Balzer, Jeffrey

DE MOMI, Elena

Fellows Mayle, Wendy

Moccia, Sara

Perin, Alessandro

Prudente, Francesca

Riviere, Cameron

S. Mattos, Leonardo

Sekula, Raymond

MOCCIA, SARA

DE MOMI, ELENA

Archivio istituzionale della ricerca - Politecnico di Milano

Toward safety enhancement in neurosurgery using a handheld roboticinstrument F. Prudente1 , S. Moccia1,2, A. Perin3, R. Sekula4, L.S. Mattos2, J. Balzer4, W. Fellows-Mayle4, E. De Momi1, C. Riviere51Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy2Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy3Besta NeuroSim Center, IRCCS Istituto Neurologico C. Besta, Milan, Italy4Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA5Robotics Institute, Carnegie Mellon University, Pittsburgh, USAfrancesca.prudente@mail.polimi.it, sara.moccia@polimi.itINTRODUCTIONImprovements  in  microsurgical  dissection  techniques,skull  base  approaches,  imaging  modalities,  andintraoperative neurophysiologic monitoring have led toa reduction in surgical morbidity and mortality for deep-seated  brain  tumors  and  cerebrovascular  lesions.Despite  these  improvements,  however,  postoperativerates  of  cerebral  infarction  and  cranial  nerve  deficitsremain high and dramatically reduce quality of life inpatients with a variety of brain disorders [1], [2]. In thecase  of  petroclival  meningioma,  for  example,  theincidence of permanent cranial nerve deficits is between20% to 76% while gross total resection is achieved inonly 20% to 85% of patients [3]. An assist device that would allow the surgeon to reducelarger unintended movements of the human hand wouldresult in reduced morbidity for patients.MATERIALS AND METHODSThis  work  consists  of  three  main  steps:  (i)  vesselsegmentation,  (ii)  3D  reconstruction  and  (iii)implementation of the forbidden-region VF framework.Segmentation AlgorithmA  Geometrical  Deformable  Model  (GDM)-basedapproach  is  used  to  perform vessel  segmentation  [4].The  vessel  segmentation  algorithm  requires  asinitialization the manual selection of a starting point  p1and an  ending point  p2,  which are  then  automaticallyconnected  with  a  minimal-cost  path  algorithm.  Theresulting  path  is  used  as  initialization  of  the  GDMalgorithm. The deformation of the GDM is guided byintensity-based external forces, which are retrieved fromthe vesselness measures defined in [5]. 3D Reconstruction AlgorithmThe 3D reconstruction is performed in order to extractthe 3D position of the segmented vessel surface in theMicron  control  reference  system.  Micron  is  a  6-DOFhandheld  instrument  equipped  with  piezoelectricactuators so that its tip can move semi-independently ofthe hand motion (range of motion: Ø4.0 × 4.0 mm) [6].The Micron system setup (Fig. 1) includes (i) an optical tracker  (Apparatus  to  Sense  Accuracy  of  Position(ASAP))  for  tracking Micron  tip  position,  and  (ii)  astereo operating microscope (Zeiss OPMI, Carl  Zeiss,AG, Germany) equipped with two CCD cameras (f=30Hz, 800x600 pixels). Since  the  controller  of  Micron  works  in  theASAP reference system while the target is detected bythe stereo-cameras, calibration mappings are mandatory.A laser probe integrated into Micron is used to scan thesurface, since the camera calibration must match a set of3D tip positions measured by the ASAP with a set of 2Dlocations detected in the camera images [7]. As for the3D  reconstruction,  the  Speeded  Up  Robust  Features(SURF) detection algorithm is used [8]. The  point  cloud  relative  to  the  vessel  position  isretrieved  by  applying  the  sum-of-squared-distance-raytracing method to corresponding point pairs. The vesselsurface  is  reconstructed  exploiting  the  convex  hull,  atriangular  mesh  that  encompasses  the  retrieved  pointcloud. Forbidden-Region Virtual Fixtures The  forbidden-region  VF  implements  the  vesselcollision avoidance task, preventing the manipulator toenter into a predefined avoidance zone, representing thesegmented  vessel  and  a  cylindrical  safety  zonesurrounding it.  The  control  command  is  triggered  as  soon  as  thedistance  d between the tool tip and the vessel surfacebecomes lower than the cylindrical radius. Each time anew  tip  position  sample  is  detected  by  ASAP,  thealgorithm  finds  the  closest  point  ps  belonging  to  thevessel surface and computes d. The Micron tip is drivento reach a point distant dsafety (i.e. the cylindrical radius)from ps if d is less than dsafety.Experimental ProtocolThe vessel segmentation algorithm was tested on fiftymicroscopy  video  frames  acquired  during  twoneurosurgical interventions at Istituto Neurologico CarloBesta,  Milan,  Italy.  