Bio Simulator and 3D Printing

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Overview

Applications of 3D printing to various anatomies in the field of medicine should start with looking at where we would like to be. Alvin Toffler who died a few years ago defined knowledge-based production as a key factor for the third wave. Furthermore, he stated that prosumers based on actionable knowledge had been a primary resource. Prosumer is a compound word made up with the words of producer and consumer, which means that consumers have to engage in manufacturing application of more valuable technology to improve production speed, range of technology, and the quality (1). These things could fit the medical field better than other industries because 3D printing technology is suitable for a patient-specific and small quantity production (2).

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Figure 1: DICOM images and 3D modeling from CT scan imported into MEDIP

Recently a number of 3D printing solutions for medical simulation in conjunction with Seoul National University Hospital (SNUH) have been developed and validated. Through a project called ν 3D project from MEDICALIP, we developed and manufactured a patient-specific 3D printing-based bio simulators with various purposes such as surgical planning, simulation, medical education, and even improving patients’ understanding (3-5). The role of bio simulator has developed and expanded beyond the simple visualization and inspection of anatomical structures in the revolutionizing medical field.

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Figure 2: Image segmentation process from DICOM Image. (A) DICOM image (B) Typical volume rendered image with annotation of anatomical structure. (C) Image segmentation of each anatomical structure. A DICOM image can be segmented into different anatomical structures and exported as STL files. (D) Merging of the anatomical structures in a volume rendered image. LA: Left atria, LV: left ventricle, RA: Right atria, RV: Right ventricle, SVC: Superior vena cava, IVC: Inferior vena cava, STL: Stereolithography or Standard Tessellation Language

Bio Simulator

Post-processing for imagery is the major procedure determining of the 3D-printed product quality out of the given image data. For the best segmentation results, algorithms can be chosen according to the desired goals. This means segmentation have to be performed with understanding the limitations and pitfalls of the imaging modalities as well as the pathologic anatomy of various situations. There are several software programs ranging from expensive high-end commercial or mid-positioning versions to open-source freeware platforms. After that, in order to use post-processed 3D volumetric imagery for 3D printing, it must be converted into a 3D mesh file format such as a STL (Stereolithography or Standard Tessellation Language) or others (OBJ, 3MF, VRML, etc.) because mesh generation for the polygon structure enables more improved computer-aided design than volume data.

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Figure 3: Visualization and planning of the desired dissection plane. (A) Dissection of the 3D segmented heart image. (B) Dissection preview of the STL heart model. (C) STL heart model before dissection. (D) STL heart model after dissection. (E) Separated pair STL heart model. (F) 3D Printed surgical practice model (bio simulator).

The 3D printing process comprises several sequential stages from the CT image to volume and mesh workflows. Also, the printing material should have physical properties such as consistency, elasticity, tensile strength, tear resistance and memory capacity that are similar to those of human soft tissue (6, 7).

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Figure 4: Various applications for 3D printed bio simulation

Conclusion

Safe and effective practice of surgery requires in-depth anatomic knowledge. A thorough understanding of human anatomy empowers medical doctors to plan a suitable approach, predict possible complications, prepare necessary equipment, and properly counsel a patient. An optimized patient-specific 3D printed model is to dramatically increase the success rate of surgery. Preoperative planning with 3D printed bio simulator has improved cancer detection rate by more than 20% (4), and it has decreased cardiac reoperation rate from 56% to 12% (8). A personalized bio simulator was technically implementable and had the potential to create for an individual medical institution which has own specialized needs.

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Figure 5: Surgeon who is practicing esophageal intubation with 3D printed bio simulator in the operating room (Seoul National University Hospital)

References:

  • [1] Toffler A. The third wave. 1st ed. New York: Morrow; 1980. 544 p. p.
  • [2] Berman B. 3-D printing: The new industrial revolution. Bus Horizons. 2012;55(2):155-62.
  • [3] Yoon SH, Park S, Kang CH, Park IK, Goo JM, Kim YT. Personalized 3D-Printed Model for Informed Consent for Stage I Lung Cancer: A Randomized Pilot Trial. Seminars in thoracic and cardiovascular surgery. 2019;31(2):316-8. Epub 2018/11/10.
  • [4] Joo I, Kim JH, Park SJ, Lee K, Yi NJ, Han JK. Personalized 3D-Printed Transparent Liver Model Using the Hepatobiliary Phase MRI: Usefulness in the Lesion-by-Lesion Imaging-Pathologic Matching of Focal Liver Lesions-Preliminary Results. Investigative radiology. 2019;54(3):138-45. Epub 2018/11/01.
  • [5] Yoon SH, Goo JM, Lee CH, Cho JY, Kim DW, Kim HJ, et al. Virtual reality-assisted localization and three-dimensional printing-enhanced multidisciplinary decision to treat radiologically occult superficial endobronchial lung cancer. Thoracic cancer. 2018;9(11):1525-7. Epub 2018/09/27.
  • [6] Yoo SJ, Spray T, Austin EH, 3rd, Yun TJ, van Arsdell GS. Hands-on surgical training of congenital heart surgery using 3-dimensional print models. The Journal of thoracic and cardiovascular surgery. 2017;153(6):1530-40. Epub 2017/03/08.
  • [7] Yoo SJ, Thabit O, Kim EK, Ide H, Yim D, Dragulescu A, et al. 3D printing in medicine of congenital heart diseases. 3D printing in medicine. 2015;2(1):3. Epub 2015/01/01.
  • [8] Ryan J, Plasencia J, Richardson R, Velez D, Nigro JJ, Pophal S, et al. 3D printing for congenital heart disease: a single site’s initial three-year experience. 3D printing in medicine. 2018;4(1):10. Epub 2019/01/17.

About the Author:

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Sang Joon Park is the founder and CEO of MEDICALIP and a professor and vice director of the Biomedical Institution from Seoul National University Hospital in South Korea. Dr. Park graduated in computer science at an engineering school. He then went to Seoul National College of Medicine to obtain his master and Ph.D. in Interdisciplinary Program in Radiation Applied Life Science. His specialty is medical imaging analysis for developing CT/MRI-based imaging biomarker. Last year (2018), he gave lectures on medical 3D printing keynotes at RSNA and he is currently working as a committee member at ISO/TC 261 (Additive Manufacturing) and ASTM (American Society of Testing Materials).

In the recent 5 years, he is leading MEDICALIP, which secured USD 5m in a Series A-B early this year, providing AI-based medical 3D modeling software “MEDIP, medical imaging & printing” for 3D organ printing. Moreover, this software MEDIP turns medical imageries into 3D data using Augmented Reality (AR) and Virtual Reality (VR), which are subsequently used in 3D printing. Dr. Park further noted that this software is feasible for segmenting and classifying the anatomy of an entire body and can be applied in several medical fields. 

MEDICALIP also develops and produces artificial models reflecting an organ’s texture and flexibility via its own 3D printing technology called “ANATDEL, anatomical model”. The models are used for planning surgeries, simulations, and educational purposes. 

Presently, MEDICALIP is the first Asian start-up to be mentioned by Gartner, global research firm, in its Hype Cycle Report, an annual graphical analysis that showcases the technological trends of healthcare providers and in the field of 3D printing, in 2018.

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