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Probabilistic Non-Rigid Registration for Safe Brain Tumor Resection

Project description

Novel image-guided system for neurosurgery

Neuronavigation systems are advanced medical tools used during neurosurgery to aid surgeons in precisely navigating and targeting areas within the brain. These systems integrate preoperative imaging, such as MRI or CT scans, with real-time information during surgery. Although they enhance surgical precision, they cannot account for brain deformation. Funded by the Marie Skłodowska-Curie Actions programme, the SafeREG project proposes to combine computational science and clinical expertise to develop a novel image registration method using intraoperative ultrasound. By focusing on the resection of brain tumours, the goal is to preserve patients’ cognitive function and improve surgical outcomes.

Objective

Brain tumors strike people in the prime of life. Surgical resection is the initial treatment for nearly all brain tumors and aims at maximizing the extent of tumor resection while preserving the patient's cognitive function. To optimize this tradeoff, neuronavigation systems have been developed to provide intraoperative guidance to surgeons. These systems allow for the visualization of the position of surgeons' surgical tools relative to the tumor and critical brain areas visible in preoperative Magnetic Resonance Imaging. However, these systems become inaccurate as the surgery progresses since they do not account for brain deformation and tissue resection occurring during surgery.
In an interdisciplinary effort, project SafeREG combines the researcher's background to the expertise of computational scientists from INRIA and clinicians from Parisian hospitals. Its objective is to invent a novel image registration methodology with intraoperative ultrasound that is rich enough to capture complex deformations occurring at the tumor and resection cavity boundaries, fast enough to be employable clinically, and interpretable enough for informed decision-making by neurosurgeons. This will be accomplished by pushing the envelope of scientific knowledge in (1) cross-modality domain adaptation for weakly- and unsupervised image segmentation; (2) modality-invariance representation learning using contrastive learning; (3) non-rigid registration with discrete probabilistic methods; (4) simulated-based variational inference for registration uncertainty quantification that leverages biomechanical knowledge. This research has the potential to deliver accurate and informed image-guided surgery, conferring a lower risk of new neurologic deficits and improved patient prognosis. Beyond neurosurgery, it has broad applications to additional areas of image-guided therapy, including spine, liver, and prostate surgery.

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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 195 914,88
Address
DOMAINE DE VOLUCEAU ROCQUENCOURT
78153 Le Chesnay Cedex
France

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