Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration

Otake, Yoshito; Schäfer, Sebastian; Stayman, J. Webster; Zbijewski, Wojciech; Kleinszig, Gerhard; Graumann, Rainer; Khanna, A. Jay; Siewerdsen, J. H.

doi:10.1117/12.911308

Cited by 9 publications

(13 citation statements)

References 9 publications

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“…For these interventions, a desirable TRE ≤ 2 mm(i.e., comparable to current intraoperative navigation systems) was achieve using the proposed method of 2D–3D registration. Nominal 2D–3D registration parameters governing the registration process were derived from previous work including initial conditions for a robust initialization using a global search [30]. …”

Section: Discussionmentioning

confidence: 99%

2D–3D radiograph to cone-beam computed tomography (CBCT) registration for C-arm image-guided robotic surgery

et al. 2014

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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 Δθ : ~ 30°. The two C-arm systems provided TREmean ≤ 2.5 mm and TREmean ≤ 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.

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Section: Discussionmentioning

confidence: 99%

2D–3D radiograph to cone-beam computed tomography (CBCT) registration for C-arm image-guided robotic surgery

et al. 2014

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“…The Free-Form method yields a deterministic solution and was therefore run once for each case, but the Known-Model method involves an optimizer with a stochastic component 34 and was therefore run 20 times, and the results were averaged. As shown in Fig.…”

Section: Iiia Comparison Of Known-model and Free-form Automatic Regmentioning

confidence: 99%

“…The registration approach followed Otake et al 34 Based on an approximate estimate of I T M and the geometric calibration of the C-arm, digitally reconstructed radiographs (DRRs) of the attenuation volume were generated. The I T M that maximizes a similarity metric (viz., gradient information 46 ) between the DRRs and the real projection images was found with a nongradient stochastic optimizer [viz., the covariance matrix adaptation evolution strategy, CMA-ES (Ref.…”

Section: Iic Automatic Image-to-world Registration Using a Known Rementioning

confidence: 99%

“…Volumetric reconstruction is performed using a variation of the Feldkamp-Davis-Kress (FDK) algorithm, 33 implemented on a graphics processing unit (GPU), providing 512 3 volumes within ∼10-20 s. Nominal volumetric FOV is 15 × 15 × 15 cm 3 at 0.3 mm voxel size. Potential applications include spine surgery, 34,35 head and neck surgery, 3,36,37 thoracic surgery, 38 brachytherapy, 39 and abdominal interventions 40 -motivating the studies below of automatic image-to-world registration for a broad variety of anatomical sites.…”

Section: Iia Experimental Setupmentioning

confidence: 99%

“…The image-to-model transformation ( I T M ) was estimated by registering the 3D attenuation volume of the ARM associated with M to the projection image coordinate system (denoted I) using an intensity-based 3D/2D registration in multiple projection views. The registration approach followed Otake et al 34 Based on an approximate estimate of I T M and the geometric calibration of the C-arm, digitally reconstructed radiographs (DRRs) of the attenuation volume were generated. The I T M that maximizes a similarity metric (viz., gradient information 46 ) between the DRRs and the real projection images was found with a nongradient stochastic optimizer [viz., the covariance matrix adaptation evolution strategy, CMA-ES (Ref.…”

Section: Iic Automatic Image-to-world Registration Using a Known Rementioning

confidence: 99%

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Robust methods for automatic image‐to‐world registration in cone‐beam CT interventional guidance

et al. 2012

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Automatic image-to-world registration methods demonstrate the potential for improved accuracy, reproducibility, and workflow in CBCT-guided procedures. A Free-Form method was shown to exhibit robustness against anatomical site, with comparable or improved TRE compared to manual registration. It was also comparable or superior in performance to a Known-Model method in which the ARM configuration is specified as a predefined tool, thereby allowing configuration of fiducials on the fly or attachment to the patient.

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Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Otake¹,

Schäfer²,

Stayman³

et al. 2012

Phys. Med. Biol.

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Surgical targeting of the incorrect vertebral level (“wrong-level” surgery) is among the more common wrong-site surgical errors, attributed primarily to a lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. 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 (viz., CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved 10 patient CT datasets from which 50,000 simulated fluoroscopic images were generated from C-arm poses selected to approximate 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 (viz., mPD < 5mm). Simulation studies showed a success rate of 99.998% (1 failure in 50,000 trials) and computation time of 4.7 sec 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 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 surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.

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Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration

Cited by 9 publications

References 9 publications

2D–3D radiograph to cone-beam computed tomography (CBCT) registration for C-arm image-guided robotic surgery

2D–3D radiograph to cone-beam computed tomography (CBCT) registration for C-arm image-guided robotic surgery

Robust methods for automatic image‐to‐world registration in cone‐beam CT interventional guidance

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

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