Image Fusion During Endovascular Aneurysm Repair, how to Fuse? An Overview of Registration and Implementation Strategies Plus Tips and Tricks

Introduction: The use of image fusion in the hybrid operating room is increasingly used. Image fusion enables physicians to deploy endovascular devices with a 3D roadmap of the vascular anatomy based on preoperative CTA or MRA. Previous studies described a decrease in nephrotoxic contrast volume, fluoroscopy time, radiation dose and procedure time in complex endovascular aortic repair (EVAR).1-4 However, there is no general consensus or guideline on the optimal technique and timing of image fusion. There are several options; 2D-3D bony landmark registration, 3D-3D aortic calcification registration, contrast enhanced cone beam CT (ceCBCT) registration. Moreover, registration can be done before or after the insertion of sheaths and (stiff) guidewires. The goal of this study was to determine image fusion accuracy. Methods: Several fusion strategies were analyzed. Strategy 1: 2D-3D registration before insertion of sheaths and guidewires. Strategy 2: 3D-3D registration before insertion of sheaths and guidewires. Strategy 3: 2D-3D registration after insertion of sheaths and guidewires. Strategy 4: 3D-3D registration after insertion of sheaths and guidewires. An overview is displayed in Figure 1A-1D. Strategy 1 was evaluated with clinical patient data. Strategies 2, 3 and 4 were evaluated with an infrarenal AAA phantom model with pelvis, vertebral column and renal calcifications as displayed in Figure 1E. For strategy 1, in total 11 EVAR patients (median age 75.5, all male) were included of which 4 were complex EVAR (fenestrated) and 7 standard EVAR. In all patients, digital subtraction angiography (DSA) was used as roadmap to deploy the devices. After DSA, manual correction was performed to correct fusion overlay to match the lowest renal artery between image fusion and DSA. Registration accuracy was determined by ostium displacement (in millimeters) of the lowest renal artery (proximal accuracy) and ostium displacement of the right and left internal iliac arteries (distal accuracy), when compared to the intra-operative DSA images (see Figure 1A & 1F). Proximal accuracy was measured before and after DSA correction. Displacement accuracy was defined as follows; accurate (0-1 mm), medium (1-4 mm) and poor (>4 mm). Tips are to register with vertebral L1/L2 centered and to correct navigation markers in axial CT-view to prevent misplacement due to ostia calcification, as displayed in Figure 1 G-H. Results: For the 11 patients the mean proximal accuracy was 0.7 (0.4-0.9) mm and distal accuracy was 5.8 (1.3-12.3) mm compared to the DSA. Before DSA correction proximal accuracy was 7.4 (1.4-11) mm. With phantom data, proximal accuracy was 0.8 (0.5-1.1) mm and distal accuracy was 2.3 (0.6-1.2) mm for strategy 2. Strategy 3 resulted in a proximal accuracy of 2.6 (1.9-3.4) mm and distal accuracy of 14.0 (13.0-15.0) mm. Strategy 4 resulted in a proximal accuracy of 1.6 (1.5-1.8) mm and distal accuracy of 4.0 (2.0- 5.9) mm. See Table 1 for an overview. Conclusion: Based on this data, image fusion proximal accuracy is equal with 2D-3D and 3D-3D registration before sheath and guidewire insertion. Manual DSA correction for 2D-3D registration is required to improve accuracy. After the insertion of guidewires, the accuracy of 3D-3D registration is superior to 2D-3D registration

Deep Learning–Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair

Purpose: Modern endovascular hybrid operating rooms generate large amounts of medical images during a procedure, which are currently mostly assessed by eye. In this paper, we present fully automatic segmentation of the stent graft on the completion digital subtraction angiography during endovascular aneurysm repair, utilizing a deep learning network. Technique: Completion digital subtraction angiographies (cDSAs) of 47 patients treated for an infrarenal aortic aneurysm using EVAR were collected retrospectively. A two-dimensional convolutional neural network (CNN) with a U-Net architecture was trained for segmentation of the stent graft from the completion angiographies. The cross-validation resulted in an average Dice similarity score of 0.957 ± 0.041 and median of 0.968 (IQR: 0.950 – 0.976). The mean and median of the average surface distance are 1.266 ± 1.506 mm and 0.870 mm (IQR: 0.490 – 1.430), respectively. Conclusion: We developed a fully automatic stent graft segmentation method based on the completion digital subtraction angiography during EVAR, utilizing a deep learning network. This can provide the platform for the development of intraoperative analytical applications in the endovascular hybrid operating room such as stent graft deployment accuracy, endoleak visualization, and image fusion correction

Retrospective Cohort Study on Vessel Deformation During Fenestrated or Branched Endovascular Aortic Repair; Traditional CTA Roadmaps Provide Insufficient and Inadequate Guidance During Target Vessel Cannulation

