In vitro accuracy evaluation of image-guided robot system for direct cochlear access

HYPOTHESIS A previously developed image-guided robot system can safely drill a tunnel from the lateral mastoid surface, through the facial recess, to the middle ear, as a viable alternative to conventional mastoidectomy for cochlear electrode insertion. BACKGROUND Direct cochlear access (DCA) provides a minimally invasive tunnel from the lateral surface of the mastoid through the facial recess to the middle ear for cochlear electrode insertion. A safe and effective tunnel drilled through the narrow facial recess requires a highly accurate image-guided surgical system. Previous attempts have relied on patient-specific templates and robotic systems to guide drilling tools. In this study, we report on improvements made to an image-guided surgical robot system developed specifically for this purpose and the resulting accuracy achieved in vitro. MATERIALS AND METHODS The proposed image-guided robotic DCA procedure was carried out bilaterally on 4 whole head cadaver specimens. Specimens were implanted with titanium fiducial markers and imaged with cone-beam CT. A preoperative plan was created using a custom software package wherein relevant anatomical structures of the facial recess were segmented, and a drill trajectory targeting the round window was defined. Patient-to-image registration was performed with the custom robot system to reference the preoperative plan, and the DCA tunnel was drilled in 3 stages with progressively longer drill bits. The position of the drilled tunnel was defined as a line fitted to a point cloud of the segmented tunnel using principle component analysis (PCA function in MatLab). The accuracy of the DCA was then assessed by coregistering preoperative and postoperative image data and measuring the deviation of the drilled tunnel from the plan. The final step of electrode insertion was also performed through the DCA tunnel after manual removal of the promontory through the external auditory canal. RESULTS Drilling error was defined as the lateral deviation of the tool in the plane perpendicular to the drill axis (excluding depth error). Errors of 0.08 ± 0.05 mm and 0.15 ± 0.08 mm were measured on the lateral mastoid surface and at the target on the round window, respectively (n =8). Full electrode insertion was possible for 7 cases. In 1 case, the electrode was partially inserted with 1 contact pair external to the cochlea. CONCLUSION The purpose-built robot system was able to perform a safe and reliable DCA for cochlear implantation. The workflow implemented in this study mimics the envisioned clinical procedure showing the feasibility of future clinical implementation.

A Novel Ultrasound-Based Registration for Image-Guided Laparoscopic Liver Ablation.

BACKGROUND Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present a novel registration approach for application in laparoscopic liver surgery, which reconstructs in real time an intraoperative volume of the underlying intrahepatic vessels through an ultrasound (US) sweep process. METHODS An existing IGS system for an open liver procedure was adapted, with suitable instrument tracking for laparoscopic equipment. Registration accuracy was evaluated on a realistic phantom by computing the target registration error (TRE) for 5 intrahepatic tumors. The registration work flow was evaluated by computing the time required for performing the registration. Additionally, a scheme for intraoperative accuracy assessment by visual overlay of the US image with preoperative image data was evaluated. RESULTS The proposed registration method achieved an average TRE of 7.2 mm in the left lobe and 9.7 mm in the right lobe. The average time required for performing the registration was 12 minutes. A positive correlation was found between the intraoperative accuracy assessment and the obtained TREs. CONCLUSIONS The registration accuracy of the proposed method is adequate for laparoscopic intrahepatic tumor targeting. The presented approach is feasible and fast and may, therefore, not be disruptive to the current surgical work flow.

Assessment of spline-based 2D-3D registration for image-guided spine surgery

2D-3D registration of pre-operative 3D volumetric data with a series of calibrated and undistorted intra-operative 2D projection images has shown great potential in CT-based surgical navigation because it obviates the invasive procedure of the conventional registration methods. In this study, a recently introduced spline-based multi-resolution 2D-3D image registration algorithm has been adapted together with a novel least-squares normalized pattern intensity (LSNPI) similarity measure for image guided minimally invasive spine surgery. A phantom and a cadaver together with their respective ground truths were specially designed to experimentally assess possible factors that may affect the robustness, accuracy, or efficiency of the registration. Our experiments have shown that it is feasible for the assessed 2D-3D registration algorithm to achieve sub-millimeter accuracy in a realistic setup in less than one minute.

