Christian Perrey

A time-efficient and accurate strain estimation concept for ultrasonic elastography using iterative phase zero estimation

In ultrasonic elastography, the exact estimation of temporal displacements between two signals is the key to estimating strain. An algorithm was previously proposed that estimates these displacements using phase differences of the corresponding base-band signals. A major advantage of these algorithms compared with correlation techniques is the computational efficiency. In this paper, an extension of the algorithm is presented that iteratively takes into account the time shifts of the signals to overcome the problems of aliasing and accuracy in the estimation of the phase shift. Thus, it can be proven that the algorithm is equivalent to the search of the maximum of the correlation function. Furthermore, a robust logarithmic compression is proposed that only compresses the envelope of the signal. This compression does not introduce systematic errors and significantly reduces decorrelation noise. The resulting algorithm is a computationally simple and very fast alternative to conventional correlation techniques, and the accuracy of strain images is improved.

Axial Strain Imaging Using a Local Estimation of the Scaling Factor from RF Ultrasound Signals

The main signal-processing techniques used in elastography compute strains as the displacement derivative. They perform well for very low deformations, but suffer rapidly from decorrelation noise. Aiming to increase the range of accurate strain measurements, we developed an adaptive method based on the estimation of strains as local scaling factors. Its adaptability makes this method appropriate for computing scaling factors resulting from larger strains or a wide spread of strain variations. First, segments corresponding to the same part of tissue are adaptively selected in the rest and stressed state echo signals. Then, local scaling factors are estimated by iteratively varying their values until reaching the zero of the phase of the complex cross-correlation function. Results from simulation and from experimental data are presented. They show how this adaptive method can track various local deformations and its accuracy for strain up to 7%.

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.

Computerized segmentation of blood and luminal borders in intravascular ultrasound

Intravascular ultrasound (IVUS) provides detailed images of normal and abnormal coronary vessel wall morphology and can be used for measuring the lumen area and plaque burden. A prerequisite for this task is the reliable segmentation of IVUS images and discrimination of blood and tissue. At frequencies above 20 MHz the backscatter of blood approaches the same level as backscatter from the vessel wall, which complicates manual segmentation. This work presents an automated scheme for the segmentation of blood in IVUS images. Based on the in vivo acquisition of radio frequency (RF) data, spectral parameters as well as first and second order textural parameters were evaluated. Tissue describing parameters were estimated directly from RF data after dividing each RF frame into numerous regions of interest to allow spatially resolved classification. Parameters originating from different parameter groups were compared with each other and a neuro-fuzzy inference system was trained on up to eight parameters to distinguish blood from tissue using a multi-feature approach. The in vivo results of the multi-feature classifier achieve classification results of A/sub ROC/=0.95 measured as the area under the receiver operating characteristic curve (ROC) and thus prove the reliability of the presented method for the segmentation of blood and tissue with IVUS.

Strain imaging with intravascular ultrasound array scanners: validation with phantom experiments.

Intravascular Ultrasound (IVUS) is routinely used in interventional cardiology for imaging coronary plaque morphology. However, the use of B-mode images for tissue characterization and detection of vulnerable coronary plaques is limited. Strain imaging with ultrasound is a new modality that provides additional information for tissue characterization by imaging differences in tissue stiffness. The aim is to differentiate between vulnerable (soft) plaques and less dangerous calcified (hard) plaques. In this work, the applicability of a time efficient strain imaging algorithm in conjunction with data from IVUS array transducers is evaluated. Unfocused radiofrequency (rf) data from the transducer array is acquired using custom made hardware. Rf line reconstruction is performed offline by synthetic aperture focusing techniques. Vessel mimicking phantoms of different geometries and material stiffness are made from agar and Polyvinyl Alcohol Cryogel (PVA). Experiments are conducted in a water tank and a water column is used for applying intraluminal pressure differences required for strain imaging. The results show that strain images can be calculated with A-lines reconstructed from unfocused rf raw data. Regions of different stiffness can be identified qualitatively by local strain variations. With the used algorithm strains of up to 2% can be imaged without significant decor-relation.

Estimation of 2D displacement and strain field in high frequency ultrasound based elastography

High frequency ultrasound (HFUS) and intravascular ultrasound (IVUS) based elastography can be utilized for tissue elasticity imaging at a microscopic level. Mechanical strain fields inside the tissue are calculated as the spatial derivatives of estimated displacement fields. A technique for the estimation of 2D displacement fields is presented. Axial and lateral displacements in the imaging plane are estimated by tracking speckle in B-mode images and analyzing phase differences between image spectra. Concept and limitations of the proposed approach are discussed. Results are compared with an approach for 1D axial displacement estimation, which is based on the analysis of radio frequency (RF) echo signals. The implemented techniques were applied to asses skin elasticity and to analyze non-uniform rotational distortions (NURD) in IVUS with rotating single element transducers. Results of in vivo measurements are presented. It is shown that B-mode based strain imaging is feasible, provided that applied strains are sufficiently small to prevent decorrelation of echo signals.

A modified synthetic aperture focusing technique for the correction of geometric artefacts in intravascular ultrasound elastography

Various elastography methods have been developed for imaging the elastic properties of coronary plaques with intravascular ultrasound (IVUS). In these methods the vessel tissue is compressed due to changes of the intraluminal pressure, i.e. by a force originating from the lumen center. If the transducer is not centered in the lumen, the ultrasound beams are not parallel to the direction of force, which leads to errors in the strain estimate. In this work a modified synthetic aperture focusing technique (SAFT) is presented, that allows the reconstruction of ultrasound beams parallel to the force direction. To verify this approach, IVUS data and a cylindrical phantom are simulated with various eccentric catheter positions.

Principle, Applications and Limitations of Ultrasound Elastography

Mechanical properties of biological tissue are of histological relevance because of a correlation between palpable lesions (e.g. nodes) and malignant tumors. The majority of cancerous lesions can be palpated as hard inclusions, at least after they have reached a certain size. However, there are also benign changes in tissue, which tend to be harder compared to surrounding tissue. As manual palpation is limited to the skills of the examiner and contributes only subjective and qualitative information, an imaging modality could be helpful, which provides the examiner with objective and quantitative information about mechanical properties of the examined tissue. Medical ultrasound imaging systems allow a visualization of internal mechanical tissue displacements caused by external surface forces or by induced internal forces. The spatial distribution of internal strain can be derived from tissue displacements to get so called “elastograms” [5], which lead to a visualization of both, the location and size of stiff tissue areas.

P1D-8 Strain Imaging with Intravascular Ultrasound: An In Vivo Study

Coronary atherosclerosis is a common disease in industrialized countries. Acute coronary syndromes are associated with a high mortality rate. They are usually caused by a sudden occlusion of the coronary lumen due to rupture of unstable plaques in the vessel wall, often with less than 50 % stenosis. Thus, plaque morphology does not give sufficient information for determining the risk of an acute syndrome. However, the mechanical properties of vulnerable coronary plaques were shown to be different from other plaque types. Therefore, IVUS strain imaging can be an important imaging tool for risk assessment of plaques

Strain imaging with intravascular ultrasound: An in vivo study

Coronary atherosclerosis is a common disease in industrialized countries. Acute coronary syndromes are associated with a high mortality rate. They are usually caused by a sudden occlusion of the coronary lumen due to rupture of unstable plaques in the vessel wall, often with less than 50 % stenosis. Thus, plaque morphology does not give sufficient information for determining the risk of an acute syndrome. However, the mechanical properties of vulnerable coronary plaques were shown to be different from other plaque types. Therefore, IVUS strain imaging can be an important imaging tool for risk assessment of plaques