SPIE Image-Guided Procedures, Robotic Procedures, and Modeling, VOLUME 8671

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1 A Gaussian Mixture + Deformable Registration Method for Cone-Beam CT-Guided Robotic Transoral Base-of-Tongue Surgery S. Reaungamornrat, a W. P. Liu, a S. Schafer, b Y. Otake, a,b S. Nithiananthan, b A. Uneri, a J. Richmon, c J. Sorger, d J. H. Siewerdsen, a,b and R. H. Taylor a a Department of Computer Science, Johns Hopkins University, Baltimore MD b Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD c Department of Otolaryngology Head and Neck Surgery, Johns Hopkins Medical Institute, Baltimore MD d Intuitive Surgical Inc., Sunnyvale, USA ABSTRACT Purpose: An increasingly popular minimally invasive approach to resection of oropharyngeal / base-of-tongue cancer is made possible by a transoral technique conducted with the assistance of a surgical robot. However, the highly deformed surgical setup (neck flexed, mouth open, and tongue retracted) compared to the typical patient orientation in preoperative images poses a challenge to guidance and localization of the tumor target and adjacent critical anatomy. Intraoperative cone-beam CT (CBCT) can account for such deformation, but due to the low contrast of soft-tissue in CBCT images, direct localization of the target and critical tissues in CBCT images can be difficult. Such structures may be more readily delineated in preoperative CT or MR images, so a method to deformably register such information to intraoperative CBCT could offer significant value. This paper details the initial implementation of a deformable registration framework to align preoperative images with the deformed intraoperative scene and gives preliminary evaluation of the geometric accuracy of registration in CBCT-guided TORS. Method: The deformable registration aligns preoperative CT or MR to intraoperative CBCT by integrating two established approaches. The volume of interest is first segmented (specifically, the region of the tongue from the tip to the hyoid), and a Gaussian mixture (GM) mode1 of surface point clouds is used for rigid initialization (GMRigid) as well as an initial deformation (GMNonRigid). Next, refinement of the registration is performed using the algorithm applied to distance transformations of the GM-registered and CBCT volumes. The registration accuracy of the framework was quantified in preliminary studies using a cadaver emulating preoperative and intraoperative setups. Geometric accuracy of registration was quantified in terms of target registration error (TRE) and surface distance error. Result: With each step of the registration process, the framework demonstrated improved registration, achieving mean TRE of 3.0 mm following the GM rigid, 1.9 mm following GM nonrigid, and 1.5 mm at the output of the registration process. Analysis of surface distance demonstrated a corresponding improvement of., 0.4, and 0.3 mm, respectively. The evaluation of registration error revealed the accurate alignment in the region of interest for base-of-tongue robotic surgery owing to point-set selection in the GM steps and refinement in the deep aspect of the tongue in the step. Conclusions: A promising framework has been developed for CBCT-guided TORS in which intraoperative CBCT provides a basis for registration of preoperative images to the highly deformed intraoperative setup. The registration framework is invariant to imaging modality (accommodating preoperative CT or MR) and is robust against CBCT intensity variations and artifact, provided corresponding segmentation of the volume of interest. The approach could facilitate overlay of preoperative planning data directly in stereo-endoscopic video in support of CBCT-guided TORS. Keywords: deformable registration, algorithm, Gaussian mixture model, cone-beam CT, image-guidance, robotic surgery, video augmentation 1. INTRODUCTION In the United States, the incidence of oropharyngeal cancer has increased for at least six consecutive years, and would lead to approximately 7,850 deaths in 01, as approximated by the American Cancer Society 1-6. The standard of care for resection of oropharyngeal cancer includes traditional invasive surgery, which involves high morbidity, long recovery, and functional deficits. Chemo-radiotherapy offers an alternative treatment but may result in gastrostomy tube dependence due to mucosal injury and tissue fibrosis. Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of oropharyngeal cancer with reduced morbidity, decreased recovery time, improved postoperative function in comparison to open procedures, 7 and reduced toxicity in comparison to radiation with chemotherapy. However, localization of the surgical target (tumor) and adjacent critical tissues (lingual nerves and arteries) is challenging, since preoperative CT and/or MR typically employed to define and visualize such structures before surgery are of lim- February, 013 (Orlando FL)

