Target Anatomy

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Jeffrey H. Siewerdsen - One of the best experts on this subject based on the ideXlab platform.

  • mind demons symmetric diffeomorphic deformable registration of mr and ct for image guided spine surgery
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Intraoperative localization of Target Anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean Target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

  • th cd 206 10 clinical application of the mind demons algorithm for symmetric diffeomorphic deformable mr to ct image registration in spinal interventions
    Medical Physics, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Purpose. Accurate intraoperative localization of Target Anatomy (e.g., tumors and vertebral levels) and critical structures (e.g., nerves and vessels) is essential to safe, effective surgery. Preoperative CT/MR images can be used to identify such vital Anatomy in intraoperative images through multimodality deformable registration. We proposed a deformable registration method to align preoperative MR to intraoperative CT using modality-independent neighborhood descriptors (MIND) and a Huber metric for robust registration. Methods. The method, called MIND Demons, estimates time-dependent diffeomorphisms between two images by optimizing a constrained symmetric energy functional incorporating priors on smoothness, conservation of momentum, geodesic length, invertibility, and closure under composition. Alternating optimization is performed using a regularized Gauss-Newton (GN) method in a multiresolution scheme. Performance was measured in phantom experiments emulating image-guided spine-surgery and in a retrospective clinical study (N=15 patients) evaluated in terms of Target registration error (TRE) and diffeomorphic properties of the resulting deformations. Results. The MIND Demons method outperformed free-form deformation (FFD) methods and conventional Demons in phantom experiments (median TRE = 1.5 mm, compared to 10.2 mm for mutual information (MI) based FFD, 2.6 mm for local-MI FFD, and 5.1 mm for normalized-MI Demons). The resulting deformations resolved realistic deformation in clinical data with sub-voxel TRE (<2 mm) in cases of cervical, thoracic, and lumbar spine and preserved topology with sub-voxel invertibility (0.004 mm) and positive-determinant nonsingular spatial-Jacobians. Conclusion. A modality-independent deformable registration method has been developed, incorporating the constrained symmetric energy functional and the Huber metric to yield stable and accurate registration, with computational efficiency improved using the GN optimization. The approach yielded registration accuracy suitable to application in image-guided spine-surgery across each region of the spine under realistic modes of deformation. S. Vogt and G. Kleinszig is with Siemens Healthcare XP.

  • automatic localization of vertebral levels in x ray fluoroscopy using 3d 2d registration a tool to reduce wrong site surgery
    Physics in Medicine and Biology, 2012
    Co-Authors: Yoshito Otake, J. W. Stayman, A. J. Khanna, Gerhard Kleinszig, Sebastian Schafer, W Zbijewski, Rainer Graumann, Jeffrey H. Siewerdsen
    Abstract:

    Surgical Targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD <5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize Target Anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site 0031-9155/12/175485+24$33.00 © 2012 Institute of Physics and Engineering in Medicine Printed in the UK & the USA 5485

  • Model-Based Tomographic Reconstruction of Objects Containing Known Components
    IEEE Transactions on Medical Imaging, 2012
    Co-Authors: Webster J. Stayman, Yoshito Otake, Jerry L. Prince, Jay A. Khanna, Jeffrey H. Siewerdsen
    Abstract:

    The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the Target Anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the Anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.

Russell H Taylor - One of the best experts on this subject based on the ideXlab platform.

  • ASMUS/PIPPI@MICCAI - Dual-Robotic Ultrasound System for In Vivo Prostate Tomography
    Medical Ultrasound and Preterm Perinatal and Paediatric Image Analysis, 2020
    Co-Authors: Kevin M. Gilboy, Bradford J. Wood, Emad M. Boctor, Russell H Taylor
    Abstract:

    Ultrasound computed tomography (USCT) offers quantitative anatomical tissue characterization for cancer detection. While most research and commercial development has focused on submerging Target Anatomy in a transducer-lined cylindrical water tank, this is not practical for imaging deep Anatomy and an alternative approach using aligned abdominal and endoluminal ultrasound probes is required. This work outlines and validates a clinical workflow and real-time motion framework for a novel dual-robotic approach specific to in vivo prostate imaging: one arm wielding a linear abdominal probe, the other wielding a linear transrectal ultrasound (TRUS) probe. After calibration, the robotic system works to keep the abdominal probe collinear with the physician-rotated TRUS probe using a convex contour tracking scheme, while also enforcing its gentle contact with the patient’s pubic region to capture the ultrasound slices needed for limited-angle tomographic reconstruction. The repeatable and accurate robotic system presents feasibility for prostate USCT and future malignancy diagnosis and staging in vivo.

