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Case report
Patient-specific 3D-printed Helmet for Post-craniectomy Defect – A Case Report
Evan Fang
University of Hong Kong Faculty of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0034-0753
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Patients who undergo decompressive craniectomy (DC) are often fitted with a protective helmet that protects the craniectomy site from injury during rehabilitation. However, conventional “one-size-fits-all” helmets may not be feasible for certain craniectomy defects. We describe the production and use of a custom 3D-printed helmet for a DC patient where a conventional helmet was not feasible due to the craniectomy defect configuration.
Case presentation: A 65-year-old male with ethmoid sinonasal carcinoma underwent cranionasal resection and DC with free vastus lateralis flap reconstruction to treat cerebrospinal fluid leakage. He required an external helmet to protect the craniectomy site, however, the rim of a conventional helmet compressed the craniectomy site, and the straps compressed the vascular pedicle of the muscle flap. Computed topography (CT) scans of the patient’s cranium were imported into 3D modelling software and used to fabricate a patient-specific, strapless helmet using fused deposition modelling (FDM). The final helmet fit the patient perfectly and circumvented the compression issues, while also providing better cosmesis than the conventional helmet. Four months postoperatively, the helmet remains intact and in use.
Conclusions: 3D printing can be used to produce low-volume, patient-specific external devices for rehabilitation where standardized adjuncts not optimal. Once initial start-up costs and training are overcome, these devices can be produced by surgeons themselves to meet a wide range of clinical needs.
Keywords
3D printing, skull defect, protective helmet, decompressive craniectomy
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Neha Sharma
ORCiDcommented on 28 January, 2021
The article deals with an interesting topic in which the authors describe their digital workflow to fabricate a 3D printed customized helmet in a patient with a post-craniectomy defect. I think the work can benefit substantially if the following issues/uncertainties in describing the workflow are addressed. 1. The authors created a 3D skin surface rendering of the patient’s head based on CT datasets, and 3D printed a head model. This printed head was then optically scanned to create a digital model, which was later used for the helmet modeling. Why wasn’t the optical scanner used for digitizing the patient to get a face scan model? Several authors have evaluated and suggested that the optical datasets have higher accuracy for face scans. Besides, the optical face scan can be superimposed and merged onto the CT datasets and could have been utilized for the helmet 3D modeling. In the end, only a helmet could have been 3D printed to support the digital workflow with minimal manual work. This could have saved the additional printing time/cost needed to print a head model. Bohner L, Gamba DD, Hanisch M, Marcio BS, Tortamano Neto P, Laganá DC, Sesma N. Accuracy of digital technologies for the scanning of facial, skeletal, and intraoral tissues: A systematic review. J Prosthet Dent. 2019, 121, 246-251. doi:10.1016/j.prosdent. Sharma N, Welker D, Cao S, von Netzer B, Honigmann P, Thieringer F. (2021) An Interactive, Fully Digital Design Workflow for a Custom 3D Printed Facial Protection Orthosis (Face Mask). In: Meboldt M., Klahn C. (eds) Industrializing Additive Manufacturing. AMPA 2020. Springer, Cham. doi:10.1007/978-3-030-54334-1_3. Sharma A, Sasaki D, Rickey DW, Leylek A, Harris C, Johnson K, Alpuche Aviles JE, McCurdy B, Egtberts A, Koul R, Dubey A. Low-cost optical scanner and 3-dimensional printing technology to create lead shielding for radiation therapy of facial skin cancer: First clinical case series. Adv Radiat Oncol. 2018 Feb 14;3(3):288-296. doi:10.1016/j.adro.2018.02.003. Briggs M, Clements H, Wynne N, Rennie A, Kellett D. 3D printed facial laser scans for the production of localised radiotherapy treatment masks - A case study. J Vis Commun Med. 2016 Aug-Oct;39(3-4):99-104. doi: 10.1080/17453054.2016.1246061. 2. What was the rationale behind the manual adaptation of a 6 mm layer of plastozote on the 3D printed head model? This aspect (manual labor and incurred consumable cost) could have been mitigated by modeling an offset of 6 mm on the digital 3D head model, similar to the digitally created 2 mm gap to accommodate the patient’s hair, as illustrated in Figure 4. 