Building Three-Dimensional Intracranial Aneurysm Models from 3D-TOF MRA: a Validation Study

To create realistic three-dimensional (3D) vascular models from 3D time-of-flight magnetic resonance angiography (3D-TOF MRA) of an intracranial aneurysm (IA). Thirty-two IAs in 31 patients were printed using 3D-TOF MRA source images from polylactic acid (PLA) raw material. Two observers measured the maximum IA diameter at the longest width twice separately. A total mean of four measurements as well as each observer’s individual average MRA lengths were calculated. After printing, 3D-printed anatomic models (PAM) underwent computed tomography (CT) acquisition and each observer measured them using the same algorithm as applied to MRA. Inter- and intra-observer consistency for the MRA and CT measurements were analyzed using the intraclass correlation coefficient (ICC) and a Bland-Altman plot. The mean maximum aneurysm diameter obtained from four MRA evaluations was 8.49 mm, whereas it was 8.83 mm according to the CT 3D PAM measurement. The Wilcoxon test revealed slightly larger mean CT 3D PAM diameters than the MRA measurements. The Spearman’s correlation test yielded a positive correlation between MRA and CT lengths of 3D PAMs. Inter and intra-observer consistency were high in consecutive MRA and CT measurements. According to Bland-Altman analyses, the aneurysmal dimensions obtained from CT were higher for observer 1 and observer 2 (a mean of 0.32 mm and 0.35 mm, respectively) compared to the MRA measurements. CT dimensions were slightly overestimated compared to MRA measurements of the created models. We believe the discrepancy may be related to the Laplacian algorithm applied for surface smoothing and the high slice thickness selection that was used. However, ICC provided high consistency and reproducibility in our cohort. Therefore, it is technically possible to produce 3D intracranial aneurysm models from 3D-TOF MRA images.

[1]  Michael Schocke,et al.  Detection and characterization of intracranial aneurysms with MR angiography: comparison of volume-rendering and maximum-intensity-projection algorithms. , 2003, AJR. American journal of roentgenology.

[2]  P. Meyers,et al.  Complications of modern diagnostic cerebral angiography in an academic medical center. , 2009, Journal of vascular and interventional radiology : JVIR.

[3]  P. Lambin,et al.  Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation , 2014, PloS one.

[4]  W M Adams,et al.  The role of MR angiography in the pretreatment assessment of intracranial aneurysms: a comparative study. , 2000, AJNR. American journal of neuroradiology.

[5]  Catherine Oppenheim,et al.  Role of MRA in the detection of intracranial aneurysm in the acute phase of subarachnoid hemorrhage. , 2013, Journal of neuroradiology. Journal de neuroradiologie.

[6]  D. Yoon,et al.  Intraobserver and interobserver variability in CT angiography and MR angiography measurements of the size of cerebral aneurysms , 2017, Neuroradiology.

[7]  Kuni Ohtomo,et al.  3D-Printed Visceral Aneurysm Models Based on CT Data for Simulations of Endovascular Embolization: Evaluation of Size and Shape Accuracy. , 2017, AJR. American journal of roentgenology.

[8]  Hongliang Li,et al.  Quality improvement of surface triangular mesh using a modified Laplacian smoothing approach avoiding intersection , 2017, PloS one.

[9]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[10]  Liu Haifeng,et al.  Diagnostic value of 3D time-of-flight magnetic resonance angiography for detecting intracranial aneurysm: a meta-analysis , 2017, Neuroradiology.

[11]  Marco Nolden,et al.  Interactive segmentation framework of the Medical Imaging Interaction Toolkit , 2009, Comput. Methods Programs Biomed..

[12]  Guo-liang Jin,et al.  3D printing of intracranial aneurysm based on intracranial digital subtraction angiography and its clinical application , 2018, Medicine.

[13]  Michael Forsting,et al.  European Stroke Organization Guidelines for the Management of Intracranial Aneurysms and Subarachnoid Haemorrhage , 2013, Cerebrovascular Diseases.

[14]  I. Torres,et al.  A simulator for training in endovascular aneurysm repair: The use of three dimensional printers. , 2017, European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery.

[15]  Yuanli Zhao,et al.  Three-dimensional intracranial middle cerebral artery aneurysm models for aneurysm surgery and training , 2018, Journal of Clinical Neuroscience.

[16]  Shigeo Sora,et al.  SIMULATION OF AND TRAINING FOR CEREBRAL ANEURYSM CLIPPING WITH 3‐DIMENSIONAL MODELS , 2009, Neurosurgery.

[17]  C. Ogilvy,et al.  RESULTS OF A PROSPECTIVE PROTOCOL OF COMPUTED TOMOGRAPHIC ANGIOGRAPHY IN PLACE OF CATHETER ANGIOGRAPHY AS THE ONLY DIAGNOSTIC AND PRETREATMENT PLANNING STUDY FOR CEREBRAL ANEURYSMS BY A COMBINED NEUROVASCULAR TEAM , 2004, Neurosurgery.

[18]  Percy Nohama,et al.  Additive Manufacturing of 3D Biomodels as Adjuvant in Intracranial Aneurysm Clipping , 2019, Artificial organs.

[19]  Rong Wang,et al.  Comparison of Two Three-Dimensional Printed Models of Complex Intracranial Aneurysms for Surgical Simulation. , 2017, World neurosurgery.

[20]  B. Tomancok,et al.  Cerebrovascular stereolithographic biomodeling for aneurysm surgery. Technical note. , 2004, Journal of neurosurgery.

[21]  N. Kaneko,et al.  Training in Cerebral Aneurysm Clipping Using Self-Made 3-Dimensional Models. , 2017, Journal of surgical education.

[22]  Dimitrios Mitsouras,et al.  Measuring and Establishing the Accuracy and Reproducibility of 3D Printed Medical Models. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[23]  P. Nelemans,et al.  Diagnosing Intracranial Aneurysms With MR Angiography: Systematic Review and Meta-Analysis , 2014, Stroke.

[24]  Michael Lehner,et al.  Cerebrovascular Biomodeling for Aneurysm Surgery , 2011, Surgical innovation.

[25]  Wilfried Philips,et al.  MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.