  • Endoscopic navigation in the clinic: registration in the absence of preoperative imaging
    International Journal of Computer Assisted Radiology and Surgery, 2019
    Co-Authors: Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H Taylor
    Abstract:

    Purpose Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference preoperative image, like a computed tomography (CT) scan, to provide structural context to the clinician. The aim of this work is to provide structural context during clinical exploration without requiring additional CT acquisition. Methods We present a method for registration during clinical endoscopy in the absence of CT scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm that uses these shape statistics along with dense point clouds from video, we simultaneously achieve two goals: (1) register the statistically mean shape of the Target Anatomy with the video point cloud, and (2) estimate patient shape by deforming the mean shape to fit the video point cloud. Finally, we use statistical tests to assign confidence to the computed registration. Results We are able to achieve submillimeter errors in registrations and patient shape reconstructions using simulated data. We establish and evaluate the confidence criteria for our registrations using simulated data. Finally, we evaluate our registration method on in vivo clinical data and assign confidence to these registrations using the criteria established in simulation. All registrations that are not rejected by our criteria produce submillimeter residual errors. Conclusion Our deformable registration method can produce submillimeter registrations and reconstructions as well as statistical scores that can be used to assign confidence to the registrations.

  • Endoscopic navigation in the clinic: registration in the absence of preoperative imaging.
    International journal of computer assisted radiology and surgery, 2019
    Co-Authors: Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H Taylor
    Abstract:

    Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference preoperative image, like a computed tomography (CT) scan, to provide structural context to the clinician. The aim of this work is to provide structural context during clinical exploration without requiring additional CT acquisition. We present a method for registration during clinical endoscopy in the absence of CT scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm that uses these shape statistics along with dense point clouds from video, we simultaneously achieve two goals: (1) register the statistically mean shape of the Target Anatomy with the video point cloud, and (2) estimate patient shape by deforming the mean shape to fit the video point cloud. Finally, we use statistical tests to assign confidence to the computed registration. We are able to achieve submillimeter errors in registrations and patient shape reconstructions using simulated data. We establish and evaluate the confidence criteria for our registrations using simulated data. Finally, we evaluate our registration method on in vivo clinical data and assign confidence to these registrations using the criteria established in simulation. All registrations that are not rejected by our criteria produce submillimeter residual errors. Our deformable registration method can produce submillimeter registrations and reconstructions as well as statistical scores that can be used to assign confidence to the registrations.

  • 2d 3d radiograph to cone beam computed tomography cbct registration for c arm image guided robotic surgery
    Computer Assisted Radiology and Surgery, 2015
    Co-Authors: Wen Pei Liu, Yoshito Otake, Mahdi Azizian, Oliver J Wagner, Jonathan M Sorger, Mehran Armand, Russell H Taylor
    Abstract:

    Purpose C-arm radiographs are commonly used for intraoperative image guidance in surgical interventions. Fluoroscopy is a cost-effective real-time modality, although image quality can vary greatly depending on the Target Anatomy. Cone-beam computed tomography (CBCT) scans are sometimes available, so 2D–3D registration is needed for intra-procedural guidance. C-arm radiographs were registered to CBCT scans and used for 3D localization of peritumor fiducials during a minimally invasive thoracic intervention with a da Vinci Si robot.

