Noninvasive 3D-head-scan used for 3D-printed customized helm by patient undergoing decompressive hemicraniectomy
Year: 2025
Type: article
Study design: Case Report
Abstract: Introduction: Decompressive hemicraniectomy (DC) is a procedure used to treat elevated, therapy-refractory intracranial pressure. Despite the severity of the underlying pathology, selected patients quickly regain mobility and are at risk of secondary injury due to the post-craniotomy defect. A 3D-printed helmet offers a quickly available and safe solution. Up to now, postoperative CT scans have been used as a template for helmet construction. In this study, we present an alternative helmet construction using a non-invasive 3D scan (ArtecLeo, Artec3D), which is used to capture craniometrics data, plan the shape of the helmet, and compare it with routinely performed CT scans. A significant difference in defect displacement between supine scans and standing or sitting scans is evident, which is quantified. Methods: We included six patients who underwent decompressive craniectomy due to therapyrefractory elevation of intracranial pressure as a consequence of following pathologies: large intracerebral hemorrhage, large cerebral infarction, sever traumatic brain injury and poor grade subarachnoid hemorrhage. All patients underwent 3-D scan and subsequently, a helmet was created to cover the craniectomy area. Results: A surface heat-map comparison was performed to demonstrate the differences between the data obtained by 3-D scan (lying and sitting position) and CT-scan. Furthermore, the heat-map demonstrates the frontal and posterior surface difference between CT-scan and sitting position. Comparing the lying position 3-D scan and CT-scan, we were able to demonstrate a tissue shift, mainly in cranial and frontal areas. Conclusion: We demonstrated that non-invasive 3D-scan (Artec3D) is a feasible alternative to scan the head of the patients after DHC in order to construct a 3D-printed Helm. According to the heat-map analysis, it seems to be more reliable data assessment method in independently moving patients.
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4409373283