The design and development of novel methodologies and customized materials to fabricate patient-specific 3D printed organ models with integrated sensing capabilities can yield advances in smart surgical aids for preoperative planning and rehearsal. Here, 3D printed prostate models are demonstrated with physical properties of tissue and integrated soft electronic sensors using custom-formulated polymeric inks. The models show high quantitative fidelity in static and dynamic mechanical properties, optical characteristics, and anatomical geometries to patient tissues and organs. The models offer tissue-mimicking tactile sensation and behavior and thus can be used for the prediction of organ physical behavior under deformation. The prediction results show good agreement with values obtained from simulations. The models also allow the application of surgical and diagnostic tools to their surface and inner channels. Finally, via the conformal integration of 3D printed soft electronic sensors, pressure applied to the models with surgical tools can be quantitatively measured.
Bibliographical noteFunding Information:
M.C.M. acknowledges the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (Award No. 1DP2EB020537), and the Army Research Office for the development of the 3D printed soft sensor (Award No. W911NF-15-1-0469). B.M.O. acknowledges support by the National Heart, Lung, and Blood Institute of the National Institutes of Health (Award No. R01HL137204). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. R.M.S. and B.R.K. acknowledge support from the Department of Urology at the University of Minnesota (UMN). C.-C.C acknowledges support from the Rebecca Q. Morgan Foundation. The authors thank D. Sciacca and S. Krishna from the UMN Medical School for their assistance in acquiring prostate tissue. The authors thank Dr. D. Giles from the UMN Polymer Characterization Facility and L. M. Tenorio from UMN Biomedical Engineering for mechanical test support. The authors also thank Dr. J. Nelson from the UMN Characterization Facility and Dr. D. Nedrelow from the UMN Tissue Mechanics Lab for assistance with hardness tests. The authors thank J. Lee from UMN Biomedical Engineering for optical test support. The authors thank T. Reihsen, L. H. Poniatowski, and D. Burke from the University of Washington (UW) Department of Urology for tissue testing, organ MRI processing, and medical application and material suggestions. The authors thank Dr. D. Idiyatullin from UMN Center for Magnetic Resonance Research (CMMR), Department of Radiology for assistance with acquiring the MRI for the 3D printed prostate model. The authors thank J. Grafft from UMN SimPORTAL for assistance with using the endoscope tower station. The authors thank N. J. Bibus from the UMN Medical Center for assistance with acquiring surgical and diagnostic tools. The authors thank Dr. J. Gorman from UMN Mechanical Engineering and J. Malluege from ANSYS, Inc. for FEM simulation assistance. The authors thank S. Thomalla from the Medical Device Center for help with commercial material printing for comparison purposes. The authors also thank Dr. D. Joung, D. Yang, and B. Bogdalek from UMN Mechanical Engineering, for their valuable comments and suggestions during paper preparation. An Institutional Review Board (IRB) review at the University of Minnesota determined that this experiment does not meet the federal definition of human subjects research and, therefore, an IRB review was not required.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
- 3D printing, organ models, 3D printed sensors, surgical aids, soft materials
PubMed: MeSH publication types
- Journal Article
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Supporting data for "3D Printed Organ Models with Physical Properties of Tissue and Integrated Sensors"
Qiu, K., Zhao, Z., Haghiashtiani, G., Guo, S., He, M., Su, R., Zhu, Z., Bhuiyan, D. B., Murugan, P., Meng, F., Park, S. H., Chu, C. C., Ogle, B. M., Saltzman, D. A., Konety, B. R., Sweet, R. M. & McAlpine, M., Data Repository for the University of Minnesota, 2020