2 ited value for precise localization in the highly deformed intraoperative setup (neck flexed, mouth open, and tongue extracted). We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately account for such deformation and to provide a basis for registration of preoperative images. The registration method is intended to accurately transform preoperative images and planning data to the intraoperative scene and subsequently permit overlay of preoperative planning data directly in stereo-endoscopic video to improve localization accuracy in TORS. 8 A logistical and workflow challenge arises with the addition of a 3D-capable C-arm to the already crowded operating environment in a manner that allows both the C-arm and the robot to operate without collision. Preliminary studies show that such C- arms (whether mobile, ceiling-mounted, or floor-mounted) can be arranged in a manner consistent with the TORS setup - at least in a manner allowing partial orbit of the C-arm (i.e., limited angle tomography) and possibly a full 180 o + fan angle rotation (i.e., CBCT). Such arrangements and capabilities are the subject of ongoing work. One feasible clinical workflow for CBCT-guided TORS is illustrated in Fig. 1, involving a C-arm used at distinct points in the procedure in a way that does not interfere with operation of the robot. Specifically, following patient setup (IV), C-arm CBCT is acquired (V) prior to docking the robot, providing a basis for preoperative-to-intraoperative image registration (VI) and guidance under stereo video (VII). This paper describes the deformable registration method that is central to accurately transforming preoperative data into the highly deformed intraoperative scene. As detailed below, the registration framework integrates a Gaussian mixture (GM) point cloud initialization followed by an intensity-invariant implementation of the algorithm. Other components of the system, such as C-arm CBCT and augmentation of stereo-endoscopic video, are the subject of other work. Figure 1. Potential clinical workflow for CBCT-guided TORS. Preoperative steps include CT or MR imaging (I), segmentation and surgical planning (II), and video calibration (III). Intraoperative steps include patient setup (IV), C-arm CBCT (V) prior to docking the robot, deformable registration of preoperative images and intraoperative CBCT (VI), and transoral robotic surgery with stereo-endoscopic video visualization overlaid by deformably registered image and planning data (VII).. DEFORMABLE REGISTRATION FOR CBCT-GUIDED TORS.1 Registration Framework A registration framework (step (VI) in Fig. 1) was developed to resolve deformation from the moving image (preoperative CT or MR, denoted I 0 ) to the fixed image (intraoperative CBCT, denoted I 1 ). The framework includes 4 steps as illustrated in Fig.. The first step involves definition and segmentation of the volume of interest (VOI) (including tongue and hyoid bone) in both I 0 and I 1. While advanced segmentation methods are under development, 9-1 initial studies used semi-automatic active-contour segmentation 13 followed by manual refinement. The segmentation included the superior surface of the tongue and extended laterally within the bounds of the mandible and inferiorly to the hyoid. The resulting segmentation masks (denoted S 0 and S 1 ) provided surface meshes from which point clouds were defined (P 0 and P 1, respectively). Second, Gaussian-mixture (GM) registration as described by Jian et al. 14, 15 was used to compute a rigid alignment of point clouds (GM Rigid ) followed by a thin-plate spline deformation (GM NonRigid ). The use of continuous probability densities to model