3. It is somehow unclear if the 3D head model was printed, including the hair's offset. If not, how was it compensated and corroborates with the authors’ digital workflow? 4. On lines 89-90, the authors state “a 512 x 512 region of interest (ROI) resolution” for CT DICOM parameters. Kindly rephrase ROI to matrix. 5. On line 101, the author state, “The 3D head model was printed for helmet design and liner fabrication.” What were the printing parameters/material/technology used for the head model? Kindly specify. 6. On lines 115-116, the authors state, “The helmet was lined with the foam insert and tested for fitting on the 3D head model”. It is clear that a foam lining needs to be inserted on the helmet's inner aspect. But how did the authors assessed the fit on the 3D printed head model? Wasn’t the 3D printed head model already adapted and glued with a 6 mm foam lining. Kindly rephrase or elaborate. 7. Were any post-processing procedures done for the 3D printed helmet? 8. On lines 152-154, the authors state, “Nonetheless, with training and initial setup costs overcome, the material cost of our helmet was 58 euros, design time was approximately 3 hours, and machine time was 11 hours”. This workflow is based on first printing a 3D head model. Therefore, it would be beneficial for the readers to know about the 3D printed head model's details. What was the cumulative costs/print time? 9. Overall, it is interesting to know that various surgeons are trying to integrate and implement 3D printing and digital technologies in their clinical workflow. However, this workflow could have been simplified in several ways. A superimposition of CT and optical face scan data could have mitigated the additional need to 3D print a head model, manual adaptation of foam lining, resulting in the decreased overall planning, consumables, and printing time. Therefore, the authors can address these limitations and improve the discussion section. 10. The authors state that the 3D printed helmet was light weight in nature. It would be interesting to know the overall weight of the 3D printed helmet in comparison to the conventional helmet. 11. Figure 6 should be improved. The text in the images is too minute to interpret. Maybe the authors can simplify the image. 12. References list can be improved. 13. Kindly update the abbreviations list. E.g., CAD, ISO, USP, and ABS are missing nomenclatures.
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This preprint is under consideration at 3D Printing in Medicine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Case report
Patient-specific 3D-printed Helmet for Post-craniectomy Defect – A Case Report
Evan Fang
University of Hong Kong Faculty of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0034-0753
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 18 Dec, 2020
Community comments: 1
Review #1 received
Received 22 Jan, 2021
Reviewer #2 agreed
On 30 Dec, 2020
Reviewers invited
Invitations sent on 30 Dec, 2020
Reviewer #1 agreed
On 30 Dec, 2020
Editor assigned
On 14 Dec, 2020
Submission checks complete
On 14 Dec, 2020
Editor invited
On 14 Dec, 2020
First submitted
On 09 Dec, 2020
Metrics
Comments: 1
PDF Downloads: ...
HTML Views: ...
Subject Areas
Surgery
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Patients who undergo decompressive craniectomy (DC) are often fitted with a protective helmet that protects the craniectomy site from injury during rehabilitation. However, conventional “one-size-fits-all” helmets may not be feasible for certain craniectomy defects. We describe the production and use of a custom 3D-printed helmet for a DC patient where a conventional helmet was not feasible due to the craniectomy defect configuration.
Case presentation: A 65-year-old male with ethmoid sinonasal carcinoma underwent cranionasal resection and DC with free vastus lateralis flap reconstruction to treat cerebrospinal fluid leakage. He required an external helmet to protect the craniectomy site, however, the rim of a conventional helmet compressed the craniectomy site, and the straps compressed the vascular pedicle of the muscle flap. Computed topography (CT) scans of the patient’s cranium were imported into 3D modelling software and used to fabricate a patient-specific, strapless helmet using fused deposition modelling (FDM). The final helmet fit the patient perfectly and circumvented the compression issues, while also providing better cosmesis than the conventional helmet. Four months postoperatively, the helmet remains intact and in use.
