Automating fusion and dealing with tortuous anatomy. Tom Carrell Guy s and St Thomas NHS Trust King s College London London, UK

Transcription

1 Automating fusion and dealing with tortuous anatomy Tom Carrell Guy s and St Thomas NHS Trust King s College London London, UK Critical Issues Malmo 2014

2 Disclosures Intellectual property Co-founder and shareholder in Cydar Ltd

3 Terminology Image fusion = 3D overlay = 3D roadmapping Registration = matching CT to X-ray

4 Why use software for overlay? Enough information on X-ray to overlay CT accurately 2D/3D image registration* X-ray video input and image processing could work with any X-ray system: new/ old, fixed/ mobile no additional X-rays/ contrast Avoid big engineering and capital expenditure *Penney G, Weese J, Little J, Desmedt P, Hill D and Hawkes D. A comparison of similarity measures for use in 2D- 3D medical image registration. IEEE Trans. Med. Imaging. 1998; 17(4);

5 Why build a fully-automated system? No disruption to clinical workflow Update 2D/3D match ('registration') continuously during surgery reducing errors But, must be accurate highly robust fast

6 Matching CT to X-ray ( registration ) Vertebral anatomy/ aortic calcification Manual, semiautomatic, automatic

7 Errors in 3D overlay Multiple causes Misregistration ( mismatching ) Posture Patient movement Table movement (uncommanded) Respiratory/ cardiac Deformation of native anatomy Very difficult to measure relative contributions Maurel, Hertault, Gonzalez et al. Evaluation of visceral artery displacement by endograft delivery system insertion. JEVT 2014;21:339-47

8 One-step match (registration) Cone-beam CT/ biplane XR/ rotational angio Track C-arm and table Assume initial match/ registration perfect Assume patient doesn't move with respect to table

9 Posture Single step registration methods assume patient posture same on operating table as during CT scan But patient posture varies from CT scan to operation Effects of muscle relaxation in general anaesthesia Average of 3-4 rotation between adjacent vertebrae (max 8 )* *Penney G, Varnavas A, Dastur N, Carrell T. Am image-guided surgery system to aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience. Image Processing in Computer-Assisted Interventions 2011;13-24

10 Patient movement IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 32, NO. 2, FEBRUARY 2013 Fig. 15. VFM reprojection distance values shown over time shown per patient who had an epidural anaesthetic. Each dot represents an individual registration. Single step registration methods assume patient does not move during operation over time shown per patient presents an individual regisever, as discussed eary of cases. pose estimation could eration (not just once y movements of the requirement could be curacy of a machine-based 2D-3D registration system, for the majority of procedures analyzed. Our proposed method to normalize the similarity measure value requires registration to a similar CT feature. Use of IEEE Trans Med Imaging Feb;32(2): nearby CT vertebrae was natural for our vertebra-based system. Varnavas A, Carrell T, Penney G. Increasing the automation of a 2D-3D registration system.

11 Patient movement 398 IEEE TRANSACTIONS Fig. 14. VFM reprojection distance values shown over time shown per patient who underwent a general anaesthetic. Each dot represents an individual registration. Single step registration methods assume patient does not move during operation knowledgable input) should be used, however, as discussed earlier, this will affect only a small minority of cases. Varnavas It isa, important Carrell T, Penney to G. note Increasing that the automation initial pose of a 2D-3D estimation registration could system. be required many IEEE Trans times Med Imaging. during2013 anfeb;32(2): operation (not just once Fig. 15. VFM reproj who had an epidural a curacy of a mach majority of proce Our proposed

12 Automated 3D overlay Fully-automated software system for CT overlay Continuously monitors X-ray video feed and updates match and overlay Tested in 131 endovascular cases Automated registration is accurate to 1mm and 1 o in each axis (x,y,z) in centre of FOV Highly robust: 0/3000+ false positive matches Slow: match currently 40 seconds October 2014: improved computing will speed up to <5 seconds Varnavas A, Carrell T. Penney G. Fully Automated Initialisation of 2D-3D Image Registration. IEEE 10 th International Symposium on Biomedical Imaging 2013;564-7

13 Automated 3D overlay Continuous monitoring and updating of 2D-3D match effectively eliminates: movement errors posture errors (in centre of Field Of View)

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15 Sources of errors in 3D overlay Misregistration ( mismatching ) Posture Patient movement Table movement (uncommanded) Respiratory/ cardiac Deformation of native anatomy

16 Sources of errors in 3D overlay Misregistration ( mismatching ) Posture Patient movement Table movement (uncommanded) Respiratory/ cardiac Deformation of native anatomy

17 Deformation Deformation more severe with tortuous anatomy Can cause large (>10mm) movements Strategies: modify (warp) overlay or new imaging Carrell TW, Modarai B, Brown JR, Penney GP. Feasibility and limitations of an automated 2D-3D rigid image registration system for complex endovascular aortic procedures. J Endovasc Ther Aug;17(4):527-33

18 Image warping (non-rigid registration) Guyot A, Varnavas A, Carrell T, Penney G. Non-rigid 2D-3D Registration Using Anisotropic Error Ellipsoids to Account for Projection Uncertainties during Aortic Surgery. Medical Image Computing and Computer- Assisted Intervention MICCAI 2013:8151;179-86

19 Extracting 3D information using lowdose X-ray fluoroscopy Alhrishy, M, Varnavas, A, Carrell, T, King, A, Penney G. Interventional Digital Tomosynthesis from a Standard Fluoroscopy System Using 2D-3D Registration Medical Image Computing and Computer-Assisted Intervention MICCAI Vol. 8151, p

20 Thank you Automated 3D overlay