the discrete point sets and L distance as a measure of density similarity relaxed the requirement for explicit point correspondence and improved robustness against outliers. Finally, the fast-symmetric algorithm 16-0 was applied to distance transformations (DT) 1 of the moving and fixed masks after GM registration. The entire framework is therefore independent of image intensity, since the step operates on the DT images rather than the image intensities directly, thereby permitting multi-modality registration such as preoperative CT or MR to intraoperative CBCT. The underlying requirement, of course, is a consistent initial segmentation of the tongue in images I 0 and I 1. February, 013 (Orlando FL)

3 Figure. Schematic flow diagram of the deformable registration framework. 0Acquisition of preoperative MR or CT (I 0 ) and intraoperative CBCT (I 1 ). Segmentation of the VOI and surface-point extraction. GM rigid and GM nonrigid point registration. algorithm applied to distance transformation of the moving and fixed images.. Experimental Measurement of Registration Accuracy The registration accuracy was analyzed in cadaver studies emulating the preoperative setup (i.e., mouth closed and tongue in natural pose) and intraoperative setup (i.e., mouth open and tongue retracted) as illustrated in Fig. 3(a,b). Preoperative CT images (I 0 ) were acquired (Philips Brilliance CT, Head Protocol, 10 kvp, 77 mas) and reconstructed at a voxel size of ( ) mm 3, and intraoperative CBCT images (I 1 ) were acquired using a mobile C-arm prototype -5 (100 kvp, 30 mas) and reconstructed at a voxel size of ( ) mm 3 as illustrated in Fig. 3(c,d). Figure 3. The intraoperative setup used in preliminary cadaver studies. (a) A prototype mobile C-arm capable of CBCT reconstruction, 4, 5 was used for acquisition of intraoperative images. (b) The cadaver was mounted in a CT-compatible frame. The mouth was open, and the tongue was retracted and secured with nylon strings. (c) An example preoperative CT (I 0 ). (d) An example CBCT (I 1 ) in the intraoperative state (mouth open and tongue retracted). The performance of the registration framework was evaluated from 5 image pairs (CT and CBCT) using the algorithm parameters summarized in Table 1. The nominal value and operating range of each registration parameter were obtained from a sensitivity analysis in which the target registration error (TRE) was computed (as detailed below) as a function of each algorithm parameter in a univariate analysis considering both geometric accuracy and runtime. Details of the sensitivity analysis and more detailed description of methodology is detailed in other work. 6 Registration accuracy was measured in terms of TRE in six 1.5 mm Teflon spheres implanted in the base of the tongue (i.e., region most pertinent to TORS), and two (left and right) prominences on the hyoid bone. Alignment was also assessed in terms of the distance between the S 0 and S 1 surfaces after registration. Analysis of geometric accuracy and image similarity over the entire segmented subvolume are the subjects of future work. Each registration step is described in the Sections 3-5 and the overall registration performance is described in Section 6. February, 013 (Orlando FL)

4 GM Rigid Parameter n GMRigid Nominal Values 800 (mm) Level 1: 0.5 Level : 0.15 Level 3: 0.05 Table 1. Nominal values of registration parameters n 800 GM Nonrigid GMNonRigid (mm) Level 1: 0.50 Level : Level 3: 0.05 GMNonRigid (mm) Level 1: Level : Level 3: DisplacementField (voxels) UpdateField (voxels) MSL (voxels) RIGID INITIALIZATION USING A GAUSSIAN MIXTURE (GM) MODEL 3.1 Rigid Registration using a Gaussian Mixture Model (GM Rigid) The entire registration process is initialized with a rigid registration computed from a GM model. The initial alignment defined a rigid mapping from the moving point cloud (P 0 ) to the fixed point cloud (P 1 ). The GM model of the point clouds consisted of a linear combination of Gaussian density components - for example, a moving point cloud P 0 (containing x 0,i for 1 < i < n in R 3 ) modeled as an over-parameterized GM containing n Gaussian components: 1 n ( x 0 ) i ( x x0,i, ) (1) n i 1 where 0 = (( 1 n, x 0, 1, ),, ( 1 n, x 0, n, )) is the set of mixture parameters describing all n equally-weighted, iso- tropic