Skip to main content

Advertisement

SpringerLink
Log in

CT-Based 3D Printing of the Glenoid Prior to Shoulder Arthroplasty: Bony Morphology and Model Evaluation

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

To demonstrate the 3D printed appearance of glenoid morphologies relevant to shoulder replacement surgery and to evaluate the benefits of printed models of the glenoid with regard to surgical planning. A retrospective review of patients referred for shoulder CT was performed, leading to a cohort of nine patients without arthroplasty hardware and exhibiting glenoid changes relevant to shoulder arthroplasty planning. Thin slice CT images were used to create both humerus-subtracted volume renderings of the glenoid, as well as 3D surface models of the glenoid, and 11 printed models were created. Volume renderings, surface models, and printed models were reviewed by a musculoskeletal radiologist for accuracy. Four fellowship-trained orthopaedic surgeons specializing in shoulder surgery reviewed each case individually as follows: First, the source CT images were reviewed, and a score for the clarity of the bony morphologies relevant to shoulder arthroplasty surgery was given. The volume rendering was reviewed, and the clarity was again scored. Finally, the printed model was reviewed, and the clarity again scored. Each printed model was also scored for morphologic complexity, expected usefulness of the printed model, and physical properties of the model. Mann–Whitney–Wilcoxon signed rank tests of the clarity scores were calculated, and the Spearman’s ρ correlation coefficient between complexity and usefulness scores was computed. Printed models demonstrated a range of glenoid bony changes including osteophytes, glenoid bone loss, retroversion, and biconcavity. Surgeons rated the glenoid morphology as more clear after review of humerus-subtracted volume rendering, compared with review of the source CT images (p = 0.00903). Clarity was also better with 3D printed models compared to CT (p = 0.00903) and better with 3D printed models compared to humerus-subtracted volume rendering (p = 0. 00879). The expected usefulness of printed models demonstrated a positive correlation with morphologic complexity, with Spearman’s ρ 0.73 (p = 0.0108). 3D printing of the glenoid based on pre-operative CT provides a physical representation of patient anatomy. Printed models enabled shoulder surgeons to appreciate glenoid bony morphology more clearly compared to review of CT images or humerus-subtracted volume renderings. These models were more useful as glenoid complexity increased.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Horton K, Fishman E: Pictorial essay: Multidetector CT angiography of pancreatic carcinoma: Part i, evaluation of arterial involvement. Am J Roentgenol. 178:827–831, 2002

    Article  Google Scholar 

  2. Beebe H, Kritpracha B: Computed tomography scanning for endograft planning: Evolving toward three-dimensional, single source imaging. Sem Vasc Surg. 17:126–134, 2004

    Article  Google Scholar 

  3. Radtke A, Sotiropoulos G, Molmenti E et al.: Computer-assisted surgery planning for complex liver resections. When it is helpful? A single-center experience over an 8-year period. Ann Surg. 252:876–883, 2010

    Article  PubMed  Google Scholar 

  4. Schienfeld M, Dym A, Spektor M, Avery L, Dym R, Amanatullah D: Acetabular fractures: What radiologists should know and how 3D CT can aid classification. RadioGraphics. 35:555–577, 2015

    Article  Google Scholar 

  5. Kim M, Hangsen A, Wink O, Quaife R, Carroll J: Rapid prototyping: A new tool in understanding and treating structural heart disease. Circulation. 117:2388–2394, 2008

    Article  PubMed  Google Scholar 

  6. Bibb R, Eggbeer D, Evans P: Rapid prototyping technologies in soft tissue facial prosthetics: Current state of the art. Rapid Prototyping Journal. 16:130–137, 2010

    Article  Google Scholar 

  7. Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T, Kumamaru KK, George E, Wake N, Caterson EJ, Pomahac B, Ho VB, Grant GT, Rybicki FJ: Medical 3D printing for the radiologist. RadioGraphics. 35:1965–1988, 2015

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wake N, Rude T, Kang S et al.: 3D printed renal cancer models derived from MRI data: Application in pre-surgical planning. Abdom Radiol. 42:1501–1509, 2017

    Article  Google Scholar 

  9. Norris T, Iannotti J: Functional outcome after shoulder arthroplasty for primary osteoarthritis: A multicenter study. J Shoulder Elbow Surg. 11:130–135, 2002

    Article  PubMed  Google Scholar 

  10. Strauss E, Roche C, Flurin P, Wright T, Zuckerman J: The glenoid in shoulder arthroplasty. J Shoulder Elbow Surg. 18:819–833, 2009

