A method for the assessment of time-varying brain shift during navigated epilepsy surgery

Momi, Elena De; Ferrigno, Giancarlo; Bosoni, Giampiero; Bassanini, P.; Blasi, P.; Casaceli, Giuseppe; Fuschillo, Dalila; Castana, Laura; Cossu, Massimo; Russo, Giorgio Lo; Cardinale, Francesco

doi:10.1007/s11548-015-1259-1

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…Other brain components were analyzed through a 3D multi-frequency Magnetic Resonance Elastography (MRE), and values of the complex shear modulus between 1.058 kP a for the thalamus and 0.649 kP a for the caudate were observed. The stiffness values for the cerebellum and the brainstem are slightly higher, as measured by [5]. According to this possible subdivision of the brain into areas with similar stiffness present in the literature, we tuned the FleX asset parameters of the NVIDIA FleX framework to give a simulation behavior as close as possible to reality.…”

Section: B Case Study: Keyhole Neurosurgerymentioning

confidence: 91%

“…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%

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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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Section: B Case Study: Keyhole Neurosurgerymentioning

confidence: 91%

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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show abstract

“…However, IGN systems have a major drawback since all these systems use pre-operative imaging on which the planning and interventional clinical phases are based. Neurosurgical manipulation, swelling due to osmotic drugs as well as anesthesia cause brain movements, known as "brain-shift", which dramatically limits the utility of pre-operative imaging for neurosurgical navigation [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

Zeineldin¹,

Karar²,

Elshaer³

et al. 2021

IEEE Access

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Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multilocation databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.

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“…Even with the latest intraoperative 3D imaging technology, such tracking is limited in spatial and temporal resolution, expensive, and thereby not always feasible [13]. Currently available imaging technologies for direct tracking of the intraoperative motion of anatomical targets exhibit important limitations: i) ionizing radiation exposure in X-ray and Computed Tomography (CT) [14,15]; ii) noisy images and inability to penetrate the bone/skull in Ultrasound [16]; iii) slow acquisition (order of at least several minutes) in Magnetic Resonance Imaging (MRI) [17][18][19]; iv) ability to track only the surgically exposed organ surface in navigation systems using cameras [20].…”

Section: Introductionmentioning

confidence: 99%

“…Currently available imaging technologies for direct tracking of the intraoperative motion of anatomical targets exhibit important limitations: i) ionizing radiation exposure in X-ray and Computed Tomography (CT) (Mathews et al, 2013;Power et al, 2016); ii) noisy images and inability to penetrate the bone/skull in Ultrasound (Sastry et al, 2017); iii) slow acquisition (order of at least several minutes) in Magnetic Resonance Imaging (MRI) (Elgezua et al, 2013;Lu et al, 2018;Miner, 2017); iv) ability to track only the exposed (during surgery) organ surface in navigation systems using cameras (De Momi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and computer simulation of needle insertion into soft tissue

et al. 2020

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In this study we present a kinematic approach for modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) method to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method’s insensitivity to assumed mechanical properties of tissue.

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A method for the assessment of time-varying brain shift during navigated epilepsy surgery

Cited by 12 publications

References 32 publications

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

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

iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

Mathematical modeling and computer simulation of needle insertion into soft tissue

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