Title : "Constraint-based brain biomechanical simulation for the intraoperative brain-shift compensation"

Jury :

 M. Yohan Payan, Directeur de recherche CNRS, TIMC-IMAG, Grenoble - Directeur
 M. Hadrien Courtecuisse, Chargé de recherche INRIA, ICube, Strasbourg - Co-encadrant
 M. Matthieu Chabanas, Maître de conférences Grenoble INP, TIMC-IMAG, Grenoble - Co-encadrant

 M. Louis Collins, Professeur Université McGill, Institut Neurologique de Montréal, Canada - Rapporteur
 Mme Maud Marchal, Maître de Conférences INSA, IRISA, Rennes - Rapporteur
 Mme Ingerid Reinertsen, Chercheuse SINTEF, Trondheim, Norvège - Examinatrice
 M. Olivier Palombi, Professeur CHUGA, Université Grenoble Alpes, Grenoble - Examinateur

Brain-shit, Resection, Intraoperative ultrasound, Biomechanical simulation, Lagrangian multipliers

Abstract

Purpose During brain tumor surgery, planning and guidance are based on preoperative MR exams. The intraoperative deformation of the brain, called brain-shift, however affect the accuracy of the procedure. In this thesis, a brain-shift compensation method integrable in a surgical workflow is presented.

Method Prior to surgery, a patient-specific biomechanical model is built from preoperative images. The geometry of the tissues and blood vessels is integrated. Intraoperatively, navigated ultrasound images are performed directly in contact with the brain. B-mode and Doppler modalities are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A biomechanical simulation is then executed in order to compensate for brain-shift. Several constraints are imposed to the biomechanical model in order to simulate the contacts with the dura mater, register the pre- and intraoperative vascular trees and constrain the cortical surface with the probe footprint. During deep tumors resection, the surgical trajectory is also constrained to remain inside the cavity induced by the resected tissues in order to capture the lateral deformations issued from tissues retraction. Preoperative MR images are finally updated following the deformation field of the biomechanical model.

Results The method was evaluated quantitatively using synthetic and clinical data. In addition, the alignment of the images was qualitatively assessed with respect to surgeons expectations. Satisfactory results, with errors in the magnitude of 2 mm, are obtained after the opening of the dura mater and for the resection of tumors close to the cortical surface. During the resection of deep tumors, while the surgical trajectory enable to capture most of the deformations induced by tissues retraction, several limitations reflects the fact that this retraction is not actually simulated.

Conclusion A new efficient brain-shift compensation method that is integrable in an operating room is proposed in this thesis. The few studied topic of the resection, and more specifically of deep tumors, is also addressed. This manuscript thus present an additional step towards an optimal system in computer assisted neurosurgery.