Diego Dall'Alba

Real-time biopsy needle tip estimation in 2D ultrasound images

Ultrasound (US) guided biopsy is a medical procedure routinely performed in clinical practice. This task could be performed by robotic systems to improve the precision in the execution and then the safety for the patient. Both robotic and human procedures could greatly benefit from real-time localization of the needle in US images. This information could guide the robot or the specialists to the correct target point avoiding critical structures. Unfortunately US data provide very low quality images of the needle making this task quite complex, even more if you want to perform the localization on-line during the image acquisition. In this work we present a needle localization method able to extract the needle orientation and the tip position in real time from B-mode US images. To evaluate the performance of the algorithm in a precise way we use an optical tracking system to measure the position and the orientation of the needle and the US probe. In such a way the comparison is not human dependent (i.e. there are no radiologists manually selecting the needle tip) and fully repeatable. The results show an improvement in term of localization accuracy compared to previous works in literature.

Robust Real-Time Needle Tracking in 2-D Ultrasound Images Using Statistical Filtering

Percutaneous image-guided tumor ablation is a minimally invasive surgical procedure for the treatment of malignant tumors using a needle-shaped ablation probe. Automating the insertion of a needle by using a robot could increase the accuracy and decrease the execution time of the procedure. Extracting the needle tip position from the ultrasound (US) images is of paramount importance for verifying that the needle is not approaching any forbidden regions (e.g., major vessels and ribs), and could also be used as a direct feedback signal to the robot inserting the needle. A method for estimating the needle tip has previously been developed combining a modified Hough transform, image filters, and machine learning. This paper improves that method by introducing a dynamic selection of the region of interest in the US images and filtering the tracking results using either a Kalman filter or a particle filter. Experiments where a biopsy needle has been inserted into a phantom by a robot have been conducted, guided by an infrared tracking system. The proposed method has been accurately evaluated by comparing its estimations with the needle tip’s positions manually detected by a physician in the US images. The results show a significant improvement in precision and more than 85% reduction of 95th percentile of the error compared with the previous automatic approaches. The method runs in real time with a frame rate of 35.4 frames/s. The increased robustness and accuracy can make our algorithm usable in autonomous surgical systems for needle insertion.

A Robust Particle Filtering Approach with Spatially-dependent Template Selection for Medical Ultrasound Tracking Applications

Tracking moving organs captured by ultrasound imaging techniques is of fundamental importance in many applications, from image-guided radiotherapy to minimally invasive surgery. Due to operative constraints, tracking has to be carried out on-line, facing classic computer vision problems that are still unsolved in the community. One of them is the update of the template, which is necessary to avoid drifting phenomena in the case of template-based tracking. In this paper, we offer an innovative and robust solution to this problem, exploiting a simple yet important aspect which often holds in biomedical scenarios: in many cases, the target (a blood vessel, cyst or localized lesion) exists in a semi-static operative field, where the unique motion is due to organs that are subjected to quasi-periodic movements. This leads the target to occupy certain areas of the scene at some times, exhibiting particular visual layouts. Our solution exploits this scenario, and consists into a template-based particle filtering strategy equipped with a spatially-localized vocabulary, which in practice suggests the tracker the most suitable template to be used among a set of available ones, depending on the proposal distribution. Experiments have been performed on the MICCAI CLUST 2015 benchmark, reaching an accuracy (i.e. mean tracking error) of 1.11 mm and a precision of 1.53 mm. These results widely satisfy the clinical requirements imposed by image guided surgical procedure and show fostering future developments.

A compact navigation system for free hand needle placement in percutaneos procedures

In this work we have designed and developed a new navigation system for interventional radiology, implemented in a light and compact device. The system attached to the needle is composed by a small screen that gives hints about the position and the orientation, a controller that commands the screen and interfaces with the computer, and a marker that communicates with a tracking system. By using a real time software the user is guided to move the needle along the desired position and orientation. To the best of our knowledges, this is the first system to have the navigation display integrated directly on the tool. The in-vitro tests we have performed, show how such a system yields a higher precision in the execution of the task and a reduction of the time required to complete the procedure.

Analytical derivation of elasticity in breast phantoms for deformation tracking

PurposePatient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms.MethodsAn analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages.ResultsApplication of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods.ConclusionIt can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.