3D-Printed Patient-Specific Models of the Aortic Arch for Advanced Visualization of Complex Neurointerventional Cases

Smruti Mahapatra1, Vishal N Bhimarasetty2, Abdul Rahim3

  • 1Department of Neurosurgery, Tulane University School of Medicine, New Orleans, LA.

Ochsner Journal
|June 20, 2025
PubMed

Insights

3D-printed patient-specific models of aortic anatomy improve visualization for neurointerventional procedures. These realistic models aid in managing complex cerebrovascular cases and may enhance stroke treatment outcomes.

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Neurosurgery

Background:

  • Cerebrovascular disease is a major global health concern, with endovascular therapies crucial for ischemic stroke treatment.
  • Complex aortic arch anatomy poses significant challenges for accessing intracranial circulation during neurointerventional procedures.
  • These anatomical complexities can negatively impact treatment efficacy and patient outcomes.

Purpose of the Study:

  • To investigate the utility of patient-specific 3D-printed models for understanding and navigating tortuous cerebrovascular anatomy.
  • To assess the accuracy and feasibility of creating 3D models from patient imaging data.

Main Methods:

  • Fabrication of 3D-printed models of the aortic arch and major branch vessels using imaging data from four patients.
  • Validation of model accuracy by comparing measured diameters to established literature values.
  • Quantification of the time and material costs associated with model creation.

Main Results:

  • Patient-specific 3D models accurately represented intricate vascular pathways, offering enhanced visualization of complex structures.
  • The physical dimensions of the 3D-printed models closely matched reported anatomical values.
  • Average model creation involved 4 hours of digital processing and 13.71 hours of 3D printing, with a material cost of ~$17.31.

Conclusions:

  • 3D-printed patient-specific models serve as valuable tools for neurointerventional training and preprocedural planning in cases of complex cerebrovascular anatomy.
  • Enhanced visualization through these models can improve clinician preparedness.
  • Utilizing these advanced visualization tools holds the potential to improve outcomes for ischemic stroke treatment.
Abstract

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