Abstract

Local drug delivery to the inner ear via micropump implants has the potential to be much more effective than oral drug delivery for treating patients with sensorineural hearing loss and to protect hearing from ototoxic insult due to noise exposure. Delivering appropriate concentrations of drugs to the necessary cochlear compartments is of paramount importance; however, directly measuring local drug concentrations over time throughout the cochlea is not possible. Indirect measurement using otoacoustic emissions and auditory brainstem response are ineffective as they only provide an estimate of concentration and are susceptible to non-linear sensitivity effects. Imaging modalities such as MRI with infused gadolinium contrast agent are limited due to the high spatial resolution requirement for pharmacokinetic analysis, especially in mice with cochlear length in the micron scale. We develop an intracochlear pharmacokinetic model using micro-computed tomography imaging of the cochlea during in vivo infusion of a contrast agent at the basal end of scala tympani through a cochleostomy. This approach requires accurately segmenting the main cochlear compartments: scala tympani (ST), scala media (SM) and scala vestibuli (SV). Each scan was segmented using 1) atlas-based deformable registration, and 2) V-Net, a encoder-decoder style convolutional neural network. The segmentation of these cochlear regions enable concentrations to be extracted along the length of each scala. These spatio-temporal concentration profiles are used to learn a concentration dependent diffusion coefficient, and transport parameters between the major scalae and to clearance. The pharmacokinetic model results are comparable to the current state of the art model, and can simulate concentrations for cases involving different infusion molecules and drug delivery protocols. While our model shows promising results, to extend the approach to larger animals and to generate accurate further experimental data, computational constraints, and time requirements of previous segmentation methods need to be mitigated. To this end, we extended the V-Net architecture with inclusion of spatial attention. Moreover, to enable segmentation in hardware restricted environments, we designed a 3D segmentation network using Capsule Networks that can provide improved segmentation performance along with 90% reduction in trainable parameters. Finally, to demonstrate the effectiveness of these networks, we test them on multiple public datasets. They are also tested on the cochlea dataset and pharmacokinetic model simulations will be validated against existing results.

Publication Date

7-21-2022

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Nathan Cahill

Advisor/Committee Member

Bonnie Jacob

Advisor/Committee Member

Christian A. Linte

Campus

RIT – Main Campus

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