Conclusions: 3D printing can be used to produce low-volume, patient-specific external devices for rehabilitation where standardized adjuncts not optimal. Once initial start-up costs and training are overcome, these devices can be produced by surgeons themselves to meet a wide range of clinical needs.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Neha Sharma
ORCiDcommented on 28 January, 2021
The article deals with an interesting topic in which the authors describe their digital workflow to fabricate a 3D printed customized helmet in a patient with a post-craniectomy defect. I think the work can benefit substantially if the following issues/uncertainties in describing the workflow are addressed. 1. The authors created a 3D skin surface rendering of the patient’s head based on CT datasets, and 3D printed a head model. This printed head was then optically scanned to create a digital model, which was later used for the helmet modeling. Why wasn’t the optical scanner used for digitizing the patient to get a face scan model? Several authors have evaluated and suggested that the optical datasets have higher accuracy for face scans. Besides, the optical face scan can be superimposed and merged onto the CT datasets and could have been utilized for the helmet 3D modeling. In the end, only a helmet could have been 3D printed to support the digital workflow with minimal manual work. This could have saved the additional printing time/cost needed to print a head model. Bohner L, Gamba DD, Hanisch M, Marcio BS, Tortamano Neto P, Laganá DC, Sesma N. Accuracy of digital technologies for the scanning of facial, skeletal, and intraoral tissues: A systematic review. J Prosthet Dent. 2019, 121, 246-251. doi:10.1016/j.prosdent. Sharma N, Welker D, Cao S, von Netzer B, Honigmann P, Thieringer F. (2021) An Interactive, Fully Digital Design Workflow for a Custom 3D Printed Facial Protection Orthosis (Face Mask). In: Meboldt M., Klahn C. (eds) Industrializing Additive Manufacturing. AMPA 2020. Springer, Cham. doi:10.1007/978-3-030-54334-1_3. Sharma A, Sasaki D, Rickey DW, Leylek A, Harris C, Johnson K, Alpuche Aviles JE, McCurdy B, Egtberts A, Koul R, Dubey A. Low-cost optical scanner and 3-dimensional printing technology to create lead shielding for radiation therapy of facial skin cancer: First clinical case series. Adv Radiat Oncol. 2018 Feb 14;3(3):288-296. doi:10.1016/j.adro.2018.02.003. Briggs M, Clements H, Wynne N, Rennie A, Kellett D. 3D printed facial laser scans for the production of localised radiotherapy treatment masks - A case study. J Vis Commun Med. 2016 Aug-Oct;39(3-4):99-104. doi: 10.1080/17453054.2016.1246061. 2. What was the rationale behind the manual adaptation of a 6 mm layer of plastozote on the 3D printed head model? This aspect (manual labor and incurred consumable cost) could have been mitigated by modeling an offset of 6 mm on the digital 3D head model, similar to the digitally created 2 mm gap to accommodate the patient’s hair, as illustrated in Figure 4. 3. It is somehow unclear if the 3D head model was printed, including the hair's offset. If not, how was it compensated and corroborates with the authors’ digital workflow? 4. On lines 89-90, the authors state “a 512 x 512 region of interest (ROI) resolution” for CT DICOM parameters. Kindly rephrase ROI to matrix. 5. On line 101, the author state, “The 3D head model was printed for helmet design and liner fabrication.” What were the printing parameters/material/technology used for the head model? Kindly specify. 6. On lines 115-116, the authors state, “The helmet was lined with the foam insert and tested for fitting on the 3D head model”. It is clear that a foam lining needs to be inserted on the helmet's inner aspect. But how did the authors assessed the fit on the 3D printed head model? Wasn’t the 3D printed head model already adapted and glued with a 6 mm foam lining. Kindly rephrase or elaborate. 7. Were any post-processing procedures done for the 3D printed helmet? 8. On lines 152-154, the authors state, “Nonetheless, with training and initial setup costs overcome, the material cost of our helmet was 58 euros, design time was approximately 3 hours, and machine time was 11 hours”. This workflow is based on first printing a 3D head model. Therefore, it would be beneficial for the readers to know about the 3D printed head model's details. What was the cumulative costs/print time? 9. Overall, it is interesting to know that various surgeons are trying to integrate and implement 3D printing and digital technologies in their clinical workflow. However, this workflow could have been simplified in several ways. A superimposition of CT and optical face scan data could have mitigated the additional need to 3D print a head model, manual adaptation of foam lining, resulting in the decreased overall planning, consumables, and printing time. Therefore, the authors can address these limitations and improve the discussion section. 10. The authors state that the 3D printed helmet was light weight in nature. It would be interesting to know the overall weight of the 3D printed helmet in comparison to the conventional helmet. 11. Figure 6 should be improved. The text in the images is too minute to interpret. Maybe the authors can simplify the image. 12. References list can be improved. 13. Kindly update the abbreviations list. E.g., CAD, ISO, USP, and ABS are missing nomenclatures.