  • 2D–3D radiograph to cone-beam computed tomography (CBCT) registration for C-arm image-guided robotic surgery
    International Journal of Computer Assisted Radiology and Surgery, 2015
    Co-Authors: Wen Pei Liu, Yoshito Otake, Mahdi Azizian, Oliver J Wagner, Jonathan M Sorger, Mehran Armand, Russell H Taylor
    Abstract:

    Purpose C-arm radiographs are commonly used for intraoperative image guidance in surgical interventions. Fluoroscopy is a cost-effective real-time modality, although image quality can vary greatly depending on the Target Anatomy. Cone-beam computed tomography (CBCT) scans are sometimes available, so 2D–3D registration is needed for intra-procedural guidance. C-arm radiographs were registered to CBCT scans and used for 3D localization of peritumor fiducials during a minimally invasive thoracic intervention with a da Vinci Si robot. Methods Intensity-based 2D–3D registration of intraoperative radiographs to CBCT was performed. The feasible range of X-ray projections achievable by a C-arm positioned around a da Vinci Si surgical robot, configured for robotic wedge resection, was determined using phantom models. Experiments were conducted on synthetic phantoms and animals imaged with an OEC 9600 and a Siemens Artis zeego, representing the spectrum of different C-arm systems currently available for clinical use. Results The image guidance workflow was feasible using either an optically tracked OEC 9600 or a Siemens Artis zeego C-arm, resulting in an angular difference of $$\Delta \theta : \sim 30^{\circ }$$ Δ θ : ∼ 30 ∘ . The two C-arm systems provided $$\hbox {TRE}_\mathrm{mean} \le 2.5 \hbox { mm}$$ TRE mean ≤ 2.5 mm and $$\hbox {TRE}_\mathrm{mean} \le 2.0 \hbox { mm}$$ TRE mean ≤ 2.0 mm , respectively (i.e., comparable to standard clinical intraoperative navigation systems). Conclusions C-arm 3D localization from dual 2D–3D registered radiographs was feasible and applicable for intraoperative image guidance during da Vinci robotic thoracic interventions using the proposed workflow. Tissue deformation and in vivo experiments are required before clinical evaluation of this system.

Nassir Navab - One of the best experts on this subject based on the ideXlab platform.

  • 3D ultrasound registration-based visual servoing for neurosurgical navigation.
    International journal of computer assisted radiology and surgery, 2017
    Co-Authors: Oliver Zettinig, Benjamin Frisch, Salvatore Virga, Marco Esposito, Anna Rienmüller, Bernhard Meyer, Christoph Hennersperger, Yu-mi Ryang, Nassir Navab
    Abstract:

    Purpose We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of Target Anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions.

  • ICRA - Toward real-time 3D ultrasound registration-based visual servoing for interventional navigation
    2016 IEEE International Conference on Robotics and Automation (ICRA), 2016
    Co-Authors: Oliver Zettinig, Benjamin Frisch, Marco Esposito, Bernhard Fuerst, Risto Kojcev, Mehrdad Salehi, Wolfgang Wein, Julia Rackerseder, Edoardo Sinibaldi, Nassir Navab
    Abstract:

    While intraoperative imaging is commonly used to guide surgical interventions, automatic robotic support for image-guided navigation has not yet been established in clinical routine. In this paper, we propose a novel visual servoing framework that combines, for the first time, full image-based 3D ultrasound registration with a real-time servo-control scheme. Paired with multi-modal fusion to a pre-interventional plan such as an annotated needle insertion path, it thus allows tracking a Target Anatomy, continuously updating the plan as the Target moves, and keeping a needle guide aligned for accurate manual insertion. The presented system includes a motorized 3D ultrasound transducer mounted on a force-controlled robot and a GPU-based image processing toolkit. The tracking accuracy of our framework is validated on a geometric agar/gelatin phantom using a second robot, achieving positioning errors of on average 0.42–0.44 mm. With compounding and registration runtimes of up to total around 550 ms, real-time performance comes into reach. We also present initial results on a spine phantom, demonstrating the feasibility of our system for lumbar spine injections.