    Article  PubMed  Google Scholar 

  11. Matsen, III F, Bicknell R, Lippitt S: Shoulder arthroplasty: The socket perspective. J Shoulder Elbow Surg. 16:241S–247S, 2007

    Article  Google Scholar 

  12. Boileau P, Sinnerton R, Chuinard C, Walch G: Arthroplasty of the shoulder. J Bone Joint Surg. 88:562–575, 2006

    Article  CAS  Google Scholar 

  13. Matsen, III F: The ream and run: Not for every patient, every surgeon or every problem. International Orthopaedics. 39:255–261, 2015

    Article  PubMed  Google Scholar 

  14. Edwards T: CORR insights: Can the ream and run procedure improve glenohumeral relationships and function shoulders with the arthritic triad? Clin Orthop Relat Res. 473:2097–2098, 2015

    Article  PubMed  PubMed Central  Google Scholar 

  15. Frankle M, Siegal S, Pupello D, Saleem A, Mighell M, Vasey M: The reverse shoulder prosthesis for glenohumeral arthritis associated with severe rotator cuff deficiency. A minimum two-year follow-up study of sixty patients. J Bone Joint Surg. 87:1697–1705, 2005

    PubMed  Google Scholar 

  16. Boileau P, Watkinson D, Hatzidakis A, Balg F. Grammont reverse prosthesis: Design, rationale, and biomechanics. J Shoulder Elbow Surg. 2005;14:147S–161S, S147, S161.

  17. Roberts C, Ekelund A, Renfree K, Liu P, Chew F: Radiologic assessment of reverse shoulder arthroplasty. RadioGraphics. 27:223–235, 2007

    Article  PubMed  Google Scholar 

  18. Farshad M, Gerber C: Reverse total shoulder arthroplasty—From the most to the least common complication. International Orthopaedics. 34:1075–1082, 2010

    Article  PubMed  PubMed Central  Google Scholar 

  19. Thompson T, Greenspoon J, Hanypsiak B, Lederman E: Reverse total shoulder arthroplasty: A comprehensive review of biomechanics, surgical technique, and potential complications. Curr Orthop Pract. 25:580–588, 2014

    Article  Google Scholar 

  20. Simon P, Gupta A, Pappou I, Hussey MM, Santoni BG, Inoue N, Frankle MA: Glenoid subchondral bone density distribution in male total shoulder arthroplasty subjects with eccentric and concentric wear. J Shoulder Elbow Surg. 24:416–424, 2015

    Article  PubMed  Google Scholar 

  21. Knowles N, Athwal G, Keener J, Ferreira L: Regional bone density variations in osteoarthritic glenoids: A comparison of symmetric to asymmetric (type B2) erosion patterns. J Shoulder Elbow Surg 24:425–432, 2015

    Article  PubMed  Google Scholar 

  22. Scalise J, Codsi M, Bryan J, Iannotti J: The three-dimensional glenoid vault model can estimate normal glenoid version in osteoarthritis. J Shoulder Elbow Surg. 17:487–491, 2008

    Article  PubMed  Google Scholar 

  23. Friedman R, Hawthorne K, Genez B: The use of computerized tomography in the measurement of glenoid version. J Bone Joint Surg. 74:1032–1037, 1992

    Article  CAS  PubMed  Google Scholar 

  24. Churchill R, Brems J, Kotschi H: Glenoid size, inclination, and version: An anatomic study. J Shoulder Elbow Surg. 10:327–332, 2001

    Article  CAS  PubMed  Google Scholar 

  25. Inui H, Sugamoto K, Miyamoto T, Machida A, Hashimoto J, Nobuhara K: Evaluation of the three-dimensional glenoid structure using MRI. J Anat. 199:323–328, 2001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Steinmann S, Cofield R: Bone grafting for glenoid deficiency in total shoulder replacement. J Shoulder Elbow Surg. 9:361–367, 2000

    Article  CAS  PubMed  Google Scholar 

  27. https://www.rsna.org/ctp.aspx. Radiological Society of North America: MIRC Clinical Trials Processor (CTP). Accessed 29 August 2017.

