Towards a deformable multi-surface approach to ligamentous spine models for predictive simulation-based scoliosis surgery planning

Audette, Michel A. (Old Dominion University, Norfolk, USA) ; Schmid, Jerome (Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland) ; Goodmurphy, Craig (Eastern Virginia Medical School, Norfolk, USA) ; Polanco, Michael (Old Dominion University, Norfolk, USA) ; Bawab, Sebastian (Old Dominion University, Norfolk, USA) ; Tapp, Austin (Old Dominion University, Norfolk, USA) ; St-Clair, H. Sheldon (Children’s Hospital of the King’s Daughters, Norfolk, USA)

Scoliosis correction surgery is typically a highly invasive procedure that involves either an anterior or posterior release, which respectively entail the resection of ligaments and bone facets from the front or back of the spine, in order to make it sufficiently compliant to enable the correction of the deformity. In light of progress in other areas of surgery in minimally invasive therapies, orthopedic surgeons have begun envisioning computer simulation-assisted planning that could answer unprecedented what-if questions. This paper presents preliminary steps taken towards simulation-based surgery planning that will provide answers as to how much anterior or posterior release is truly necessary, provided we also establish the amplitude of surgical forces involved in corrective surgery. This question motivates us to pursue a medical image-based anatomical modeling pipeline that can support personalized finite elements simulation, based on models of the spine that not only feature vertebrae and inter-vertebral discs (IVDs), but also descriptive ligament models. This paper suggests a way of proceeding, based on the application of deformable multi-surface Simplex model applied to a CAD-based representation of the spine that makes explicit all spinal ligaments, along with vertebrae and IVDs. It presents a preliminary model-based segmentation study whereby Simplex meshes of CAD vertebrae are registered to the subject’s corresponding vertebrae in CT data, which then drives ligament and IVD model registration by aggregation of neighboring vertebral transformations. This framework also anticipates foreseen improvements in MR imaging that could achieve better contrasts in ligamentous tissues in the future.


Keywords:
Conference Type:
full paper
Faculty:
Santé
School:
HEdS - Genève
Institute:
Aucun institut
Publisher:
Cham, Springer
Date:
2019-01
Cham
Springer
Granada, Spain
16 september 2018
Pagination:
13 p.
Published in:
Computational Methods and Clinical Applications for Spine Imaging
Numeration (vol. no.):
2019, pp. 90-102
Series Statement:
Lecture Notes in Computer Science, vol. 11397
DOI:
ISSN:
1611-3349
ISBN:
978-3-030-13735-9
Appears in Collection:



 Record created 2019-07-17, last modified 2019-07-19

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