Dr. Sureerat Reaungamornrat Profile

Dr. Sureerat Reaungamornrat

Senior Research Scientist at Siemens Healthineers

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Publications (8)

Proceedings Article | 2 March 2018 Presentation + Paper

Inter-scanner variation independent descriptors for constrained diffeomorphic demons registration of retina OCT

S. Reaungamornrat, A. Carass, Y. He, S. Saidha, P. Calabresi, J. Prince

Proceedings Volume 10574, 105741B (2018) https://doi.org/10.1117/12.2293790

KEYWORDS: Optical coherence tomography, Image registration, Denoising, Silver, Retina, Image quality, Image resolution, Speckle, Image segmentation, Rigid registration

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Purpose: OCT offers high in-plane micrometer resolution, enabling studies of neurodegenerative and ocular-disease mechanisms via imaging of the retina at low cost. An important component to such studies is inter-scanner deformable image registration. Image quality of OCT, however, is suboptimal with poor signal-to-noise ratio and through-plane resolution. Geometry of OCT is additionally improperly defined. We developed a diffeomorphic deformable registration method incorporating constraints accommodating the improper geometry and a decentralized-modality-insensitiveneighborhood-descriptors (D-MIND) robust against degradation of OCT image quality and inter-scanner variability. Method: The method, called D-MIND Demons, estimates diffeomorphisms using D-MINDs under constraints on the direction of velocity fields in a MIND-Demons framework. Descriptiveness of D-MINDs with/without denoising was ranked against four other shape/texture-based descriptors. Performance of D-MIND Demons and its variants incorporating other descriptors was compared for cross-scanner, intra- and inter-subject deformable registration using clinical retina OCT data. Result: D-MINDs outperformed other descriptors with the difference in mutual descriptiveness between high-contrast and homogenous regions > 0.2. Among Demons variants, D-MIND-Demons was computationally efficient, demonstrating robustness against OCT image degradation (noise, speckle, intensity-non-uniformity, and poor throughplane resolution) and consistent registration accuracy [(4±4 μm) and (4±6 μm) in cross-scanner intra- and inter-subject registration] regardless of denoising. Conclusions: A promising method for cross-scanner, intra- and inter-subject OCT image registration has been developed for ophthalmological and neurological studies of retinal structures. The approach could assist image segmentation, evaluation of longitudinal disease progression, and patient population analysis, which in turn, facilitate diagnosis and patient-specific treatment.

Proceedings Article | 9 March 2017 Presentation + Paper

Geometric calibration using line fiducials for cone-beam CT with general, non-circular source-detector trajectories

M. Jacobson, M. Ketcha, A. Uneri, J. Goerres, T. De Silva, S. Reaungamornrat, S. Vogt, G. Kleinszig, J. Siewerdsen

Proceedings Volume 10132, 101320I (2017) https://doi.org/10.1117/12.2255724

KEYWORDS: Calibration, Image quality, Computed tomography, 3D modeling, Spatial resolution, Head, Sensors, Bone, Spherical lenses

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Proceedings Article | 3 March 2017 Presentation + Paper

Deformable 3D-2D registration for guiding K-wire placement in pelvic trauma surgery

J. Goerres, M. Jacobson, A. Uneri, T. de Silva, M. Ketcha, S. Reaungamornrat, S. Vogt, G. Kleinszig, J.-P. Wolinsky, G. Osgood, J. Siewerdsen

Proceedings Volume 10135, 101350A (2017) https://doi.org/10.1117/12.2255952

KEYWORDS: X-rays, Calibration, Image registration, Surgery, Imaging systems, Navigation systems, Fluoroscopy, Bone, X-ray computed tomography, Radiography, Digital x-ray imaging

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Proceedings Article | 18 March 2016 Paper

MIND Demons for MR-to-CT deformable image registration in image-guided spine surgery

S. Reaungamornrat, T. De Silva, A. Uneri, J.-P. Wolinsky, A. Khanna, G. Kleinszig, S. Vogt, J. Prince, J. Siewerdsen

Proceedings Volume 9786, 97860H (2016) https://doi.org/10.1117/12.2208621

KEYWORDS: Image registration, Spine, Surgery, Computed tomography, Image-guided intervention, Magnetic resonance imaging, Error analysis, Single mode fibers, Tumors, Optimization (mathematics), Electromagnetic coupling

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Purpose: Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. 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.

Method: The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields 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, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons.

Result: The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

Conclusions: A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.

Proceedings Article | 12 March 2014 Paper

Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations

S. Reaungamornrat, A. Wang, A. Uneri, Y. Otake, Z. Zhao, A. Khanna, J. Siewerdsen

Proceedings Volume 9036, 90360R (2014) https://doi.org/10.1117/12.2043474

KEYWORDS: Image registration, Spine, Surgery, Computed tomography, Image-guided intervention, Tissues, Bone, Neck, Optical spheres, Rigid registration

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Purpose: Deformable registration of preoperative and intraoperative images facilitates accurate localization of target and critical anatomy in image-guided spine surgery. However, conventional deformable registration fails to preserve the morphology of rigid bone anatomy and can impart distortions that confound high-precision intervention. We propose a constrained registration method that preserves rigid morphology while allowing deformation of surrounding soft tissues. Method: The registration method aligns preoperative 3D CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with penalties on rigid body motion imposed according to a simple intensity threshold. The penalties enforced 3 properties of a rigid transformation – namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments (involving phantoms, an ovine spine, and a human cadaver) as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. Result: FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. Conclusions: A promising method for deformable registration in CBCT-guided spine surgery has been identified incorporating a constrained FFD to preserve bone morphology. The approach overcomes distortions intrinsic to unconstrained FFD and could better facilitate high-precision image-guided spine surgery.