  28. Friedman T, Michalski M, Goodman T, Brown J: 3D printing from diagnostic images: A radiologist’s primer with an emphasis on musculoskeletal imaging—Putting the 3D printing of pathology into the hands of every physician. Skeletal Radiol. 45:307–321, 2016

    Article  PubMed  Google Scholar 

  29. Riesenkampff E, Rietdorf U, Wolf I, Schnackenburg B, Ewert P, Huebler M, Alexi-Meskishvili V, Anderson RH, Engel N, Meinzer HP, Hetzer R, Berger F, Kuehne T: The practical clinical value of three-dimensional models of complex congenitally malformed hearts. J Thoracic Cardiovasc Surg. 138:571–580, 2009

    Article  Google Scholar 

  30. Matsumoto J, Morris J, Foley T et al.: Three-dimensional physical modeling: Application and experience at mayo clinic. RadioGraphics. 35:1989–2006, 2015

    Article  PubMed  Google Scholar 

  31. Rose A, Webster C, Harryson O, Formeister E, Rawal R, Iseli C: Pre-operative simulation of pediatric mastoid surgery with 3D-printed temporal bone models. Intl J Ped Otorhinolaryngology. 70:740–744, 2015

    Article  Google Scholar 

  32. Gauci M, Boileau P, Baba M, Chaoui J, Walch G: Patient-specific glenoid guides provide accuracy and reproducibility in total shoulder arthroplasty. Bone Joint J. 98-B:1080–1085, 2016

    Article  CAS  PubMed  Google Scholar 

  33. Podolsky D, Martin A, Whyne C, Massicotte E, Hardisty M, Ginsberg H: Exploring the role of 3-dimensional simulation in surgical training: Feedback from a pilot study. J Spinal Disord Tech. 23:e70–e74, 2010

    Article  PubMed  Google Scholar 

  34. Waran V, Narayanan V, Karuppiah R, Owen S, Aziz T: Utility of multimaterial 3D printers in creating models with pathologica entities to enhance the training experience of neurosurgeons. J Neurosurg. 120:489–492, 2014

    Article  PubMed  Google Scholar 

  35. Waran V, Narayanan V, Karuppiah R, Pancharatnam D, Chandran H, Raman R, Rahman ZAA, Owen SLF, Aziz TZ: Injecting realism in surgical training—Initial simulation experience with custom 3D models. J Surg Edu. 71:193–197, 2014

    Article  Google Scholar 

  36. Bader C, Kolb D, Weaver JC, Sharma S, Hosny A, Costa J, Oxman N: Making data matter: Voxel printing for the digital fabrication of data across scales and domains. Sci Adv. 4:eaas8652, 2018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors acknowledge 3D Systems for providing software and 3D printing services used in this research.

Author information

Authors and Affiliations

  1. Baltimore VA Medical Center, Baltimore, MD, USA

    Kenneth C. Wang, Mohit N. Gilotra, Natalie Leong, S. Ashfaq Hasan & Eliot L. Siegel

  2. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA

    Kenneth C. Wang & Eliot L. Siegel

  3. Department of Pathology and Laboratory Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA

    Anja Jones

  4. Southeast Radiology, Chester, PA, USA

    Shivkumar Kambhampati

  5. Department of Orthopaedics, University of Maryland, School of Medicine, Baltimore, MD, USA

    Mohit N. Gilotra, Brian Shiu, Natalie Leong & S. Ashfaq Hasan

  6. 3D Medical Applications Center, Department of Radiology, Walter Reed National Military Medical Center, Radiology and Radiological Services & Naval Postgraduate Dental School, Uniform Services University of the Health Sciences, Bethesda, MD, USA

    Peter C. Liacouras

  7. Guthrie Clinic, Sayre, PA, USA

    Satre Stuelke

Authors
  1. Kenneth C. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anja Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shivkumar Kambhampati
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohit N. Gilotra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peter C. Liacouras
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Satre Stuelke
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Brian Shiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Natalie Leong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. S. Ashfaq Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Eliot L. Siegel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenneth C. Wang.

Ethics declarations

Conflict of Interest

3D Systems provided access to the DICOM-to-Print software and also printed one model presented in this work. The authors retained full control of all aspects of the study. One author reports grant funding from the Department of Veterans Affairs, and the Orthopaedic Research and Education Foundation, for other work relating to shoulder disorders. Another author reports grant funding from the National Center for Defense Manufacturing and Machining for other work relating to 3D printing.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K.C., Jones, A., Kambhampati, S. et al. CT-Based 3D Printing of the Glenoid Prior to Shoulder Arthroplasty: Bony Morphology and Model Evaluation. J Digit Imaging 32, 816–826 (2019). https://doi.org/10.1007/s10278-019-00177-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-019-00177-4

Keywords

Search

Navigation