Automatic Search for Photoacoustic Marker Using Automated Transrectal Ultrasound

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [19]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53 degree average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29 degree and 0.48 degree average errors after 28 data acquisitions.Comment: 13 pages, 9 figure

Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy

Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modelity in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker method, where the ultrasound / photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principles of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics. The result is also comparable with our previous approach, and the proposed method can be implemented with a normal white light stereo camera and doesn't require highly accurate localization of the PM. Conclusion: The proposed frame registration algorithm enabled a simple yet efficient integration of commercial US/PA imaging system into laparoscopic surgical setting by leveraging the characteristic properties of acoustic wave propagation and laser excitation, contributing to automated US/PA image-guided surgical intervention applications.Comment: 12 pages, 9 figure

Ultrasound speckle detection using low order moments

Abstract — Speckle detection is essential in many areas of quantitative ultrasound. In this work, speckle is characterized with R=SNR and S=skewness of the amplitude of the ultrasound signal data A. Different powers of A can be used to calculate R and S. Prager et al. [1] proposed a method for finding the optimum power value, which then was further scrutinized [2]. We propose using two different powers of A in R and S, and perform a large number of computer simulations to find these optimal values. I

Ultrasound Elastography Using Multiple Images

Displacement estimation is an essential step for ultrasound elastography and numerous techniques have been proposed to improve its quality using two frames of ultrasound RF data. This paper introduces a technique for calculating a displacement field from three (or multiple) frames of ultrasound RF data. To calculate a displacement field using three images, we first derive constraints on variations of the displacement field with time using mechanics of materials. These constraints are then used to generate a regularized cost function that incorporates amplitude similarity of three ultrasound images and displacement continuity. We optimize the cost function in an expectation maximization (EM) framework. Iteratively reweighted least squares (IRLS) is used to minimize the effect of outliers. An alternative approach for utilizing multiple images is to only consider two frames at any time and sequentially calculate the strains, which are then accumulated. We formally show that, compared to using two images or accumulating strains, the new algorithm reduces the noise and eliminates ambiguities in displacement estimation. The displacement field is used to generate strain images for quasi-static elastography. Simulation, phantom experiments and in-vivo patient trials of imaging liver tumors and monitoring ablation therapy of liver cancer are presented for validation. We show that even with the challenging patient data, where it is likely to have one frame among the three that is not optimal for strain estimation, the introduction of physics-based prior as well as the simultaneous consideration of three images significantly improves the quality of strain images. Average values for strain images of two frames versus ElastMI are: 43 versus 73 for SNR (signal to noise ratio) in simulation data, 11 versus 15 fo

Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment

<div><p>We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.</p></div

Virtual remote center of motion control for needle placement robots

Abstract. Surgical robots, including those with remote center of motion (RCM) mechanisms, have demonstrated utility in image-guided percutaneous needle placement procedures. However, widespread clinical application of these robots is hindered by not only complicated mechanical design but also the need for calibration and registration of the robot to the medical imager prior to each use. In response, we propose a Virtual RCM algorithm that requires only online tracking or registering the surgical tool to the imager, and a five degree-offreedom (DOF) robot comprised of three prismatic DOF and two rotational DOF. The robot can be unencoded, uncalibrated, and does not require preoperative registration. An incremental adaptive motion control cycle both guides the needle to the insertion point and orients it to align with the target. The robot executes RCM motion “virtually ” without having a physically constrained fulcrum point. The proof-of-concept prototype system achieved 0.78 mm translation and 0.78 degrees rotational accuracy (within the tracker accuracy), within 17 iterative steps (0.5-1secs)