  • MICCAI (2) - Robust Segmentation of Various Anatomies in 3D Ultrasound Using Hough Forests and Learned Data Representations
    Lecture Notes in Computer Science, 2015
    Co-Authors: Fausto Milletari, Christoph Hennersperger, Seyed-ahmad Ahmadi, Christine Kroll, Federico Tombari, Amit Shah, Annika Plate, Kai Boetzel, Nassir Navab
    Abstract:

    3D ultrasound segmentation is a challenging task due to image artefacts, low signal-to-noise ratio and lack of contrast at anatomical boundaries. Current solutions usually rely on complex, Anatomy-specific regularization methods to improve segmentation accuracy. In this work, we propose a highly adaptive learning-based method for fully automatic segmentation of ultrasound volumes. During training, Anatomy-specific features are obtained through a sparse auto-encoder. The extracted features are employed in a Hough Forest based framework to retrieve the position of the Target Anatomy and its segmentation contour. The resulting method is fully automatic, i.e. it does not require any human interaction, and can robustly and automatically adapt to different anatomies yet enforcing appearance and shape constraints. We demonstrate the performance of the method for three different applications: segmentation of midbrain, left ventricle of the heart and prostate.

  • Probabilistic Sparse Matching for Robust 3D/3D Fusion in Minimally Invasive Surgery
    IEEE Transactions on Medical Imaging, 2015
    Co-Authors: Dominik Neumann, Nassir Navab, Saša Grbić, Matthias John, Joachim Hornegger, Razvan Ionasec
    Abstract:

    Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected Anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac Anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor Anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both Target Anatomy and anchor Anatomy alignment errors.

  • Predicate-Based Focus-and-Context Visualization for 3D Ultrasound.
    IEEE transactions on visualization and computer graphics, 2014
    Co-Authors: Christian Schulte Zu Berge, Maximilian Baust, Ankur Kapoor, Nassir Navab
    Abstract:

    Direct volume visualization techniques offer powerful insight into volumetric medical images and are part of the clinical routine for many applications. Up to now, however, their use is mostly limited to tomographic imaging modalities such as CT or MRI. With very few exceptions, such as fetal ultrasound, classic volume rendering using one-dimensional intensity-based transfer functions fails to yield satisfying results in case of ultrasound volumes. This is particularly due its gradient-like nature, a high amount of noise and speckle, and the fact that individual tissue types are rather characterized by a similar texture than by similar intensity values. Therefore, clinicians still prefer to look at 2D slices extracted from the ultrasound volume. In this work, we present an entirely novel approach to the classification and compositing stage of the volume rendering pipeline, specifically designed for use with ultrasonic images. We introduce point predicates as a generic formulation for integrating the evaluation of not only low-level information like local intensity or gradient, but also of high-level information, such as non-local image features or even anatomical models. Thus, we can successfully filter clinically relevant from non-relevant information. In order to effectively reduce the potentially high dimensionality of the predicate configuration space, we propose the predicate histogram as an intuitive user interface. This is augmented by a scribble technique to provide a comfortable metaphor for selecting predicates of interest. Assigning importance factors to the predicates allows for focus-and-context visualization that ensures to always show important (focus) regions of the data while maintaining as much context information as possible. Our method naturally integrates into standard ray casting algorithms and yields superior results in comparison to traditional methods in terms of visualizing a specific Target Anatomy in ultrasound volumes.

Jerry L. Prince - One of the best experts on this subject based on the ideXlab platform.

  • mind demons symmetric diffeomorphic deformable registration of mr and ct for image guided spine surgery
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Intraoperative localization of Target Anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean Target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

