Bossone Research Center, Room 709, located at 32nd and Market Streets.
BIOMED PhD Thesis Defense
Title:
Finite Element and Neural-Network-Based Modeling for Simulation of Scoliosis Progression and Correction
Speaker:
Christian D'Andrea, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
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Advisor:
Sriram Balasubramanian, PhD
Professor
School of Biomedical Engineering, Science and Health Systems
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Details:
Spine fusion is the standard surgical treatment for correction of severe adolescent scoliosis, but it suppresses remaining growth. Growth-modulating approaches such as vertebral body tethering (VBT) have been developed to mitigate fusion downsides, acting through both on-table correction and sustained asymmetric mechanical alteration of vertebral growth to correct curvature over years. These procedures present a planning challenge by requiring a surgeon to anticipate the correction occurring post-operatively due to years of stress-altered growth. Incomplete curve correction and implant breakage have been observed, so a tool is needed to assist surgeons in planning such procedures by predicting long-term growth-based correction, while accounting for patient-specific factors including flexibility and skeletal maturity.
This work laid a foundation for such a simulation tool, developing finite element (FE) models which were personalized to the detailed spine geometry of 20 VBT patients and calibrated to match their correction due to both surgery and subsequent stress-modulated growth. Beyond VBT, new growth-modulating implants are being developed, and are tested using expensive and time-consuming porcine in vivo testing. Here, FE models may accelerate growth-modulating implant development by screening implant designs prior to animal testing. Hence, similar FE modeling approaches to those used for VBT were applied here to develop and calibrate porcine-specific models to reproduce scoliosis induction observed in vivo after implantation of a novel tether-based device.
Conventional FE methods still may take hours to solve, which excludes possible intra-operative use for adaptive real-time surgical planning. Neural-network-based methods have been demonstrated to learn an FE model's solutions, but still often require thousands of conventional FE evaluations for training. Training neural nets directly using material constitutive laws and without use of conventional FE solutions may enable faster training, but this approach is not well-explored in biomechanics. Such applications to intervertebral disc deformation and lumbar spine bending range of motion were therefore developed here, demonstrating similar accuracy and increased speed compared to conventional methods.
Taken together, this work develops toward pre-clinical, pre-operative, and intra-operative applications of spine biomechanical simulation with focus on pediatric scoliosis correction with growth modulation.