Position-based modeling of lesion displacement in ultrasound-guided breast biopsy

Tagliabue, Eleonora; Dall’Alba, Diego; Magnabosco, Enrico; Tenga, Chiara; Fiorini, Paolo

doi:10.1007/s11548-019-01997-z

Cited by 16 publications

(13 citation statements)

References 29 publications

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“…Besides accurate 3D volumes, this study will also benefit the multimodal image fusion. The two typical clinical applications are image-guided intervention for soft tissues like breasts [18], [19], and imaging-guided orthopedic surgery [12]. For the former one, CT or MR is often used to provide high-resolution anatomies, while US images provide a live view during the intervention.…”

Section: Discussionmentioning

confidence: 99%

Deformation-Aware Robotic 3D Ultrasound

Jiang,

Zhou,

et al. 2021

Preprint

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Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the inconsistent location and geometry. This work proposes a patientspecified stiffness-based method to correct the tissue deformations in robotic 3D US acquisitions. To obtain the patient-specified model, robotic palpation is performed at sampling positions on the tissue. The contact force, US images and the probe poses of the palpation procedure are recorded. The contact force and the probe poses are used to estimate the nonlinear tissue stiffness. The images are fed to an optical flow algorithm to compute the pixel displacement. Then the pixel-wise tissue deformation under different forces is characterized by a coupled quadratic regression. To correct the deformation at unseen positions on the trajectory for building 3D volumes, an interpolation is performed based on the stiffness values computed at the sampling positions. With the stiffness and recorded force, the tissue displacement could be corrected. The method was validated on two blood vessel phantoms with different stiffness. The results demonstrate that the method can effectively correct the force-induced deformation and finally generate 3D tissue geometries.

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Section: Discussionmentioning

confidence: 99%

Deformation-Aware Robotic 3D Ultrasound

Jiang,

Zhou,

et al. 2021

Preprint

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“…a) White matter parameters calibration: We created a scene in Unity containing the previously mentioned parallelepiped-shaped simulated phantom. To describe the entire model as deformable, we constrain all the particles to fall within at least one cluster by imposing cluster radius to be at least half of cluster spacing (set to [5,35] mm), as proposed in [31]. Consequently, cluster radius is restricted to the range [2.5, 35] mm to keep the simulation stable; the upper limit is coincident with the cluster spacing one to maintain an overlap between the various clusters.…”

Section: B Case Study: Keyhole Neurosurgerymentioning

confidence: 99%

Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

Segato

Vece

Zucchelli

et al. 2021

IEEE Robot. Autom. Lett.

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Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, timebounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73±2.15%. The stability, accuracy, and real-time performance make this model suitable for creating a dynamic environment for KN path planning, pre-operative path planning, and intra-operative guidance.

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“…Modelling methods that do not rely on continuum mechanics laws have also been used to approximate soft tissues behavior. Among these, the position-based dynamics (PBD) approach has been used to predict breast lesions displacement due to US probe pressure in realtime, providing comparable accuracy with FE models [21]. However, not being based on real mechanical properties, such model requires an initial optimization of simulation parameters to obtain a realistic description of the deformation.…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Deep Neural Network for Real-Time Lesion Tracking in Ultrasound-Guided Breast Biopsy

Mendizábal

Tagliabue

Brunet

et al. 2020

Computational Biomechanics for Medicine

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In the context of ultrasound (US) guided breast biopsy, image fusion techniques can be employed to track the position of USinvisible lesions previously identified on a pre-operative image. Such methods have to account for the large anatomical deformations resulting from probe pressure during US scanning within the real-time constraint. Although biomechanical models based on the finite element (FE) method represent the preferred approach to model breast behavior, they cannot achieve real-time performances. In this paper we propose to use deep neural networks to learn large deformations occurring in ultrasoundguided breast biopsy and then to provide accurate prediction of lesion displacement in real-time. We train a U-Net architecture on a relatively small amount of synthetic data generated in an offline phase from FE simulations of probe-induced deformations on the breast anatomy of interest. Overall, both training data generation and network training are performed in less than 5 hours, which is clinically acceptable considering that the biopsy can be performed at most the day after the pre-operative scan. The method is tested both on synthetic and on real data acquired on a realistic breast phantom. Results show that our method correctly learns the deformable behavior modelled via FE simulations and is able to generalize to real data, achieving a target registration error comparable to that of FE models, while being about a hundred times faster.

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Position-based modeling of lesion displacement in ultrasound-guided breast biopsy

Cited by 16 publications

References 29 publications

Deformation-Aware Robotic 3D Ultrasound

Deformation-Aware Robotic 3D Ultrasound

Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

Physics-Based Deep Neural Network for Real-Time Lesion Tracking in Ultrasound-Guided Breast Biopsy

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