Augmented environments for the targeting of hepatic lesions during image-guided robotic liver surgery

BACKGROUND Stereotactic navigation technology can enhance guidance during surgery and enable the precise reproduction of planned surgical strategies. Currently, specific systems (such as the CAS-One system) are available for instrument guidance in open liver surgery. This study aims to evaluate the implementation of such a system for the targeting of hepatic tumors during robotic liver surgery. MATERIAL AND METHODS Optical tracking references were attached to one of the robotic instruments and to the robotic endoscopic camera. After instrument and video calibration and patient-to-image registration, a virtual model of the tracked instrument and the available three-dimensional images of the liver were displayed directly within the robotic console, superimposed onto the endoscopic video image. An additional superimposed targeting viewer allowed for the visualization of the target tumor, relative to the tip of the instrument, for an assessment of the distance between the tumor and the tool for the realization of safe resection margins. RESULTS Two cirrhotic patients underwent robotic navigated atypical hepatic resections for hepatocellular carcinoma. The augmented endoscopic view allowed for the definition of an accurate resection margin around the tumor. The overlay of reconstructed three-dimensional models was also used during parenchymal transection for the identification of vascular and biliary structures. Operative times were 240 min in the first case and 300 min in the second. There were no intraoperative complications. CONCLUSIONS The da Vinci Surgical System provided an excellent platform for image-guided liver surgery with a stable optic and instrumentation. Robotic image guidance might improve the surgeon's orientation during the operation and increase accuracy in tumor resection. Further developments of this technological combination are needed to deal with organ deformation during surgery.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

Multi-modal diffeomorphic demons registration based on point-wise mutual information

In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.

Mesh-based vs. Image-based Statistical Appearance Model of the Human Femur: a Preliminary Comparison Study for the Creation of Finite Element Meshes

Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.

Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

Surgical planning tool for robotically assisted hearing aid implantation

PURPOSE : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. METHODS :    Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. RESULTS :    The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of [Formula: see text] mm. CONCLUSIONS :    Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time.

Endoscopic Image Overlay for the Targeting of Hidden Anatomy in Laparoscopic Visceral Surgery

Limitations associated with the visual information provided to surgeons during laparoscopic surgery increases the difficulty of procedures and thus, reduces clinical indications and increases training time. This work presents a novel augmented reality visualization approach that aims to improve visual data supplied for the targeting of non visible anatomical structures in laparoscopic visceral surgery. The approach aims to facilitate the localisation of hidden structures with minimal damage to surrounding structures and with minimal training requirements. The proposed augmented reality visualization approach incorporates endoscopic images overlaid with virtual 3D models of underlying critical structures in addition to targeting and depth information pertaining to targeted structures. Image overlay was achieved through the implementation of camera calibration techniques and integration of the optically tracked endoscope into an existing image guidance system for liver surgery. The approach was validated in accuracy, clinical integration and targeting experiments. Accuracy of the overlay was found to have a mean value of 3.5 mm ± 1.9 mm and 92.7% of targets within a liver phantom were successfully located laparoscopically by non trained subjects using the approach.

Unifying energy minimization and mutual information maximization for robust 2D/3D registration of X-ray and CT images

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.

