Patent Application: US-201113016093-A

Abstract:
a global registration system and method identifies bronchoscope position without the need for significant bronchoscope maneuvers , technician intervention , or electromagnetic sensors . virtual bronchoscopy renderings of a 3d airway tree are obtained including vb views of branch positions within the airway tree . at least one real bronchoscopic video frame is received from a bronchoscope inserted into the airway tree . an algorithm according to the invention is executed on a computer to identify the several most likely branch positions having a vb view closest to the received rb view , and the 3d position of the bronchoscope within the airway tree is determined in accordance with the branch position identified in the vb view . the preferred embodiment involves a fast local registration search over all the branches in a global airway - bifurcation search space , with the weighted normalized sum of squares distance metric used for finding the best match .

Description:
during bronchoscopy , the physician maneuvers a bronchoscope through the airway tree . an image - based bronchoscopy guidance system provides discrete guidance at bifurcations . in order to register the view seen in the bronchoscope video with multi - detector computed - tomography mdct co - ordinate system , we model the image seen by the bronchoscope by using 3d mdct data . to model the vb views , the bronchoscope camera is first calibrated by using an off - line procedure [ 42 ]. parameters such as the focal length f and the fov angle of the camera are computed . coefficients are also found in order to correct the barrel distortion of the video frames as seen in fig1 . fig1 a and 1b show bronchoscopic video frame distortion correction . in particular , fig1 a shows an example of a bronchoscopic video frame with barrel distortion , and fig1 b shows the bronchoscopic video frame after distortion correction . to create vb views , the airway tree is automatically segmented using a robust segmentation algorithm [ 43 ]. the marching cubes algorithm is run on the 3d segmentation to obtain polygonal surface representation of the airway tree [ 44 ]. the virtual bronchoscope is designed as a pinhole camera model with a focal length of f and the same image dimensions as the bronchoscope camera . the endoluminal airway surfaces are assumed to be lambertian , with the light source at the focal point . secondary reflections are disregarded . using these assumptions , the vb view is rendered using opengl [ 45 ] as shown in fig2 a - 2f . virtual bronchoscopic views . fig2 a - 2c show examples of the 3d airway tree and the virtual bronchoscope ( yellow cylinder + graphical needle ) positioned within the trachea , left main bronchus and the right main bronchus ( shown in fig2 d - 2f ) and the corresponding vb views . global registration can be defined as establishing the current branch position of the bronchoscope in the 3d airway tree . to formulate the global registration problem , we consider the scenario where the bronchoscope is “ blindly ” inserted into the airway so that it lies at some bifurcation . we denote the real bronchoscopic ( rb ) video frame from the bronchoscope as i v ( x , v ). the virtual bronchoscopy ( vb ) renderings obtained from the virtual bronchoscope are denoted by i ct ( x , y ). the current unknown viewpoint of the bronchoscope is denoted by θ =( x , y , z , α , β , γ ), where ( x , y , z ) gives the 3d spatial position and ( α , β , γ ) specifies the euler angles . the global registration algorithm finds the branch that contains the viewpoint with a vb view closest to the given rb view . this problem can be posed as a maximum a posteriori ( map ) problem . the first part is probability density estimation problem , where we estimate the posterior density over the space of available bronchoscope poses given the incoming bronchoscopic video . this problem formulation is similar to that by moreno et al [ 32 ]. in that paper , an iterative solution is used for global registration but we present a method for registration using a single frame which is similar to the method by wei β et al [ 46 ]. from map point of view , the global registration problem is an optimization problem where we estimate the branch in which the bronchoscope gives a pose that maximizes the posteriori probability density where { circumflex over ( θ )} is the estimated optimum view point , χ is one of the view points in k tree , the search space for the view points and p ( χ | i v ) is the posterior density over the space of available bronchoscope poses given the incoming bronchoscopic video . using bayes theorem , we get in the above expression , if all bronchoscope poses are considered equi - likely p ( χ ) can be considered as a constant and p ( i v ) is a constant with respect to χ . thus , we get the term p ( i v | χ ) can be estimated using a similarity function that finds the similarity between i v and the vb image rendered at χ , given by i ct χ . hence , ( 3 ) becomes argmax p ( i v | χ )= argmax c ( i v , i ct χ ) ( 4 ) where c (•,•) is a similarity function that gives the measure of similarity between the bronchoscopic video frame i v and i ct χ the rendering obtained at pose χ . if the similarity function c (•,•) is replaced by a dissimilarity measure , the global registration problem becomes here d (•,•) is a dissimilarity function between the rb image i v and the vb view i ct χ at view point χ and k tree is the search space for the view points . the optimum branch is given by where b (•) is a function that finds the branch