Gaussian components, is the standard deviation in mm, the 1 n term is the equal weighting, and ( x x, ) is an isotropic Gaussian density i, represented as: i 0, i 3 1 i( x x0,i, ) ( ) exp[ x x0 ], i () where x0, i is a point i in P 0 and a mean vector of a Gaussian component i. The fixed point cloud P 1, also containing n points, is modeled similarly using the standard deviation of for n equally weighted isotropic Gaussian components. The resulting rigid transformation is parameterized by a rotation matrix R and a translation vector t which can be applied to a moving point x as: 0, i GM Rigid[ x0,i ] Rx0, i t (3) where GM Rigid is the rigid transformation. We estimated GM Rigid by minimizing the L distance between the two point cloud mixtures. Since the L -norm of a fixed mixture is constant, and the L -norm of a moving mixture is invariant under rigid transformation (i.e., ( x 0 ( R, t)) dx ( x 0) dx ), the cost function only involved the inner product between the two mixtures: d x ( R, t)) ( x ) dx (4) ( 0 1 where 0( R, t) is the set of GM parameters for the rigidly transformed moving mixture, and 1 is the set of GM parameters for the fixed mixture. The limited memory Broyden-Fletcher-Goldfarb-Shanno method with bounded constraints (L- BFGS-B) 7, 8 was used to minimize the cost function in a hierarchical multiscale scheme to improve robustness against local minima. In this work, the nominal number of hierarchical levels was experimentally identified and fixed to three, with minimization starting at the coarsest level, where the isotropic Gaussian components of both mixtures were modeled using a large standard deviation. The rigid transformation resulting from the coarsest level was passed to the next finer level, with the standard deviation reduced at each step. February, 013 (Orlando FL)

5 3. Results: GM Rigid Registration Figure 4 illustrates the initial rigid alignment of the fixed and moving images after GM rigid registration (step in Fig. Mask ). On the left is a sagittal slice of the (segmented) fixed image ( I 1 ) showing the tongue in the deformed, extended state (contour in blue). On the right is the moving image following GM rigid registration ( ) overlaid in green. I Mask 0 GMRigid The CT and CBCT images illustrate differences in air infiltration in the cadaver (particularly in the tissue and vessels along the inferior aspect of the tongue) and in image quality in each modality (sharper, noisier, and with a greater degree of streak artifacts for CBCT, compared to the smoother and more uniform CT image), although the image intensities are not directly used in the registration (i.e., the framework is independent of the underlying intensity or modality to the extent that a consistent segmentation of the tongue is obtained in I 0 and I 1 ). Rigid registration is seen simply to align the bulk (~center of mass) of the region of interest, although the GMRigid parameter could be varied somewhat to alter the rigid registration to yield better alignment at the tip (higher GMRigid ) or the base (lower GMRigid ). Figure 4. Initialization by rigid registration. (left) A sagittal slice of the intraoperative CBCT image (i.e., the fixed image). (right) The corresponding sagittal slice of the preoperative CT image after GM rigid initialization in step of Fig.. The performance of the initial GM rigid registration (step ) evaluated using 5 pairs of CT and CBCT is illustrated in Fig. 5. An example image pair after GM rigid registration is displayed as a semi-opaque overlay, achieving TRE of 3.0±1.6 mm (median TRE =.9 mm) and surface distance error of. ± 3.1 mm. The median TRE at each target fiducial (8 target points at the base of the tongue) over all 5 pairs was interpolated over the VOI to yield the TRE map shown on the right of Fig. 5. The interpolation used multi-quadric (MQ) radial basis functions with a shape parameter selected using the method suggested by Foley 9, 30. Figure 5. Initialization by GM rigid step of Fig.. (left) Semiopaque overlay of fixed and moving images, showing the relative alignment of surfaces and target points implanted in the tongue. (right) Sagittal slice of the interpolated median TRE measured from 5 image pairs, with median TRE =.9 mm. 4. DEFORMATION USING A GAUSSIAN MIXTURE 4.1 Nonrigid Registration using a Gaussian Mixture Model (GM Nonrigid) GM nonrigid registration step ( in Fig. ) was applied to compute deformation of the point clouds following the GM rigid step. The nonrigid mapping of moving and fixed point clouds was estimated using a GM model similar to that described above. A thin-plate spline (TPS) representing the deformation was parameterized by affine components (A and t) and a warp component (W) which is applied to each point in the moving image as: GM NonRigid [ x0,i GMRigid 0,i GMRigid 0,i GMRigid ] Ax t WU ( x ) (5) February, 013 (Orlando FL)

