Patient Specific Prediction of Parenchymal Molecular Dispersal Utilizing Medical Images Within Diffusion Modeling Environments

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Authors

Shaw, Caleb

Issue Date

2024

Type

Dissertation

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en_US

Keywords

Computational Fluid Dynamics , Drug Delivery , Finite Element Analysis , Magnetic Resonance Imaging , Molecular Diffusion

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Abstract

Knowledge of solute dispersal within the brain is critically important for drug delivery for cancer treatment and for information of brain functioning. The delivery of drugs into the brain can be systemic or focal; however, for focal delivery, overdosing off-target tissue can induce excess damage. Brain conditions can result in bio-molecule imbalances which can be released into the interstitial matrix which knowledge of this dispersal illuminates location and magnitude of the condition. To predict solute dispersal, computational and in-vitro models of solute dispersal can aid surgical prediction and supplement investigatory work into the parenchymal biomolecular environment. Given the highly heterogeneous nature of the brain, use of individual specific brain tissue reduces error of molecular prediction. To that end, we construct in-vitro and in-silico models of the brain using multiple MRI types to inform individual specific brain structure to guide computational molecular dispersal following the individual’s specific environment. To do this we constructed a molecular dispersal model within a finite element analysis (FEA) software and validated with a hydrogel model to replicate perturbed infusion techniques. Subsequently, using neuro-imaging software, we extracted CAD objects of the pia mater to be reconstructed in-silico and in-vitro to provide the boundary of a molecular dispersal environment. To inform the internal tissue details, we further utilized neuro-imaging software to extract mass data from MRIs to be interpreted as tissue data providing structural details (MPRAGE) and tractography details (DTI). This data was subsequently imported into FEA to guide molecular dispersal.

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