Estimation of tool pose based on force-density correlation during robotic drilling

The application of image-guided systems with or without support by surgical robots relies on the accuracy of the navigation process, including patient-to-image registration. The surgeon must carry out the procedure based on the information provided by the navigation system, usually without being able to verify its correctness beyond visual inspection. Misleading surrogate parameters such as the fiducial registration error are often used to describe the success of the registration process, while a lack of methods describing the effects of navigation errors, such as those caused by tracking or calibration, may prevent the application of image guidance in certain accuracy-critical interventions. During minimally invasive mastoidectomy for cochlear implantation, a direct tunnel is drilled from the outside of the mastoid to a target on the cochlea based on registration using landmarks solely on the surface of the skull. Using this methodology, it is impossible to detect if the drill is advancing in the correct direction and that injury of the facial nerve will be avoided. To overcome this problem, a tool localization method based on drilling process information is proposed. The algorithm estimates the pose of a robot-guided surgical tool during a drilling task based on the correlation of the observed axial drilling force and the heterogeneous bone density in the mastoid extracted from 3-D image data. We present here one possible implementation of this method tested on ten tunnels drilled into three human cadaver specimens where an average tool localization accuracy of 0.29 mm was observed.

Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations.

Statistical appearance models have recently been introduced in bone mechanics to investigate bone geometry and mechanical properties in population studies. The establishment of accurate anatomical correspondences is a critical aspect for the construction of reliable models. Depending on the representation of a bone as an image or a mesh, correspondences are detected using image registration or mesh morphing. The objective of this study was to compare image-based and mesh-based statistical appearance models of the femur for finite element (FE) simulations. To this aim, (i) we compared correspondence detection methods on bone surface and in bone volume; (ii) we created an image-based and a mesh-based statistical appearance models from 130 images, which we validated using compactness, representation and generalization, and we analyzed the FE results on 50 recreated bones vs. original bones; (iii) we created 1000 new instances, and we compared the quality of the FE meshes. Results showed that the image-based approach was more accurate in volume correspondence detection and quality of FE meshes, whereas the mesh-based approach was more accurate for surface correspondence detection and model compactness. Based on our results, we recommend the use of image-based statistical appearance models for FE simulations of the femur.

Precise estimation of postoperative cup alignment from single standard X-ray radiograph with gonadal shielding

This paper addresses the problem of estimating postoperative cup alignment from single standard X-ray radiograph with gonadal shielding. The widely used procedure of evaluation of cup orientation following total hip arthroplasty using single standard anteroposterior radiograph is known inaccurate, largely due to the wide variability in individual pelvic position relative to X-ray plate. 2D-3D image registration methods have been introduced to estimate the rigid transformation between a preoperative CT volume and postoperative radiograph(s) for an accurate estimation of the postoperative cup alignment relative to an anatomical reference extracted from the CT data. However, these methods require either multiple radiographs or a radiograph-specific calibration, both of which are not avaiable for most retrospective studies. Furthermore, these methods were only evaluated on X-ray radiograph(s) without gonadal shielding. In this paper, we propose to use a hybrid 2D-3D registration scheme combining an iterative landmark-to-ray registration with a 2D-3D intensity-based registration to estimate the rigid transfromation for a precise estimation of cup alignment. Quantitative and qualitative results evaluated on clinical and cadaveric datasets are given which indicate the validity of our approach.

Novel autoantigens immunogenic in COPD patients

Background Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies. In this study we analyze the complexity of the autoantibody response and reveal the nature of the antigens that are recognized by autoantibodies in COPD patients. Methods An array of 1827 gridded immunogenic peptide clones was established and screened with 17 sera of COPD patients and 60 healthy controls. Protein arrays were evaluated both by visual inspection and a recently developed computer aided image analysis technique. By this computer aided image analysis technique we computed the intensity values for each peptide clone and each serum and calculated the area under the receiver operator characteristics curve (AUC) for each clone and the separation COPD sera versus control sera. Results By visual evaluation we detected 381 peptide clones that reacted with autoantibodies of COPD patients including 17 clones that reacted with more than 60% of the COPD sera and seven clones that reacted with more than 90% of the COPD sera. The comparison of COPD sera and controls by the automated image analysis system identified 212 peptide clones with informative AUC values. By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity. Conclusion The identification of a rather complex humoral immune response in COPD patients supports the idea of COPD as a disease with strong autoimmune features. The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.