Introduction: Optimal visualization of target vessels in three-dimensions (3D) plays a key role in complex endovascular procedures, such as fenestrated and branched endovascular aortic repair (EVAR). A popular imaging technique to enhance target vessel visualization is image fusion. Image fusion combines real-time fluoroscopy with static preprocedural anatomical images, typically computed tomography angiography (CTA) to create an arterial roadmap. However, the introduction of stiff endovascular devices cause the arteries to stretch, leading to a mismatch between the actual position of the (origin of the) artery and its representation on the image fusion roadmap. This retrospective study assesses vessel deformation of the aorta and its side branches due to the introduction of a stiff endovascular devices, during fenestrated and branched EVAR. Furthermore, the influence of vascular tortuosity on the extent of vessel deformation was analysed. Methods: Patients that underwent fenestrated or branched EVAR between January 2015 and January 2018 were retrospectively included in this study. Two imaging datasets were collected from each patient: 1) the preoperative CTA and 2) the intraoperative contrast-enhanced cone beam computed tomography (ce-CBCT), acquired after the insertion of the stiff guidewire and stent delivery device (Zenith custom made, Cook, Bloomington IN, USA) . Manual registration of both datasets was performed, using the bony landmarks of the vertebrae. Subsequently, the ostium of the celiac artery (CA), superior mesenteric artery (SMA), left renal artery (LRA) and right renal artery (RRA) were marked in both the CTA and ce-CBCTreconstructions.The ostium displacement of the four target vessels was reported as a 3D vector as well as a 2D vector in the coronal plane (RRA and LRA) or sagittal plane (CA and SMA). The tortuosity index of the iliac and the abdominal aortic segment were calculated. The effect of the tortuosity index on the extent of vessel deformation was assessed using linear regression. Results: In total 77 target vessels from 20 patients were included in this study. The 3D mean displacement vector of the ostium of the CA, SMA, RRA and LRA were respectively 8.73.8mm, 7.42.7mm, 7.92.7mm and 7.64.4mm. The 2D mean displacement vector for the SMA and CA in the sagittal viewing plane was 4.92.9mm and 6.53.0mm respectively. The 2D mean displacement vector of the RRA and LRA in the coronal viewing plane was 7.02.8mm 6.24.3mm respectively. An example of the 2D displacement of the RRA in the coronal plane is shown in Figure 1. In total, 74% of the target vessels had a 2D vector displacement of more than 50% of the diameter of the vessel. The mean tortuosity index of the abdominal aortic segment and the iliac segment was 1.10 and 1.34 respectively. Linear regression showed no association between the extend of vessel displacement and the tortuosity index of the abdominal aortic segment (p¼0.37), nor the iliac segment (p¼0.11). Conclusion: There is significant vessel displacement of the ostium of the target vessels, during fenestrated and branched EVARs caused by the introduction of stiff endovascular devices. Consequently, preoperative CTA roadmaps are inadequate to guide target vessel cannulation during fenestrated or branched EVAR

Target vessel displacement during fenestrated and branched endovascular aortic repair and its implications for the role of traditional computed tomography angiography roadmaps

Background: This retrospective study quantifies target vessel displacement during fenestrated and branched endovascular aneurysm repair due to the introduction of stiff guidewires and stent graft delivery systems. The effect that intraoperative vessel displacement has on the usability of computed tomography angiography (CTA) roadmaps is also addressed. Methods: Patients that underwent fenestrated or branched EVAR were included in this retrospective study. Two imaging datasets were collected from each patient: (I) preoperative CTA and (II) intraoperative contrastenhanced cone beam computed tomography (ceCBCT) acquired after the insertion of the stiff guidewire and stent graft delivery system. After image registration, the 3D coordinates of the ostium of the celiac artery, superior mesenteric artery, right renal artery and left renal artery were recorded in both the CTA and the ceCBCT dataset by two observers. The three-dimensional displacement of the ostia of the target vessels was calculated by subtracting the coordinates of CTA and ceCBCT from one another. Additionally, the tortuosity index and the maximum angulation of the aorta were calculated. Results: In total 20 patients and 77 target vessels were included in this study. The ostium of the celiac, superior mesenteric, right renal and left renal artery underwent non-uniform three-dimensional displacement with mean absolute displacement of 8.2, 7.7, 8.2 and 6.2 mm, respectively. The average displacement of all different target vessels together was 7.8 mm. A moderate correlation between vessel displacement and the maximum angulation of the aortoiliac segment was found (Spearman's ρ=0.45, P<0.05). Conclusions: The introduction of stiff endovascular devices during fenestrated or branched EVAR causes significant, non-uniform displacement of the ostium of the visceral and renal target vessels. Consequently, preoperative CTA roadmaps based on bone registration are suboptimal to guide target vessel catheterization during these procedures