Titolo presentazione. sottotitolo

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1 Optimal positioning of a tubular continuum robot Titolo presentazione for resection of suprasellar brain tumors in sottotitolo consideration of task constraints Author: Natalia Boffa Milano, XX mese 20XX Advisor: Prof. Dr.- Ing. Elena De Momi Co- advisors: Prof. Dr.- Ing. Jessica Burgner- Kahrs, M.Sc. Carolin Fellmann

2 Clinical Problem: Pituitary Adenoma Surgical approaches Pituitary gland Transcranial Invasive Risk of infection Disfigurement Long recovery Sphenoid sinus Endonasal Minimally invasive Reduced complications Short recovery [Burgner et al., IEEE/RSJ, 2011] (2)

3 Tubular Continuum Robot concentric precurved, superelastic tubes 2 Smallest continuum robot (diameter < 2.5mm) α $ β $ β % α % α # β # 3 2 DOF per tube: Rotation α 4 Translation β Teleoperable [Burgner et al., IEEE/RSJ, 2011] (3)

4 Video (4)

5 Motivation Resort to invasive surgery Wide tumors Pituitary gland Advantages of robot assisted surgery Better reachability Tumors located deep into the brain Augmented manipulability Less fatigue for the surgeon + Avoid invasive surgery [Burgner et al., IEEE/RSJ, 2011] (5)

6 Goal: Maximization of Target Coverage Robot design optimization Robot base placement optimization (t, R) Target i = 1,2,3 L ' ( k ' L ' ) CS /01230 CS design parameters per tube (9 in total): ( Length of the straight section L ' ) Length of the curved section L ' Curvature parameter k ' Robot base placement with respect to reference frame: Position t Orientation R (6)

7 State of the Art Robot design optimization Collision-free pathways [Torres et al., IROS, 2012] Reachability of target points [Burgner et al., ICRA, 2013] 2 separate problems: navigation and manipulation [Bergeles et al.,t-ro, 2015] Initial design Optimal design [Anor et al., IEEE, 2011] Robot base placement optimization: actual issue (7)

8 Materials Medical imaging datasets Micro-adenoma 1 cm Macro-adenoma Carlo Besta Neurological Institute, Milan, Italy 6 subjects (2 micro-adenomas + 4 macro-adenomas) 3T MR scanner with contrast agent CT scanner 4 cm (8)

9 Methods D reconstruction of surgical workspace Workspace modelling Model discretization (1 mm 3 voxels) Tumor Sphenoid sinus Nasal passage (9)

10 Definition of Admitted Base Placements Initial base placement Tumor Sphenoid sinus 9 additional base placements Sphenoid sinus upper base center Insertion points Nasal passage 9 x (t, R) Transformed frames CS CS /01230 (10)

11 Definition of Admitted Base Placements Initial base placement 9 additional base placements Insertion points Tumor Sphenoid sinus * * * * * * * * Nasal passage Patient s nostril Transformed frames Sphenoid sinus upper base center CS CS / x (t, R) (10)

12 Robot Configuration Samples 12

13 Definition of Admitted Base Placements Initial base placement 9 additional base placements Insertion points Nasal passage Sphenoid sinus Tumor * * * * * * * * Sphenoid sinus upper base center CS Transformed frames Insertion points CS /01230 (10)

14 Set of Admitted Base Placements Initial base placement Tumor Sphenoid sinus Definition of 8 base placements (t, R) 8 insertion points at patient s nostril Direction of insertion towards sphenoid sinus upper base center CS Nasal passage R CS CS /01230 Patient s nostril Insertion point Sphenoid sinus upper base center (10)

15 Optimization Algorithm Random initial design Selection of base placement Generation of configuration samples For each sample Check of workspace boundaries New design Computation of reached tumor voxels Sequential Quadratic Programming (SQP) Stopping criteria Optimal design and base placement (11)

16 Optimization Results Before optimization After After optimization 1 Design 2 Robot placement 3 Reached tumor points (10000 samples) x/y plane x/z plane x/y plane x/z plane (12)

17 Algorithm Evaluation: Comparison with Previous Work Previous surgical workspaces 3 workspace models taken from [Burgner et al., ICRA, 2013]: A, B, C Tumor coverage comparison Improvements with respect to [Burgner et al., ICRA, 2013]: A B + Consideration of robot base placement + Optimization of all robot tube components (Micro-adenoma) C (Macro-adenoma) Comparison of median tumor coverages Workspace model Previous median coverage [%] Actual median coverage [%] A B (Macro-adenoma) C (13)

18 Towards an Application- Specific Approach Generic representations 2 workspace models using mean dimensions from literature: D, E Design shape comparison Comparison of optimized design shapes using mean distance (MD) D 7.5$mm 7.5$mm 7.5$mm E 11.5$mm 11.5$mm 11.5$mm Interchange of similar designs (MD < cm) z d $ d % 13.6$mm 13.6$mm 17.5$mm (Micro-adenoma) 17.5$mm (Macro-adenoma) MD = 0.37 cm Can these models be representative of more clinical cases? MD = Y y $T y % %T + z $T z % TZ$ %T K y The designs optimized for model D fit all other micro-adenoma cases considered (14)

19 Influence of Model Geometry on Tumor Coverage Tumor volume V t Tumor access constraint A c V 5 A A a = V 5 A [mm] Correlation test (Pearson coefficient): p-value> 0.05 Correlation test (Pearson coefficient): p-value <0.05 Linear regression model: R-squared= 73% (14)

20 Conclusions and Future Developments Conclusions Robot base placement increases target coverage Robot performance depends on the space available at the base of the tumor Future developments Increase number of tube components Multi-objective optimization Collision-free pathways for insertion (15)

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