Preprocessing of Medical Image Data for Three-Dimensional Bioprinted Customized-Neural-Scaffolds

Abstract

Despite the promising nerve regeneration research, the lack of a therapeutic scaffold to completely restore neurological function is still ongoing and the demand in providing patients with a successful therapeutic outcome is ever increasing. With the advancements in three-dimensional (3D) bioprinting, custom scaffolds with a predetermined size and shape have been successfully fabricated, which can be utilized in nerve damage regenerative attempts to potentially aid in the regaining of nerve function. With the documented success of graphical processing unit (GPU) implementation utilized for image-guided surgeries of tubular and organ structures, we propose the implementation of known processing methods as a means to drastically decrease the time required to process medical images related to nerve damage. In addition, we further propose that the merging of medical image cropping and 3D printing techniques provides a novel approach for providing patient-specific customized-neural-scaffolds for patients suffering with newly acquired nerve damage. Finally, we provide a proposed schematic that incorporates the implementation of GPUs and 3D printing, which we propose will beneficially decrease the waiting times for medical staff to provide patients with customized neural treatments.

Impact Statement

Nerve damage, which can be devastating, triggers several biological cascades, which result in the insufficiencies of the human nervous system to provide complete nerve repair and regain of function. Since no therapeutic strategy exists to provide immediate attention and intervention to patients with newly acquired nerve damage, we propose a strategy in which accelerated medical image processing through graphical processing unit implementation and three-dimensional printing are combined to produce a time-efficient, patient-specific (custom-neural-scaffold) solution to nerve damage. This work aims to beneficially shorten the time required for medical decision-making so that improved patient outcomes are achieved.