Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Nadeau, Caroline; Krupa, Alexandre

doi:10.1109/tro.2013.2256690

Cited by 38 publications

(31 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Janvier et al (2014) used a robotic system to autonomously reconstruct a 3D US image of the arteries from the iliac in the lower abdomen down to the popliteal behind the knee; Pahl and Supriyanto (2015) used linear robotic stages to enable autonomous transabdominal ultrasonography of the cervix; Vitrani et al (2005Vitrani et al ( , 2007 implemented an US imagebased visual servoing algorithm for autonomous guidance of an instrument during intracardiac surgery; Mebarki et al (2008Mebarki et al ( , 2010 utilized the concept of US image moments for autonomous visual servoing; Novotny et al (2007) presented a real-time 3D US image-based visual servoing method to guide a surgical instrument to a tracked target location; Abolmaesumi et al (2000) investigated the feasibility of visual servoing for motion in the plane of the US probe in one dimension; Nadeau and Krupa developed US image-based visual servoing techniques, which use image intensities (Nadeau and Krupa, 2013) and moments based on image features (Nadeau and Krupa, 2010); Sauvée et al (2008) introduced visual servoing of instrument motion based on US images through non-linear model predictive control; and Stoll et al (2006) used a line detection algorithm and a passive instrument marker to provide real-time 3D US-based visual servoing of surgical instruments. Our approach differs from those systems because it implements a cooperative control scheme that fuses the US image acquired by an expert, the US probe position, and the contact force on the patient to enable reproducible probe placement -therefore reproducible soft-tissue deformation -with respect to the target organ.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

View full text Add to dashboard Cite

Radiation therapy typically begins with the acquisition of a CT scan of the patient for planning, followed by multiple days where radiation is delivered according to the plan. This requires that the patient be reproducibly positioned (set up) on the radiation therapy device (linear accelerator) such that the radiation beams pass through the target. Modern linear accelerators provide cone-beam computed tomography (CBCT) imaging, but this does not provide sufficient contrast to discriminate many abdominal soft-tissue targets, and therefore patient setup is often done by aligning bony anatomy or implanted fiducials. Ultrasound (US) can be used to both assist with patient setup and to provide real-time monitoring of soft-tissue targets. However, one challenge is that the ultrasound probe contact pressure can deform the target area and cause discrepancies with the treatment plan. Another challenge is that radiation therapists typically do not have ultrasound experience and therefore cannot easily find the target in the US image. We propose cooperative control strategies to address both the challenges. First, we use cooperative control with virtual fixtures (VFs) to enable acquisition of a planning CT that includes the soft-tissue deformation. Then, for the patient setup during the treatment sessions, we propose to use real-time US image feedback to dynamically update the VFs; this co-manipulation strategy provides haptic cues that guide the therapist to correctly place the US probe. A phantom study is performed to demonstrate that the co-manipulation strategy enables inexperienced operators to quickly and accurately place the probe on a phantom to reproduce a desired reference image. This is a necessary step for patient setup and, by reproducing the reference image, creates soft-tissue deformations that are consistent with the treatment plan, thereby enabling real-time monitoring during treatment delivery.

show abstract

Section: Introductionmentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

View full text Add to dashboard Cite

show abstract

“…A more extensive version of this method was studied in , where the method was tested using a six‐DOF robot holding a US probe and tracking a user‐specified region in the US image when a phantom was randomly moved by hand. Nadeau and Krupa also developed an intensity‐based technique to perform visual servoing using 2D and 3D US probes. They implemented hybrid vision/force control to perform positioning and tracking tasks by using the Jacobian matrix relating time variation of pixel intensities to the probe velocity, while regulating the force of probe–tissue interaction to a constant value.…”

Section: Methodsmentioning

confidence: 99%

Visual servoing in medical robotics: a survey. Part II: tomographic imaging modalities - techniques and applications

Azizian

Najmaei

Khoshnam

et al. 2014

Int J Med Robotics Comput Assist Surg

View full text Add to dashboard Cite

show abstract

“…By extracting features from live 2D or 3D images, tracking of various anatomies such as the carotid artery as well as of surgical tools has been achieved based on ultrasound [16,17]. When coupled with a robot control scheme, applications such as organ motion compensation [18] and visibility maintenance in tele-operation [19] become feasible.…”

Section: Related Workmentioning

confidence: 99%

3D ultrasound registration-based visual servoing for neurosurgical navigation

et al. 2017

View full text Add to dashboard Cite

Purpose We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions. Method Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions. Results Despite the varying resolution of the acquired Oliver Zettinig and Benjamin Frisch have contributed equally to this work. Yu-Mi Ryang and Nassir Navab have contributed equally to this work.B Oliver Zettinig 3D sweeps, we achieved direction-independent positioning errors of 0.35±0.19 mm and 0.61 • ±0.45 • , respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of 1.45 ± 0.78 mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of 1.33±0.33 mm, superior to the gold standard. Conclusion The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

show abstract

Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Cited by 38 publications

References 22 publications

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Visual servoing in medical robotics: a survey. Part II: tomographic imaging modalities - techniques and applications

3D ultrasound registration-based visual servoing for neurosurgical navigation

Contact Info

Product

Resources

About