Active localization and tracking of needle and target in robotic image-guided intervention systems

Renfrew, Mark; Griswold, Mark A.; Çavuşoğlu, M. Cenk

doi:10.1007/s10514-017-9640-2

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…For example, if the limiting steps are vascular or carotid access, then practical implementation of long distance EVT would have to be delayed until the robotic system is capable of performing these critical tasks. Investigators worldwide have taken on this challenge by incorporating ultrasound image guidance and simultaneous localization and mapping (SLM) into the procedure [10,11]. While image guidance largely facilitates task completion, haptic feedback or the sense of touch, is well known to improve operator performance [3,12].…”

Section: Master-slave Systems and Their Potential Applicationsmentioning

confidence: 99%

Neurointerventional Robotics: Challenges and Opportunities

et al. 2020

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The term "robot" first appeared in a 1920 play by the Czech writer Karel Čapek, titled R.U.R (Rossum's Universal Robots) [1]. It was derived from the Czech word "robota", which means servitude or forced labor. Although the word "robot" is nowadays widely used in everyday language, it is not clearly defined what we actually mean by it. The Robotic Institute of America has defined a robot as "a reprogrammable multifunctional manipulator designed to move materials, parts, tools, or other specialized devices through various programmed motions for the performance of a variety of tasks"[2, 3]. Medical robotics account for 8% of all professional service robot sales, and with increasing popularity, worldwide sales figures increased by 20% from 2011 to 2013. This is mostly due to growing demands in the field of robot-assisted surgery [4], but medical robots are increasingly used for other purposes as well: some hospitals use cleaning robots which emit UV rays for instance, delivery robots transport meals and medication to patients' rooms, and rehabilitation robots have been successfully integrated in many surgical and neurological rehabilitation programs [5]. In the field of neurointervention, robots are clearly on the rise: in the early months of 2020, 3 studies were published in rapid succession that showed safety and

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Section: Master-slave Systems and Their Potential Applicationsmentioning

confidence: 99%

Neurointerventional Robotics: Challenges and Opportunities

et al. 2020

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“…Once the initial 2D image slice plane was selected, it was not necessarily needed to be the same one in the remaining time steps. In a typical application, this varying single image slice could be either manually selected from a stack of slices by a clinician or automatically by an active sensing algorithm 91 . Here, the set of slice planes are selected randomly between the basal and mid-ventricular slices (Fig.…”

Section: Methodsmentioning

confidence: 99%

Deformable cardiac surface tracking by adaptive estimation algorithms

Tuna

Franson

Seiberlich

et al. 2023

Sci Rep

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This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step. For the real cardiac MRI datasets, the average root-mean-square tracking errors of 2.61 mm and 3.42 mm are reported respectively for the fixed and varying image slice planes. This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations via a low-order probabilistic model with the future goal of utilizing this method for the targeted interventional cardiac procedures under MR image guidance. For the real cardiac MRI datasets, the presented method was able to track the points-of-interests located on different sections of the cardiac surface within a precision of 3 pixels. The analyses show that the use of deformable cardiac surface tracking algorithm can pave the way for performing precise targeted intracardiac ablation procedures under MRI guidance. The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.

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“…We see that the estimates will be biased, which did not occur when neglecting the squared noise term. In Section 6.1 we numerically explore the trade-offs from choosing the unbiased, twice-approximated estimator, and this approximation, encoded in (8). Since the method in Section 3.1 uses a well known closed-form estimator, we develop our distributed online active localization algorithm based on the linear model (4).…”

Section: Taking the Quadratic Noise Term (3) Into Considerationmentioning

confidence: 99%

ISEE.U: Distributed online active target localization with unpredictable targets

Vasques¹,

Soares²,

Gomes³

2022

Preprint

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This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not assume specific target dynamics and, thus, our method is robust when facing unpredictable targets. Each agent computes the control that maximizes overall target position accuracy via a local estimate of the Fisher Information Matrix. We compared the proposed method with a state of the art algorithm outperforming it when the target movements do not follow a prescribed trajectory, with x100 less computation time, even when our method is running in one central CPU.

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Active localization and tracking of needle and target in robotic image-guided intervention systems

Cited by 10 publications

References 30 publications

Neurointerventional Robotics: Challenges and Opportunities

Neurointerventional Robotics: Challenges and Opportunities

Deformable cardiac surface tracking by adaptive estimation algorithms

ISEE.U: Distributed online active target localization with unpredictable targets

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