containing the view point { circumflex over ( θ )}. the proposed algorithm accepts as input one or more live bronchoscopic video frames and outputs the 3d position of the bronchoscope within the airway tree . the algorithm is invoked during live bronchoscopy when the position of the bronchoscope is unknown . the algorithm will then determine the bronchoscope position within the airway tree . before invoking the algorithm , the physician positions the bronchoscope to give a good view of an airway bifurcation , so that the lumen region is well represented , as shown in fig4 . multiple runs of the algorithm on either the same bifurcation or at related bifurcations ( parent - daughter branches ) can be used to improve the overall accuracy of the method . the algorithm broadly divides into two major stages : intra - branch search and inter - branch search . fig3 illustrates a pre - computation for intra - branch search . this figure shows the pre - computation carried out for the branch highlighted by the blue line . the red lines are the pre - defined centerline navigation routes through the 3d airway tree . the green triangles highlight three of the pre - defined view - sites ( a , b , c ) along the selected branch . the figures to the right of the view - sites are the vb renderings ( a 1 , b 1 , c 1 ), the lumen thresholded images ( a 2 , b 2 , c 2 ) and the minimum enclosing rectangles ( a 3 , b 3 , c 3 ) associated with the respective view - sites . the longer length of the minimum enclosing rectangle is saved during pre - computation and later used for localizing the position of the given rb frame in a given branch . where c (•,•) is a similarity function , { circumflex over ( θ )} b i is the optimum view point in branch i for the given rb video frame i v and b i is a subset of k tree and contains all the view points in branch i . this optimization process needs spanning through all possible view positions in a given branch . this is not practical in a real - time algorithm due to the excessive time involved . hence , we use a novel intra - branch search which comprises of two steps . in the first step , the intra - branch search carries out a fast scan through all the pre - defined view sites along the centreline of the branch . the second step comprises render position adjustment using fast local registration . because of uncertainty in the roll angle of the bronchoscope we use a set of four likely positions for each branch using different roll angles . the minimum enclosing rectangle of the lumen region has a change in its larger dimension as the render position moves along the centreline towards the bifurcation as shown in fig3 . this is the underlying idea of the fast - scan search along the centreline . in this search , first the input real image i v is thresholded to segment out the lumen region . the p - tile thresholding with a value of p = 10 has been empirically found to give good results for segmenting out the lumen . once the lumen region is segmented , the convex hull of the segmented region is found . we use the bentley - faust - preparata ( bfp ) fast approximate 2d convex hull algorithm for this purpose [ 47 ]. this convex hull is used to find the minimum enclosing rectangle of the lumen region as shown in fig3 . the larger dimension of the enclosing rectangle is saved as dim max . the same feature extraction step is carried out at all the pre - defined centreline view points of all the branches as a pre - computation step and the larger dimension of the enclosing rectangle is saved . during live global registration , the intra - branch search gives the best centreline view - point at each branch that has its feature value closest to dim max . the best rendering position that is obtained has a fixed roll angle . however , the real image could have any possible roll angle . to address this , roll angles of 90 degrees , 180 degrees and 270 degrees are applied to this rendering position resulting in the four best rendering positions for each branch . the real image is obtained from a bronchoscope having multiple degrees of freedom . so the four best rendering positions obtained in the previous step may not give rendering images i ct similar to the real image i v . therefore , the four rendering positions obtained from the previous step are adjusted further using the inverse compositional method for local registration [ 24 ]. the local registration uses wnssd metric for image comparison and the optimal gauss - newton gradient for parameter update ( θ δ ) as given in ( 8 ) and ( 9 ). where μ ct and μ v are the respective weighted - image means and θ ct 2 and σ v 2 are the respective weighted image variances and w u , v are weights that can be used to arbitrarily assign higher importance to pixels in the image based on geometry , graylevel value , gradient strength , or any appropriate confidence measure . here , we use the trivially weighted case is a 6d row vector of steepest - descent images , ī v ( w ( u , v , z ; θ eq )) and ī ct ( u , v ) are the normalized images and h , the gauss - newton hessian is computed as parameter update is run for 200 ms , which is sufficient for convergence and limits the total run - time of the algorithm resulting in four image rendering positions for each branch . the rendered images at each of these positions are thresholded to obtain the lumen region . these thresholded images are then compared with the lumen thresholded image obtained from the real image using the metric c 1 described in equation ( 11 ). substituting from equation ( 11 ) in equation ( 7 ) we get the best rendering