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. 2018 Feb:10578:105780N.
doi: 10.1117/12.2293630. Epub 2018 Mar 12.

A simulation platform using 3D printed neurovascular phantoms for clinical utility evaluation of new imaging technologies

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A simulation platform using 3D printed neurovascular phantoms for clinical utility evaluation of new imaging technologies

S V Setlur Nagesh et al. Proc SPIE Int Soc Opt Eng. 2018 Feb.

Abstract

Modern 3D printing technology allows rapid prototyping of vascular phantoms based on an actual human patient with a high degree of precision. Using this technology, we present a platform to accurately simulate clinical views of neuro-endovascular interventions and devices. The neuro-endovascular interventional phantom has a 3D printed cerebrovasculature model derived from a patient CT angiogram and embedded inside a human skull providing bone attenuation. Acrylic layers were placed underneath and on top of the skull, simulating entrance and exit tissue attenuation and also simulating forward scatter. The 3D model was connected to a pulsatile flow loop for simulating interventions using clinical devices such as catheters and stents. To validate the x-ray attenuation and establish clinical accuracy, the automatic exposure selection by a clinical c-arm system for the phantom was compared with that for a commercial anthropomorphic head phantom (SK-150, Phantom Labs). The percentage difference between automatic exposure selection for the neuro-intervention phantom and the SK-150 phantom was under 10%. By changing 3D printed models, various patient diseased anatomies can be simulated accurately with the necessary x-ray attenuation. Using this platform various interventional procedures were performed using new imaging technologies such as a high-resolution x-ray fluoroscope and a dose-reduced region-of-interest attenuator and differential temporally filtered display for enhanced interventional imaging. Simulated clinical views from such phantom-based procedures were used to evaluate the potential clinical performance of such new technologies.

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Figures

Figure 1
Figure 1
DSA image showing an aneurysm in the patient’s ICA region of the printed model. The image was acquired using the traditional FPD system.
Figure 2
Figure 2
Post deployment image showing PED stents used to treat the aneurysm shown in figure 1. The image is acquired using the traditional FPD system.
Figure 3
Figure 3
Post deployment image showing PED stents used to treat the aneurysm shown in figure 1. The image is acquired using the high resolution HRF-CCD system.
Figure 4
Figure 4
a) Schematic diagram of the neurointervention phantom framework. b) Human skull embedded with a 3D printed model
Figure 5
Figure 5
Post deployment image showing PED stent. The image is acquired using the HRF-CMOS system.
Figure 6
Figure 6
Post deployment image with contrast showing PED stent deployed across the MCA aneurysm . The image is acquired using the HRF-CMOS system.
Figure 7
Figure 7
Post deployment image showing PED stent. The image is acquired using the traditional FPD system.
Figure 8
Figure 8
Post deployment image with contrast showing PED stent deployed across the MCA aneurysm. The image is acquired using the traditional FPD system.
Figure 9
Figure 9
Edge image of figure 6 (HRF-CMOS) derived using Sobel Edge Detector. The arrows indicate the two positions with poor wall apposition as marked by the rater
Figure 10
Figure 10
Edge image of figure 8 (FPD) derived using Sobel Edge Detector. The arrow indicates the one position with poor wall apposition as marked by the rater
Figure 11
Figure 11
DSA showing an aneurysm in the left ICA and MCA region
Figure 12
Figure 12
A single image from the sequence obtained during treatment of the ICA aneruysm shown in figure 11, using the new dose-reduced and display-enhanced imaging technique.
Figure 13
Figure 13
Background subtracted image of figure 12.
Figure 14
Figure 14
ROI in figure 12 zoomed in using bicubic interpolation.
Figure 15
Figure 15
Background subtracted roadmap image of figure 14.

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