• Corpus ID: 109931542

Fast Robotic Soft Matter 3 D Printing for Patient-specific Surgical Phantoms

@inproceedings{Chang2018FastRS,
  title={Fast Robotic Soft Matter 3 D Printing for Patient-specific Surgical Phantoms},
  author={Michael C. Chang and Sean R. Niemi and Christopher P. Kabb and Thomas E. Angelini and Francis J. Bova and Scott A. Banks},
  year={2018}
}

• Michael C. Chang, Sean R. Niemi, +3 authors S. Banks
• Published 2018
• Materials Science, Medicine

The recent introduction of three-dimensional (3D) printing (also known as additive manufacturing) techniques into the field of medicine and neurosurgery has provided methods for fabricating patient-specific models for neurosurgical training, teaching, simulation, and pre-surgical planning. Soft matter technology, using a granular gel as the supporting material for 3D printing photopolymer hydrogels, now permits printing of anatomically complex models with realistic textures and tactile properties. However, there hasn’t been a promising soft matter 3D printing system that could be used for fabricating neurosurgical patientspecific models. A major limitation is that current soft matter 3D printing technologies are unavailable to directly print these models with the same anatomical size in a timely fashion. The aim of this project is to create a robotic soft matter 3D printing system using the soft matter 3D printing technology for fast fabrication of patient-specific models with anatomically realistic appearance and textures. The prototype system consists of a SCARA (4-axis) robotic arm, a large-volume-closed-loop-pressure-controlled hydrogel dispensing pump, and a high-level controller for coordinating and synchronizing the robot and the pump. The method consists of path planning for 3D printing, fast motion control of the robotic arm, and precise hydrogel dispensing control. Models are directly fabricated from photopolymerizable hydrogel extruded into the granular gel. A 10-cubic-inch-sized model can be printed in 5 minutes. It significantly improves the time and cost for fabricating the same sized model, making it feasible to fabricate neurosurgical anatomical models for surgeons to practice/prepare in advance of surgical cases, or for realistic teaching exercises.