6 where x 0,i GMRigid is a point i in the moving image after GM rigid registration, A is a 3 3 affine coefficient matrix, t is a 3 1 translation vector, W is a 3 n warp coefficient matrix, and U(x i ) = [ U(x i, x k ) ] is a n 1 vector of TPS basis functions. The basis function for 3D is defined as U(x i, x k ) = x i x k where is a Euclidean distance 14. The TPS was estimated in a regularization framework in which the regularization term involves the TPS bending energy proportional to WKW T. The kernel K is the n n matrix containing k i,j = U(x i, x j ). The cost function was the combination of a distance measure and the bending energy represented by: ( E x ( W,A, t)) - ( x ( W,A, t)) ( x ) dx trace ( WKW ) (6) GMNonRigid where 0( W,A, t) is the set of GM parameters for the warped moving mixture, 1 is the set of corresponding parameters for the fixed mixture, and GMNonRigid is a regularization parameter. The cost function, similar to GM rigid, was iteratively minimized using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method 31 in a multiscale scheme, with the number of the levels fixed at three. In addition to adjusting the standard deviation ( GMNonRigid ) in each level, the strength of regularization was also adjusted at each level by varying GMNonRigid. Increasing GMNonRigid, amounts to stronger smoothing of the resulting deformation. The final GM NonRigid transform was used to interpolate a displacement for each voxel in the (rigidly registered) volume to a corresponding voxel in the fixed volume. These displacement vectors comprised a GM nonrigid displacement field denoted D GMNonRigid. 4. Results: GM Nonrigid Registration The performance of the GM nonrigid registration step is summarized in Figs. 6 and 7. The GM nonrigid displacement field (D GMNonRigid ) shows the transformation mapping gross deformation of the tongue up to ~30 mm from to T Mask GMRigid I Mask 0 GMNonRigid the fixed CBCT image. The resulting deformed image is shown on the right in Fig. 6, where a sagittal slice of the image after GM nonrigid registration (, shown in orange) is overlaid with the fixed CBCT image (blue). The previously noted differences associated with air infiltration in the cadaver are again evident, but because these features were not included in the point clouds driving the GM Rigid and GM NonRigid transforms, they did not affect the registration process, and fairly good alignment of surfaces was achieved. I 0 Figure 6. Deformable registration using a GM model of point clouds. Sagittal slices show the deformation field (D GMNonRigid ) and image resulting from the GM nonrigid step. Labels and correspond to steps in Fig. Figure 7 illustrates the geometric accuracy of the GM nonrigid registration step assessed from 5 (CT and CBCT) image pairs. The semi-opaque overlay of volumes shows improvement in surface matching, with mean surface distance of 0.4 mm. As illustrated in the interpolated TRE map on the right of Fig. 7, the overall TRE was improved to 1.9 ± 0.9 mm. February, 013 (Orlando FL)

7 Figure 7. Deformable registration using a GM model of point clouds. (left) Semiopaque overlay of fixed and moving volumes and 16 target points implanted therein. A median TRE of 1.7mm was achieved, with overall TRE illustrated in the (right) sagittal TRE map computed from 5 image pairs. 