The  segmentation  algorithm  wasevaluated  in  terms  of  accuracy,  sensitivity,  specificityand Dice similarity coefficient, computed with respectto gold-standard manual segmentation performed by anexpert. Figure 1. On the left the system set-up: (a) ASAP trackingsystem; (b)  Zeiss stereo operating microscope; (c) Micronmanipulator. On the right orginal video frames and examplesof printed pattern.For  the  VF  performance  evaluation,  anexperimental  setup  was  developed  to  simulate  thesurgical  environment  and  let  Micron  interact  withphantom vessels. Three flat patterns that reproduce thevessels  to  avoid  were  printed  (Fig.1).  Twenty-oneimages  of  these patterns  (seven of  each  pattern)  withdifferent orientations were captured by the left and theright  camera  of  the  Micron  microscope  (4Xmagnification). The accuracy of the 3D reconstructionwas  evaluated  in  terms  of  reprojection  errors  of  thepoint cloud. The vessel avoidance algorithm was tested in real timeby operating  Micron  above  an  image of  the  vascularpattern and making repeated attempts to cross the bloodvessel while in contact with the surface.RESULTSThe  vessel  segmentation  performance  values  arereported in Table 1.Table 1. Segmentation algorithm performanceAcc=Accuracy; Se=Sensitivity; Sp=Specificity; DSC=DiceSimilarity Coefficient; q1= 1st quartile; ~x =median; q3=3rd quartile; IQR=Interquartile Range.The  reprojection  error  statistics  computed  for  the  21phantom images are shown in Table 2.Table 2. Reprojection error distribution for 21 phantom imagesq1= 1st quartile; ~x =median; q3= 3rd quartile;IQR=Interquartile Range.Using the VF algorithm, the Micron tip was forced tolift over the vessel  (green trajectory in Fig. 2)  when thenull position of the manipulator (red trajectory in Fig. 2)was within  dsafety = 1.6 mm, as suggested in [9]. The dmeasure  over  time  when  the  VF  is  active  (green)  isplotted in Fig.2 against  what the distance would havebeen if the VF were inactive (red).Figure 2. On the left the Micron tip trajectories with respect tothe ASAP reference system when the virtual fixture is on(green) and off (red). On the right the distance from thesurface (d) of the Micron tip from the vessel surface over timewhen the virtual fixture is on (green) and what it would havebeen if the fixture were off (red). dsafety is 1600 μm.DISCUSSIONIn this  paper,  we presented a new approach to  vesselavoidance for safe robotic assisted neurosurgery, whichexploits  a  handheld tool  to  reliably constrain surgeonmovements into predefined forbidden zones. Future  works  will  aim  at  implementing  atracking algorithm for real-time applications in order totake  into  account  also  tissue  deformation  and  thepresence of surgical tools in the scene. A 3D phantomthat reproduces a more truthful surgical scenario will befurther developed for testing.ACKNOWLEDGMENTSPartial funding provided by U.S. National Institutes of Health (grant no. R01EB000526) and by Politecnico di Milano, Italy.REFERENCES[1]  Lawrence JD et  al.  An investigation into quality of  lifeimprovement  in  patients  undergoing  microvasculardecompression  for  hemifacial  spasm.  Journal  ofNeurosurgery. 2017; 1-9.[2] Sekula  RF  et  al.  Microvascular  decompression  forhemifacial spasm in patients> 65 years of age: an analysisof  outcomes  and  complications.  Muscle  &  nerve.2013; 48(5): 770-776.[3] Xu F et al. Petroclival meningiomas: an update on surgicalapproaches,  decision  making,  and  treatment  results.Neurosurgical focus. 2013; 35(6): E11.[4] Moccia  S  et  al.  Safety  enhancement  in  roboticneurosurgery  through  vessel  tracking.  Proc  6th  JointWorkshop  on  New  Technologies  for  Computer/RobotAssisted Surgery. 2016.[5] Frangi AF et al. Multiscale vessel enhancement filtering.Med Image Comput Comput Assist Interv. 1998;130-7.[6] Yang S et al. Manipulator design and operation for a six-degree-of-freedom  handheld  tremor-cancelingmicrosurgical instrument. IEEE/ASME Trans Mechatron.2015; 20(2):761-72.[7] Yang,  S.  Handheld  micromanipulator  for  robot-assistedmicrosurgery.  Ph.D.  dissertation,  Carnegie  Mellon  Univ.2015.[8] Bay H et al. Speeded-up robust features (SURF). ComputVis Image Understanding. 2008;110:346-59.[9] Trope  M  et  al.  The  role  of  automatic  computer-aidedsurgical  trajectory  planning  in  improving  the  expectedsafety  of  stereotactic  neurosurgery.  Int  J  Comput  AssistRadiol Surg. 2015;10(7):1127-40.

Toward safety enhancement in neurosurgery using a handheld robotic instrument

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Toward safety enhancement in neurosurgery using a handheld robotic instrument

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