Proceedings Article | 8 March 2013 Paper

A gaussian mixture + demons deformable registration method for cone-beam CT-guided robotic transoral base-of-tongue surgery

S. Reaungamornrat, W. Liu, S. Schafer, Y. Otake, S. Nithiananthan, A. Uneri, J. Richmon, J. Sorger, J. Siewerdsen, R. Taylor

Proceedings Volume 8671, 86710J (2013) https://doi.org/10.1117/12.2007937

KEYWORDS: Image registration, Image segmentation, Tongue, Computed tomography, Clouds, Magnetic resonance imaging, Rigid registration, Surgery, Video, Mouth

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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 Demons 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 2.2, 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 Demons 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.

Proceedings Article | 17 February 2012 Paper

Tracker-on-C for cone-beam CT-guided surgery: evaluation of geometric accuracy and clinical applications

S. Reaungamornrat, Y. Otake, A. Uneri, S. Schafer, D. Mirota, S. Nithiananthan, J. W. Stayman, A. J. Khanna, D. Reh, G. Gallia, R. Taylor, J. Siewerdsen

Proceedings Volume 8316, 831609 (2012) https://doi.org/10.1117/12.911454

KEYWORDS: Video, Fluoroscopy, Surgery, X-rays, Optical tracking, Visualization, Spine, Radiography, Image registration, X-ray computed tomography

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Conventional surgical tracking configurations carry a variety of limitations in line-of-sight, geometric accuracy, and mismatch with the surgeon's perspective (for video augmentation). With increasing utilization of mobile C-arms, particularly those allowing cone-beam CT (CBCT), there is opportunity to better integrate surgical trackers at bedside to address such limitations. This paper describes a tracker configuration in which the tracker is mounted directly on the Carm. To maintain registration within a dynamic coordinate system, a reference marker visible across the full C-arm rotation is implemented, and the "Tracker-on-C" configuration is shown to provide improved target registration error (TRE) over a conventional in-room setup - (0.9±0.4) mm vs (1.9±0.7) mm, respectively. The system also can generate digitally reconstructed radiographs (DRRs) from the perspective of a tracked tool ("x-ray flashlight"), the tracker, or the C-arm ("virtual fluoroscopy"), with geometric accuracy in virtual fluoroscopy of (0.4±0.2) mm. Using a video-based tracker, planning data and DRRs can be superimposed on the video scene from a natural perspective over the surgical field, with geometric accuracy (0.8±0.3) pixels for planning data overlay and (0.6±0.4) pixels for DRR overlay across all C-arm angles. The field-of-view of fluoroscopy or CBCT can also be overlaid on real-time video ("Virtual Field Light") to assist C-arm positioning. The fixed transformation between the x-ray image and tracker facilitated quick, accurate intraoperative registration. The workflow and precision associated with a variety of realistic surgical tasks were significantly improved using the Tracker-on-C - for example, nearly a factor of 2 reduction in time required for C-arm positioning, reduction or elimination of dose in "hunting" for a specific fluoroscopic view, and confident placement of the x-ray FOV on the surgical target. The proposed configuration streamlines the integration of C-arm CBCT with realtime tracking and demonstrated utility in a spectrum of image-guided interventions (e.g., spine surgery) benefiting from improved accuracy, enhanced visualization, and reduced radiation exposure.

Proceedings Article | 2 March 2011 Paper

Architecture of a high-performance surgical guidance system based on C-arm cone-beam CT: software platform for technical integration and clinical translation

Ali Uneri, Sebastian Schafer, Daniel Mirota, Sajendra Nithiananthan, Yoshito Otake, Sureerat Reaungamornrat, Jongheun Yoo, J. Webster Stayman, Douglas Reh, Gary Gallia, A. Jay Khanna, Gregory Hager, Russell Taylor, Gerhard Kleinszig, Jeffrey Siewerdsen

Proceedings Volume 7964, 796422 (2011) https://doi.org/10.1117/12.878191

KEYWORDS: Surgery, Video, Image registration, Computer architecture, Visualization, Fluoroscopy, Endoscopy, 3D image processing, Imaging systems, Computed tomography

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Intraoperative imaging modalities are becoming more prevalent in recent years, and the need for integration of these modalities with surgical guidance is rising, creating new possibilities as well as challenges. In the context of such emerging technologies and new clinical applications, a software architecture for cone-beam CT (CBCT) guided surgery has been developed with emphasis on binding open-source surgical navigation libraries and integrating intraoperative CBCT with novel, application-specific registration and guidance technologies. The architecture design is focused on accelerating translation of task-specific technical development in a wide range of applications, including orthopaedic, head-and-neck, and thoracic surgeries. The surgical guidance system is interfaced with a prototype mobile C-arm for high-quality CBCT and through a modular software architecture, integration of different tools and devices consistent with surgical workflow in each of these applications is realized. Specific modules are developed according to the surgical task, such as: 3D-3D rigid or deformable registration of preoperative images, surgical planning data, and up-to-date CBCT images; 3D-2D registration of planning and image data in real-time fluoroscopy and/or digitally reconstructed radiographs (DRRs); compatibility with infrared, electromagnetic, and video-based trackers used individually or in hybrid arrangements; augmented overlay of image and planning data in endoscopic or in-room video; real-time "virtual fluoroscopy" computed from GPU-accelerated DRRs; and multi-modality image display. The platform aims to minimize offline data processing by exposing quantitative tools that analyze and communicate factors of geometric precision. The system was translated to preclinical phantom and cadaver studies for assessment of fiducial (FRE) and target registration error (TRE) showing sub-mm accuracy in targeting and video overlay within intraoperative CBCT. The work culminates in the development of a CBCT guidance system (reported here for the first time) that leverages the technical developments in Carm CBCT and associated technologies for realizing a high-performance system for translation to clinical studies.

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