5. REFINEMENT USING DEMONS REGISTRATION 5.1 Multi-Resolution Registration on Distance Transformation Images The registration concludes with a refinement of the deformed image arising from the previous GM nonrigid step using the algorithm. First, to maintain independence of the registration process to image intensity and improve robustness against image artifacts, a distance transformation (DT) 1 was computed for both the fixed mask (S 1 ) and the moving mask after GM rigid and nonrigid registration (denoted simply as S 0 GM ). The resulting "distance map" presents an image showing the distance of each voxel from the closest boundary of the mask. Thus, voxels in the deeper aspects of the tongue exhibit high DT values and voxels at the boundary and outside the mask were assigned to zero. A histogram matching 3 was first applied to normalize the DT images prior to registration. The fast symmetric force variant of the algorithm was implemented in which gradients from both DT images are used in estimating the displacement field. The process alternates between estimation of force and regularization using Gaussian filters. Regularization can be applied to the update field prior to field addition or to the estimated displacement field after field addition. 16, 33 The field addition at the current iteration it was represented by convolution of the update and displacement fields with Gaussian kernels: D ( it) ( it 1) ( it) G ( D G U) (7) DisplacementField UpdateField (it) ( 1) where D is the estimated displacement field at the iteration it, D it is the previously estimated displacement (it) field, U G DisplacementField is the current update field, UpdateField G is a Gaussian filter with a standard deviation of UpdateField, and is a Gaussian filter with a standard deviation of DisplacementField. Smoothing the update field with results in a fluid-like deformation model, while smoothing the displacement field with G DisplacementField G UpdateField results in an elastic-like model. The properties and capabilities of each deformation model was illustrated in the literature. 33 iteratively composes the displacement field in a multi-resolution scheme, where a hierarchical coarse-to-fine image pyramid was constructed by repeatedly downsampling the DT images by a factor of two (e.g., downsampling factors of 8, 4,, and 1 voxels). Registration begins at the coarsest level of the pyramid and after convergence, the displacement field is upsampled and initializes the displacement field at the subsequent finer level. The convergence criterion used in the current experiments was based on changes in mean square error of voxel values Results: Registration The performance of the registration following the step is shown in Figs. 8 and 9. An example distance map DT for the fixed image is shown in Fig. 8 (left). The displacement field (middle, with superimposed vectors scaled in magnitude by a factor of two for visualization) is seen to refine the registration primarily in deeper aspects of the base-oftongue. On the right, a sagittal slice of the resulting deformed image after registration ( I Mask 0, contoured in pink) is overlaid by the fixed CBCT (blue contour) for comparison. February, 013 (Orlando FL)

8 Figure 8. Refinement using deformable registration. (left) An example distance transform (DT) map computed on the fixed CBCT image. (middle) Example displacement field (vector magnitudes scaled by a factor of for purposes of visualization), showing a refinement of image alignment primarily in the deep aspects of the tongue. (right) Sagittal slice illustrating the final image resulting from the entire registration process. Since the GM rigid and nonrigid steps operate on surface point clouds, and the step operates on DT distance maps, the entire process is independent of image intensity and applicable to registration of preoperative CT or MR to intraoperative CBCT. The improvement in geometric accuracy following the step is depicted in Fig. 9. The semi-opaque overlay of volumes shows a high degree of surface matching and fairly good alignment of target points. The surface distance error was 0.3 ± 0.3 mm. As illustrated in the TRE map of Fig. 9 (right), the overall TRE analyzed from 8 target points at the base-of-tongue in 5 image pairs was 1.5± 0.6 mm, with the median TRE of 1.4 mm. Note the improvement in each metric compared to the previous GM rigid and nonrigid initialization steps (Figs. 5 and 7, respectively). Figure 9. Refinement using deformable registration. (left) Semiopaque overlay of fixed and moving volumes, achieving median surface distance of 0.3 mm. (right) A sagittal slice of the interpolated median TRE measured from 5 image pairs. 