localized hardenings. The examination by digital palpation is inaccurate and even in combination with PSAvalue and a transrectal ultrasonic examination the result is often not reliable. Ultrasound strain imaging is able to measure and visualize the elastic properties of a tissue region and hence is an adequate supplement for commonly used diagnostic procedures. We have developed a real time system for elastographic mechanical tissue assessment of the prostate which can be used for the transrectal ultrasonic examination for navigation and diagnosis. During the examination a sequence of ultrasonic images is acquired while the organ is slightly compressed by the ultrasound probe. Using a numerical analysis of image pairs of the acquired sequence the tissue strain is calculated which represents the spatial elasticity distribution of a specific cross-section of the organ and which are able to distinguish hard areas in the tissue. We present results from several patients. which show, that real time strain imaging is able to detect tumor-like areas which are inconspicuous in the bmode image. The results correspond to the histological specimens. After the evaluation of 130 patients using a prospective study we found the specificity for cancer detection to be approximately 84% and a sensitivity of approximately 76 %. Furthermore the tumor location and extend was correctly predicted in most of the investigated patients using our real time strain imaging. SIGNAL PROCESSING Strain imaging was first described by Ophir in 1991 [1], but could not easily be clinically applied so far, because the described method had no real time capability. To use it in a clinical setting we invented a time efficient algorithm, called “phase root seeking” [2], which in a current system is able to calculate up to 30 strain images per second using a conventional desktop PC. Similar to [1], time shifts are estimated using a discrete number of windows at discrete depths. The time shift τm,k of the k-th window of two A-lines centered around tk = k∆T is estimated by the following iterative formula L k m k m , 1 , 0 , , − τ = τ (1)

Computer Aided Diagnosis of Parotid Gland Lesions Using Ultrasonic Multi-Feature Tissue Characterization

In this article, an ultrasound based system for computer aided characterization of biologic tissue and its application to differential diagnosis of parotid gland lesions is proposed. Aiming at an automated differentiation between malignant and benign cases, the system is based on a supervised classification using tissue-describing features derived from ultrasound radio-frequency (RF) echo signals and image data. Standard diagnostic ultrasound equipment was employed to acquire ultrasound RF echo data from parotid glands of 138 patients. Lesions were manually demarcated as regions-of-interest (ROIs) in the B-mode images. Spectral ultrasound backscatter and attenuation parameters are estimated from diffraction corrected RF data, yielding spatially resolved parameter images. Histogram based statistical measures derived from the parameters distributions inside the ROI are used as tissue describing features. In addition, texture features and shape descriptors are extracted from demodulated ultrasound image data. The features are processed by a maximum likelihood classifier. An optimal set of 10 features was chosen by a sequential forward selection algorithm. The classifiers performance is evaluated using total cross validation and receiver operating characteristic (ROC) analysis. As a reference method, postoperative pathohistologic analysis was conducted and proved malignancy or prospective malignancy in 51 patients. The classification using the proposed system yielded an area under the ROC curve of 0.91, proving significant potential for differentiating between malignant and benign parotid gland lesions.

[Ultrasound-based imaging modalities for thermal therapy monitoring].

Thermal therapy has been established as an alternative and minimally invasive approach for the treatment of tumors. During a thermal therapy tissue is heated locally up to above 60 degrees C. Cancerous tissue can thus be destroyed by coagulation. At present there are no suitable imaging modalities available for an accurate real-time monitoring of the coagulation process. A subproject of the Ruhr Center of Competence for Medical Engineering (KMR Bochum) aims at developing an ultrasound-based, real-time capable monitoring system for thermal therapy. Therefore several tissue characterizing imaging modalities will be combined in a new multimodal concept. Initial experiments with porcine liver in vitro have shown that real-time monitoring of a thermal therapy using various imaging methods simultaneously will be possible.

2G-6 Classification of Parotid Gland Tumors using Sonohistology

In this paper results from a clinical study on differentiating between various types of parotid gland tumors using computerized tissue characterization (Sonohistology) are presented. Complex baseband ultrasound data have been acquired during the common examinations of patients who were scheduled to have parotid surgery shortly after the acquisition. Data of 123 benign and malignant parotid-gland alterations have been included in the study. For data acquisition, a conventional diagnostic ultrasound scanner controlled by custom software running on a laptop computer was used. Tumors were manually contoured in the B-Mode images. Acquired data were stored on an external PC and subdivided into numerous regions of interest (ROI). For each ROI, a set of tissue characterizing spectral and texture features was calculated. Moreover, Fourier descriptors have been calculated from the contours of the lesions to characterize differences in shape of certain kinds of tumors. Training data have been generated from the manually contoured areas. For classification, the training data have been divided in up to four subclasses. The final classification was done using two target classes. The first class included all cases for which a surgical treatment was definitely necessary. The second class included all cases that did not necessitate a surgical treatment. The best feature set was processed by a classification system. For classification, the maximum likelihood measure was used. Classification was done by total cross validation over cases. The best feature set was found by sequential forward selection and included two spectral features (attenuation and slope), two first order texture feature (squared signal to noise ratio and kurtosis), two measures from the cooccurrence matrix (sum variance and variance of sum of squares) and two Fourier descriptors. The receiver operating characteristics curve area was AROC = 0.86

Computer Assisted Characterization of Lymph Nodes Using Spectral Ultrasound Backscatter and Attenuation Measures

This paper deals with a system for computer assisted characterization of biological tissue using diagnostic ultrasound and its application in the differential diagnosis of lymph nodes. Using standard diagnostic ultrasound equipment, ultrasound radio frequency (RF) data originating from lymph nodes of 24 patients were acquired. 12 patients were proven to have malignant alterations of lymph nodes. The proposed system aims at an automated differentiation between malignant and benign cases. In a first step, spectral ultrasound backscatter and attenuation measures were extracted from diffraction corrected RF data, yielding spatially resolved parameter images. A reduced representation of the measures was found using first order statistics and used as tissue describing features. The features were processed by the classification system. An optimal set of features was chosen by a sequential forward selection algorithm and included 3 features. Classification was performed by total cross validation using a probabilistic neural network. Inputs to the network could be biased, depending on the target class. Thereby, the classifier could be forced to reach an arbitrary sensitivity in detecting positive cases. Thus, receiver operator characteristic (ROC) curves could be determined. The area under the ROC curve was 0.94, proving the potential of the proposed method for differentiating malignant and benign lymph nodes.