Search

In surgical practice, small metallic instruments are frequently used to perform various tasks inside the human body. We address the problem of their accurate localization in the tissue. Recent experiments using medical ultrasound have shown that this modality is suitable for real-time visualization of anatomical structures as well as the position of surgical instruments. We propose an image- processing algorithm that permits automatic estimation of the position of a line-segment-shaped object. This method was applied to the localization of a thin metallic electrode in biological tissue. We show that the electrode axis can be found through maximizing the parallel integral projection transform that is a form of the Radon transform. To accelerate this step, hierarchical mesh-grid algorithm is implemented. Once the axis position is known, localization of the electrode tip is performed. The method was tested on simulated images, on ultrasound images of a tissue mimicking phantom containing a metallic electrode, and on real ultrasound images from breast biopsy. The results indicate that the algorithm is robust with respect to variations in electrode position and speckle noise. Localization accuracy is of the order of hundreds of micrometers and is comparable to the ultrasound system axial resolution.

In medical applications, miniature surgical instruments such as needles, or electrodes are introduced into human body. The position of instrument in tissue can be estimated using 3D ultrasound. In previous publications, we introduced two novel algorithms for automatic electrode localization from 3D ultrasound images. The first method is based on the parallel integral projection (PIP) transform, a modification of the Radon transform. We showed that the axis of the electrode can be estimated from the maximum of PIP transformation. To accelerate search for the maximum, a hierarchical mesh-grid algorithm is implemented. In second method, the electrode axis is described by a cubic polynomial. The distribution of voxel intensities inside the electrode region is a priori estimated from acquired data. The model parameters are robustly estimated using the RANSAC estimator. In this paper, their performance in terms of accuracy is compared. A series of tests on numerical phantoms created with the FIELD II simulation program were performed to quantitatively evaluate the localization accuracy. We observed a decrease in accuracy when artificial noise was added to the input data. The algorithms were also tested on real ultrasound data of a cryogel phantom comprising metallic electrode

Utilization of tools during surgical interventions sets the problem of their accurate localization within biological tissue. The ultrasound imaging represents an inexpensive and a flexible approach for a real-time image acquisition of tissue structure with metal instruments. There are several difficulties involving processing of ultrasound images: Their noisy nature makes the localization task difficult; the objects appear irregular and incomplete. Our task is to determine the position of a curvilinear electrode in biological tissue from a three-dimensional ultrasound image. Initially, the data are segmented by thresholding and processed with the randomized version of the RANSAC (R-RANSAC) algorithm. The curvilinear electrode is modeled by a three-dimensional cubic curve. Its shape is subject to check using a curvature measure in the hypothesis evaluation step of the R-RANSAC algorithm. Subsequently, we perform the least squares curve fitting to the data that have been marked by the R-RANSAC as the ones corresponding to the sought object. The position estimation is optimal with respect to the mean square criterion. Finally, the localization of the electrode tips is carried out by a hypothesis testing on the distances between projections of inliers on the estimated curve. The algorithm has been tested on real three-dimensional ultrasound images of a tissue mimicking phantom with a curvilinear object. From the results, we conclude that the method is very stable even if the data contain high percentage of outliers. The computational cost of the algorithm shows the possibility of real-time data processing.

In this paper we describe a method for automatic electrode localization in soft tissue from a radiofrequency signal. The method exploits a property of the Radon transform (RT) that allows us to localize a line-segment in 3D data. The method directly processes the radiofrequency (RF) signal provided by the ultrasound (US) probe. Thus, there is no need for ultrasound image reconstruction. The method is able to detect line-segments that are discontinuous. The low computational cost allows us to process data in real-time. The algorithm was tested on a radiofrequency signal acquired by scanning a phantom containing a thin metal electrode of 150 /spl mu/m in diameter. The experiments show that the developed technique is capable of reliably finding an arbitrarily positioned line-segment in a 3D image.