6. OVERALL REGISTRATION PERFORMANCE Table summarizes the overall registration performance of the framework evaluated in 5 image pairs and using the nominal values of registration parameters detailed in Table 1. The TRE improved from 3.0 ± 1.6 mm (median.9 mm) following the GM rigid step ( ) to 1.9 ± 0.9 mm (median 1.7 mm) following GM nonrigid ( ), and 1.5± 0.6 mm (median 1.4 mm) following ( ). The surface distance errors showed corresponding improvement from ( ). ± 3.0 mm (median 1.3 mm), to ( ) 0.4 ± 0.4 mm (median 0.4 mm), and finally ( ) 0.3 ± 0.3 mm (median 0.3 mm) at the output of the complete registration process. TRE (mm) Surface Distance (mm) Table. Registration accuracy of the registration framework GM rigid GM nonrigid 3.0 ± ± ± 0.6 (median.9) (median 1.7) (median 1.4). ± 3.0 (median 1.3 mm) 0.4 ± 0.4 (median 0.4 mm) 0.3 ± 0.3 (median 0.3 mm) February, 013 (Orlando FL)

9 7. DISCUSSION AND CONCLUSION A registration framework integrating GM models and algorithm was developed to resolve the large deformation in transoral base-of-tongue surgery. A system currently under development for CBCT-guided TORS would incorporate the framework by taking intraoperative CBCT as a basis for deforming preoperative images and planning data into the highly deformed intraoperative scene. Since the framework operates on GM models of surface points and distance transforms of the segmentation masks, it is independent of image intensity and therefore suitable to multi-modality image registration - e.g., preoperative CT and/or MR to intraoperative CBCT. The registration framework involves a hybridization of model-based initialization followed by refinement. Previous work 35, 36 reported a similar two-step strategy to successfully register CBCT images of the inflated and deflated lung for thoracic surgery in which a mesh evolution (rather than a GM model) was employed for initialization followed by an intensity-corrected variant for refinement. An interesting area of potential future work includes software architecture presenting a streamlined interface for combination of such hybrid registration methods, facile adjustment of the underlying algorithmic parameters, and highspeed (GPU) implementation combined with internal validation checks and measures of registration accuracy. The overall registration performance exhibited in the preliminary cadaver studies reported above corresponded to a mean TRE of 1.5 mm and mean surface distance error of 0.3 mm, revealing accurate alignment at the region of interest for robot-assisted, base-of-tongue surgery. The initial implementation required approximately 5 minutes to complete the registration process (including 1 s for point-cloud extraction, 1.3 s for the GM rigid step,. min for the GM nonrigid step, and.6 min for ). This is potentially within the workflow requirements shown in Fig. 1. Improvements for consideration in future work include a modified GM nonrigid step using compact-support radial basis functions and implementation on GPU. Streamlined, semi-automatic segmentation could be developed for practical application of the method. Also the addition of registration constraints from internal structures (e.g., the lingual vessels) could potentially improve registration accuracy, and complete evaluation of the registration framework. ACKNOWLEDGEMENTS This research was supported in part by the Thai Royal Government Scholarship, the Department of Computer Science (Johns Hopkins University), Siemens Healthcare (XP, Erlangen, Germany), and NIH R01-CA The authors gratefully acknowledge Dr. Erik Tryggestad (Department of Radiation Oncology, Johns Hopkins University) and Dr. Adam Wang (Department of Biomedical Engineering, Johns Hopkins University) for assistance with data acquisition, and Dr. Bing Jian and Dr. Jerry L. Prince (Department of Electrical and Computer Engineering, Johns Hopkins University) for valuable discussion regarding Gaussian mixture models. Dr. Rainer Graumann and Dr. Gerhard Kleinszig (Siemens XP) provided collaboration on development of the prototype mobile C-arm for CBCT. The authors extend their thanks to Mr. Ronn Wade (University of Maryland, State Anatomy Board) for assistance with the cadaver specimens. REFERENCES [1] Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J. and Thun, M. 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