position of the branch . where k { circumflex over ( θ )} b i ={{ circumflex over ( θ )} b 1 , { circumflex over ( θ )} b 2 , . . . , { circumflex over ( θ )} b n } is the set of view points obtained from the intra - branch search . in ( 12 ) we use the wnssd dissimilarity metric as defined in equation ( 8 ). the optimum view point is used to find the current bronchoscope branch position using equation ( 6 ). the above global registration method has been summarized in equation ( 12 ). fig4 a - 4f show the best matches for the different branches of the search space and fig4 b , the correctly identified branch after the inter - branch search . fig4 a shows the input rb frame and fig4 b - 4f show the vb images associated with the best bronchoscope position as found by the intra - branch search in five different branches of the search space . inter - branch search correctly identified branch with rendering shown in fig4 b as the best branch . threshold the image i ct using p - tile thresholding ( p = 10 ). use bfp algorithm to find the convex hull of the thresholded lumen from view - site j , obtain 4 viewing positions ( roll angles of use ( 11 ) to find the vb image at the best render position for the use ( 8 ) to find d 1 ( i ) ( i ct = i ct θ b 1 ). to evaluate the accuracy of the global registration algorithm , we carried out three sets of tests . in the first set , we evaluated the global registration algorithm using virtual case studies . the second set of tests evaluated the accuracy of the global registration algorithm using rb frames obtained from the bronchoscopic exploration of an airway phantom . in the third set , we evaluated the improvement in the accuracy of the algorithm when multiple test frames were used . the global registration algorithm was evaluated using the three virtual cases derived from the ct data of consented patients as described in table i . while using virtual bronchoscopic cases , a minimum of 28 branches from the first five airway generations were used . four different tests were carried out by varying the search - space of the algorithm . in all tests , a virtual bronchoscopic bifurcation view was randomly selected from the branch search space — this served as the unknown “ live bronchoscope video ” view in the test . this bifurcation view was obtained by randomly moving to any of the view sites along a branch . a random roll angle from 0 - 360 degrees was used and a perturbation of up to ± 5 mm was applied to the virtual bronchoscopic position to move the virtual bronchoscope off the center line . in the first test set , all branches were used for the search space . in the second set , the search space was divided into two by using branches either in the left lung or right lung only . in the third set , the search space was divided into five different parts based on the lung lobar regions . in the fourth set , only branches that were daughters of the same branch were used in the search space . the results of these tests are summarized in table ii . global registration accuracy ranged from 71 % to 92 %. note that a random selection from among 28 airway branches would only give an “ accuracy ” of 3 . 6 %. table ii global registration accuracy in percent for different test sets for virtual bronchoscopic cases . case full - tree half - tree lobar branch pairs number ( set 1 ) ( set 2 ) ( set 3 ) ( set 4 ) 47 73 % 76 % 80 % 92 % 49 75 % 77 % 83 % 91 % 61 71 % 73 % 80 % 83 % the airway phantom test involved live bronchoscopy on a preconstructed airway - tree phantom [ 25 ]. the phantom consists of 5 different accessible branch bifurcations when using a 5 . 9 mm diameter bronchoscope . while carrying out the global registration , each incoming video frame from the bronchoscope was used as input and the output of the global - registration algorithm was one of the five branches from the search space . in order to carry out the tests , the bronchoscope was moved to each of the five different branches and the video collected from this bronchoscope maneuver was used in the testing . from the collected video , only those frames that gave good bifurcation views were used in the study . a total of 836 such frames were obtained . the global registration algorithm gave an accuracy of 89 % for the 836 frames ( see fig5 a - 5j ). fig5 a - 5j show global registration results for airway phantom . the top row ( fig5 a - 5e ) are the input bronchoscopic video frames and the bottom row ( fig5 f - 5j ) are the vb images at the bronchoscope position estimated by the algorithm . to evaluate the performance of the global registration algorithm using multiple frames , the virtual cases described in table i were used . this test setup was the same as that used for evaluation using virtual cases . we carried out two sets of tests . in the first set , multiple “ random views ” of the same bifurcation were used for testing . in this testing methodology , each of the frames was independently evaluated by the global registration algorithm . the frame that gave the lowest wnssd metric ( bestvalue from ( 12 )) decided the branch location for the frames . in the second set , test frames from two consecutive bifurcations were used . both the branches were independently evaluated . of the two test frames , the frame giving the lower wnssd metric determined the branch pair . the results for this set of testing has been summarized in table iii . the global registration accuracy ranges from 82 % to 99 %. in all of the above tests , the algorithm was found to run in an average time of 2 - 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