Advanced 3D Guidance for EVAR Procedures. Gilles Soulez, MD, MSc University of Montreal

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1 Advanced 3D Guidance for EVAR Procedures Gilles Soulez, MD, MSc University of Montreal

2 Disclosure & Acknowledgments Research Grants Canadian Health Research Institute Canadian Fundation of Innovation Natural Sciences and Engineering Research Council Fonds Recherche Québec-Santé Siemens Medical Cook Medical CAE Patent licensing (Cook Medical)

3 Current limitation of CT planning and 2D DSA guidance for EVAR Procedure planning performed on CTA DSA guidance Radiation exposure and CIN Need to recreate ideal projection for each procedural step No visualization of thrombus to delineate landing zones Increasing incidence of complex EVAR Hostile neck Branched/Fenestrated Chimney

4 Renal function & EVAR GFR ml/min Stage 1 >90 Stage Stage Stage Stage 5 < 15 Brown LC. An Surg 2010;

5 Irradiation EVAR & irradiation 1 Planning CT EVAR + CT FU at 1, 3, 6 and 12 months and annually msv on 5 years 70-year-old = attributable cancer risk = 0.60% (1 in 170). 50-year-old = 1.03% (1 in 100) 30% of CT scan unnecessary 2 Stochastic injuries due to staff exposition 3 1.White HA. J Cardiovasc Surg : Zhou W. J Vasc Surg Stecker MS-JVIR-2009

6 Improve guidance 3Droadmap Alignment 3D and 2D projection Real time fluoroscopy with2d/3d overlay Synchronization of 3D rendering with C- arm angulation

7 Per-operative 2D/3D Workflow

8 Limitation of overlaying CT volume rendering and fluoro Thrombus outline is not seen Superposition of VR will mask guidewires, delivery device and stent markers Only a rigid registration can be performed since you do not have a mathematical geometric model (Mesh)

9 How to improve volume overlay Fading in and out Stent boost Use vessel transparent dysplay Complete segmentation of AAA lumen and thrombus outlines Create a lumen and thrombus mesh Dysplay only the mesh Dijkstra-ML-J-Vasc-Surg-2011 Tacher-V-JVIR-2013

10 AAA segmentation and modeling Longitudinal view of the AAA Outerwall segmentation from the celiac trunk to one selected iliac artery Segmentation plans defined on transverse section of the AAA 3D rendering of the AAA lumen and thrombus Automated D-Max and volume measurements Kauffmann C et al. EJR 2011;77:502-8

11 Automatic CT segmentation for lumen and thrombus

12 Planning Define centerlines Aorta, iliac, renal arteries CT, SMA Internal iliac arteries Propose and define the best working view for each procedural steps Orthogonal projection to target vessel centerline Adjust if needed for C-arm mechanical range

13 3D/3D Registration Perform a CBCT Before draping Automatic bone registration Spine/spine Fine tuning of registration based on vascular Calcification alignement Vessel ostia Projection of 3D CT mesh in the fluoroscopic spac Synchronization of C-arm, table, magnification with the new 3D/3D space

14 Registration with Biplanar Fluoroscopy Separate VR of spine and aortic lumen from CT Spine fusion /biplanar fluoroscopy Replacement of spine by aortic lumen Advantage Dose Easy Drawback Absence of precise vascular alignement

15 Rigid registration

16 Live DSA correction

17 Live DSA correction

18 Registration accuracy 3D/3D registration is generated at the onset of the procedure (before patient draping) Need to minimize patient motion Influenced by anesthesia Arm position Registration renal arteries Error X axis Error z axis Before correction mm 10.6±11 7.4±5.3 Bone based correction mm 3.5± ±3.7 DSA correction mm 0.6± ±0.4 Kaufmann et al. JVIR 2015

19 Optimization of overlay for bone correction Substraction of bone marrow to outline bone contours Manual bone based correction of the 3D/3D alignement during fluoroscopy Manual alignement optimization based on DSA Mean error on renal 1.95 ±2.46 mm (0-7 mm) Fukuda-T et al. Europ-J Vascular-Endovasc-Surg-2013

20 Iliac artery Iliac centerline deviation 38.3±15.6mm Ostia internal iliac artery Ipsi lateral 5.6±2mm (x axis) 4.3±3mm (z axis) Contralateral 6.1±3mm (x axis) 5.5±4.2 (z axis) Kaufmann et al. JVIR 2015