  • th cd 206 10 clinical application of the mind demons algorithm for symmetric diffeomorphic deformable mr to ct image registration in spinal interventions
    Medical Physics, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Purpose. Accurate intraoperative localization of Target Anatomy (e.g., tumors and vertebral levels) and critical structures (e.g., nerves and vessels) is essential to safe, effective surgery. Preoperative CT/MR images can be used to identify such vital Anatomy in intraoperative images through multimodality deformable registration. We proposed a deformable registration method to align preoperative MR to intraoperative CT using modality-independent neighborhood descriptors (MIND) and a Huber metric for robust registration. Methods. The method, called MIND Demons, estimates time-dependent diffeomorphisms between two images by optimizing a constrained symmetric energy functional incorporating priors on smoothness, conservation of momentum, geodesic length, invertibility, and closure under composition. Alternating optimization is performed using a regularized Gauss-Newton (GN) method in a multiresolution scheme. Performance was measured in phantom experiments emulating image-guided spine-surgery and in a retrospective clinical study (N=15 patients) evaluated in terms of Target registration error (TRE) and diffeomorphic properties of the resulting deformations. Results. The MIND Demons method outperformed free-form deformation (FFD) methods and conventional Demons in phantom experiments (median TRE = 1.5 mm, compared to 10.2 mm for mutual information (MI) based FFD, 2.6 mm for local-MI FFD, and 5.1 mm for normalized-MI Demons). The resulting deformations resolved realistic deformation in clinical data with sub-voxel TRE (<2 mm) in cases of cervical, thoracic, and lumbar spine and preserved topology with sub-voxel invertibility (0.004 mm) and positive-determinant nonsingular spatial-Jacobians. Conclusion. A modality-independent deformable registration method has been developed, incorporating the constrained symmetric energy functional and the Huber metric to yield stable and accurate registration, with computational efficiency improved using the GN optimization. The approach yielded registration accuracy suitable to application in image-guided spine-surgery across each region of the spine under realistic modes of deformation. S. Vogt and G. Kleinszig is with Siemens Healthcare XP.

  • Model-Based Tomographic Reconstruction of Objects Containing Known Components
    IEEE Transactions on Medical Imaging, 2012
    Co-Authors: Webster J. Stayman, Yoshito Otake, Jerry L. Prince, Jay A. Khanna, Jeffrey H. Siewerdsen
    Abstract:

    The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the Target Anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the Anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.

  • MICCAI (1) - Statistical atlases of bone Anatomy: construction, iterative improvement and validation
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2007
    Co-Authors: Gouthami Chintalapani, Lotta M. Ellingsen, Ofri Sadowsky, Jerry L. Prince, Russell H Taylor
    Abstract:

    We present an iterative bootstrapping framework to create and analyze statistical atlases of bony Anatomy such as the human pelvis from a large collection of CT data sets. We create an initial tetrahedral mesh representation of the Target Anatomy and use deformable intensity-based registration to create an initial atlas. This atlas is used as prior information to assist in deformable registration/segmentation of our subject image data sets, and the process is iterated several times to remove any bias from the initial choice of template subject and to improve the stability and consistency of mean shape and variational modes. We also present a framework to validate the statistical models. Using this method, we have created a statistical atlas of full pelvis Anatomy with 110 healthy patient CT scans. Our analysis shows that any given pelvis shape can be approximated up to an average accuracy of 1.5036 mm using the first 15 principal modes of variation. Although a particular intensity-based deformable registration algorithm was used to produce these results, we believe that the basic method may be adapted readily for use with any registration method with broadly similar characteristics.

S. Reaungamornrat - One of the best experts on this subject based on the ideXlab platform.

  • mind demons symmetric diffeomorphic deformable registration of mr and ct for image guided spine surgery
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Intraoperative localization of Target Anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean Target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

  • th cd 206 10 clinical application of the mind demons algorithm for symmetric diffeomorphic deformable mr to ct image registration in spinal interventions
    Medical Physics, 2016
    Co-Authors: S. Reaungamornrat, A. Uneri, A. J. Khanna, Jerry L. Prince, Tharindu De Silva, Sebastian Vogt, Gerhard Kleinszig, Jean Paul Wolinsky, Jeffrey H. Siewerdsen
    Abstract:

    Purpose. Accurate intraoperative localization of Target Anatomy (e.g., tumors and vertebral levels) and critical structures (e.g., nerves and vessels) is essential to safe, effective surgery. Preoperative CT/MR images can be used to identify such vital Anatomy in intraoperative images through multimodality deformable registration. We proposed a deformable registration method to align preoperative MR to intraoperative CT using modality-independent neighborhood descriptors (MIND) and a Huber metric for robust registration. Methods. The method, called MIND Demons, estimates time-dependent diffeomorphisms between two images by optimizing a constrained symmetric energy functional incorporating priors on smoothness, conservation of momentum, geodesic length, invertibility, and closure under composition. Alternating optimization is performed using a regularized Gauss-Newton (GN) method in a multiresolution scheme. Performance was measured in phantom experiments emulating image-guided spine-surgery and in a retrospective clinical study (N=15 patients) evaluated in terms of Target registration error (TRE) and diffeomorphic properties of the resulting deformations. Results. The MIND Demons method outperformed free-form deformation (FFD) methods and conventional Demons in phantom experiments (median TRE = 1.5 mm, compared to 10.2 mm for mutual information (MI) based FFD, 2.6 mm for local-MI FFD, and 5.1 mm for normalized-MI Demons). The resulting deformations resolved realistic deformation in clinical data with sub-voxel TRE (<2 mm) in cases of cervical, thoracic, and lumbar spine and preserved topology with sub-voxel invertibility (0.004 mm) and positive-determinant nonsingular spatial-Jacobians. Conclusion. A modality-independent deformable registration method has been developed, incorporating the constrained symmetric energy functional and the Huber metric to yield stable and accurate registration, with computational efficiency improved using the GN optimization. The approach yielded registration accuracy suitable to application in image-guided spine-surgery across each region of the spine under realistic modes of deformation. S. Vogt and G. Kleinszig is with Siemens Healthcare XP.

  • An on-board surgical tracking and video augmentation system for C-arm image guidance
    International Journal of Computer Assisted Radiology and Surgery, 2012
    Co-Authors: S. Reaungamornrat, Y. Otake, A. Uneri, S. Schafer, D. J. Mirota, S. Nithiananthan, J. W. Stayman, G. Kleinszig, A. J. Khanna, R. H. Taylor
    Abstract:

    Purpose Conventional tracker configurations for surgical navigation carry a variety of limitations, including limited geometric accuracy, line-of-sight obstruction, and mismatch of the view angle with the surgeon’s-eye view. This paper presents the development and characterization of a novel tracker configuration (referred to as “Tracker-on-C”) intended to address such limitations by incorporating the tracker directly on the gantry of a mobile C-arm for fluoroscopy and cone-beam CT (CBCT). Methods A video-based tracker (MicronTracker, Claron Technology Inc., Toronto, ON, Canada) was mounted on the gantry of a prototype mobile isocentric C-arm next to the flat-panel detector. To maintain registration within a dynamically moving reference frame (due to rotation of the C-arm), a reference marker consisting of 6 faces (referred to as a “hex-face marker”) was developed to give visibility across the full range of C-arm rotation. Three primary functionalities were investigated: surgical tracking, generation of digitally reconstructed radiographs (DRRs) from the perspective of a tracked tool or the current C-arm angle, and augmentation of the tracker video scene with image, DRR, and planning data. Target registration error (TRE) was measured in comparison with the same tracker implemented in a conventional in-room configuration. Graphics processing unit (GPU)-accelerated DRRs were generated in real time as an assistant to C-arm positioning (i.e., positioning the C-arm such that Target Anatomy is in the field-of-view (FOV)), radiographic search (i.e., a virtual X-ray projection preview of Target Anatomy without X-ray exposure), and localization (i.e., visualizing the location of the surgical Target or planning data). Video augmentation included superimposing tracker data, the X-ray FOV, DRRs, planning data, preoperative images, and/or intraoperative CBCT onto the video scene. Geometric accuracy was quantitatively evaluated in each case, and qualitative assessment of clinical feasibility was analyzed by an experienced and fellowship-trained orthopedic spine surgeon within a clinically realistic surgical setup of the Tracker-on-C. Results The Tracker-on-C configuration demonstrated improved TRE (0.87 ± 0.25) mm in comparison with a conventional in-room tracker setup (1.92 ± 0.71) mm ( p