Clinical, Anatomical, and Demographic features associated with the use of Patient-Specific Advanced Visualization Modeling in Cerebrovascular Disease
Objective:
Characterizing clinical attributes of three-dimensional (3D) advanced visualization (AV) modeling in complex cerebrovascular diseases (CVD).
Background:
Visualizing clinical imaging is traditionally accomplished via two-dimensional planar imaging. The growth of 3D-rendering technologies has created novel opportunities for advanced visualization (AV) in CVD, specifically cerebral aneurysms (CA) and arteriovenous malformation (AVMs). The limitations of 2D imaging in CVD may be mitigated with patient-specific AV models which can be seen using 3D Viewer software (3DV), Mixed Reality (XR), and/or 3D Printing (3DP). There remains a gap in knowledge, however, in understanding how these tools are being used in complex CVD.
Design/Methods:
The AV anatomical models are based on Digital Imaging and Communications in Medicine (DICOM) datasets procured via routine clinical imaging. DICOM datasets are segmented using computer-aided design (CAD) software into 3D files. Clinical, anatomical, and demographic features of CVD patients were analyzed. Descriptive statistics were used to characterize clinical attributes associated with AV models used in CVD.
Results:
Seven neurovascular physicians over 24 months requested 61 CVD cases. CA models accounted for 81.9% (n= 50) and AVMs accounted for 18.1% (n= 11) of AV modeling requests. CA data indicated that 80% of patients who received AV models were female (n= 40). Among the AV CA cases, 94% used rXA (rotational angiography), while the remaining used were CTA (4%) and MRI (2%). 3DP was used in 70% of CA cases (n = 35) to create physical models.
Conclusions:
Preliminarily, AV models are used in both CA and AVM and most frequently based on rXA imaging. CAs are commonly viewed using 3DV as well as 3DP. Future directions will include assessing the utility of AV models in CVD across training levels. Additionally, we will collect and compare clinical outcomes data to assess the efficacy of AV in complex CVD.