21 Vascular displacement is a concern Deformation induced by endovascular devices

22 Elastic registration with automated detection of endovascular devices Device segmentation Lessard S et al. Med Eng Phys 2015

23 Preliminary results Errors at the internal iliac ostia estimation after a mesh deformation, measured offline 28 patients (21 EVAR and 7 FEVAR) 43 measures Before correction mean error 11.3±7mm After correction 1.99±2.03 mm 19 no error Principal suspected causes of error External iliac high curvature High calcification score Lessard S et al. Manuscript in preparation

24 Deformation influenced by calcification and curvature

25 Ideal Workflow for minimizing misregistration AAA mesh creation > MIP or VR overlay CBCT > biplanar? Better alignment of vascular structure on CBCT (calcification) Continuous and automated process of bone correction to correct bone displacement Apply correction on CBCT/CT fusion model Update of vascular structure Guidewire (geometric centerline alignement) After each DSA Mechanical modeling (FEA)

26 Clinical utility for standard EVAR 72 pts database /matching based on BMI 16 vessel navigator 16 conventional DSA Stangenberg-L-J-Vasc-Surg-2015

27 2D/3D interesting for complex EVAR Increasing proportion of EVAR in hostile neck anatomies Fenestrated/branched SG/chimney procedures Helpful to align fenestration and catheterize side vessels

28 Fenestrated/branched EVAR

29 Clinical utility Tacher-V et al JVIR 2013

30 Clinical utility: fenestrated, branched SG 72 patients FEVAR 41 no CT fusion 31 after CT fusion Less Fluoro, procedure time Radiation, Contrast dose Less blood loss (p<0.0001) Trend for lower hospital stay (p=0.07) Mc Nally et al. J Vasc Surg 2015

31 102 pts intra-operative fusion Standard (44)/Fenestrated (18)/Branched (26)/Thoracic (14) Hartault-A-Eur-J-Vasc-Endovasc-Surg-2015

32 Comprehensive biomechanical modeling of EVAR Developing a comprehensive patient-specific biomechanical model of the aorto-iliac vascular structure and surrounding organs Intra-luminal thrombus Abdominal fat Spine Simulating EVAR to perform advanced procedural planning Assessing arterial deformations during EVAR Improving 2D/3D roadmap guidance (elastic registration using the simulation outputs Performing procedural rehearsal for physicians training

33 EVAR simulation Use the mesh to create a biomechanical model Aorta Stent-graft Use Finite element analysis to estimate the interaction between SG and aorta Simulate SG deploiement to optimize SG planning Simulate vessel deformation to optimize image guidance Procedure reharsal for training Roy D et al. IMA Journal of Applied Mathematics 2014;79:

34 Simulation: aorta Done in 10 min. from segmentation files. Displacement = 80mm Finite element mesh Points defining the spine curve Lumen only Roy D et al. Numerical analysis 2014

35 Overall Workflow Start Patients AAA, Thrombus, Spine, Pelvic Bone and Fat Reconstruction Simulation Plug in for translating patient data to LSDYNA Zero Pressure Geometry Select stent-graft Deployment Simulation Deformation and configuration Roadmap Translating simulation result to prioperative software Tools detection Tools identification 3D roadmap Proper simulation selection

36 Patient geometry reconstruction ORS software Numerical Model Lumen Thrombus Spine Patient from LCTI database

37 Device Characterization (Cook Medical) 1 Stent (Steel/nitinol) 3 2 Graft (Dacron) Equivalent mean properties for typical sections of body and leg catheters E axial E circ. = 402 MPa = 165 MPa

38 Abdominal fat characterization The hyper-viscoelastic material is considered for the surrounding organ The experimental data on human adipose tissue is taken from Gerhard Sommer et al. Stress strain data

39 Vessel Wall characterization AAA exhibits significant anisotropic behavior Hyperelastic Matrix Collagen fibers reinforcement Layered structure of AAA Matrix Fibers Stress strain data I=Intima M=Media A=Adventitia 39

40 Results: Arterial deformation by guidewires Dugas A et al. CVIR 2012;35:779-87

41 Results: Stent-Graft Deployment (1) (2) (3) (4) (5) (6) 41

42 Results: Stent-Graft Deployment

43 Multimodal image fusion and simulation for EVAR 3D roadmap will minimize procedure time, contrast, irradiation and complication in complex EVAR procedures Improved visualization and correction if overlay done after AAA segmentation and mesh creation Automated device detection useful to perform geometric correction during the procedure Biomechanical simulation of AAA, SG and their mutual interaction will play a major role in the future