C-arm Tracking and Reconstruction Without an External Tracker

Jain, Ameet; Fichtinger, Gabor

doi:10.1007/11866565_61

Cited by 23 publications

(33 citation statements)

References 6 publications

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“…It has been shown that higher accuracy in C-arm pose estimation will result in more accurate matching and vice versa [1,8]. Therefore, the matching and the motion compensation problems can be solved iteratively in a loop in which reconstructed seed positions can be used to improve on C-arm pose estimation.…”

Section: Motion Compensation Algorithmmentioning

confidence: 99%

“…It is known that this problem can be solved in 3D up to a scale [1,8], which means that the reconstructed volume can arbitrary shrink or expand (see Fig 1(c)). In [8], the distance between two points on a radio-opaque fiducial was used to find the scaling factor. However, we do not use an external fiducial here.…”

Section: Motion Compensation Algorithmmentioning

confidence: 99%

“…Auxiliary trackers are expensive and further complicate the surgery as they need calibration steps, line of sight and space in the operating room. It has been suggested that implanted seeds can be used for pose estimation [1,8]. However, to be computationally feasible, they need an initial estimation of the pose, conveniently by using external trackers and fiducials.…”

Section: Introductionmentioning

confidence: 99%

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Prostate Brachytherapy Seed Reconstruction Using C-Arm Rotation Measurement and Motion Compensation

Dehghan

Lee

Moradi

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. During prostate brachytherapy, C-arm flouroscopy images are used for a qualitative assessment of the procedure. Three dimensional reconstruction of the implanted seeds can be used for intraoperative dosimetry and quantitative assessment. Accurate C-arm pose estimation is necessary for 3D localization of the seeds. We propose to measure the C-arm rotation angles and computationally compensate the inevitable C-arm translational motion to estimate the pose. We compensate the oscillation, sagging and wheel motion of the C-arm using a three-level optimization algorithm, without which the reconstruction can fail. We validated our approach on simulated and 10 data sets from 5 patients and gained on average 99.1% success rate, 0.33 mm projection error and computation time of less than one minute per patient, which are clinically excellent results.

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Section: Motion Compensation Algorithmmentioning

confidence: 99%

Section: Motion Compensation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prostate Brachytherapy Seed Reconstruction Using C-Arm Rotation Measurement and Motion Compensation

Dehghan

Lee

Moradi

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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“…With our method, a less accurate estimation of the pose may suffice. For example in [4], starting from an initial guess, pose was further estimated iteratively using the current 3D reconstruction, yet the seed matching remained susceptible to pose errors. Applying our method to [4] promises a clinically viable solution without using external tracker or encoder on the C-arm.…”

Section: Phantom Experimentsmentioning

confidence: 99%

“…For example in [4], starting from an initial guess, pose was further estimated iteratively using the current 3D reconstruction, yet the seed matching remained susceptible to pose errors. Applying our method to [4] promises a clinically viable solution without using external tracker or encoder on the C-arm. We are also extending the method to reconstructing overlapping seeds that are occluded in one or more X-ray images [5,6].…”

Section: Phantom Experimentsmentioning

confidence: 99%

Toward Optimal Matching for 3D Reconstruction of Brachytherapy Seeds

Labat¹,

Jain

Fichtinger³

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. X-ray C-arm fluoroscopy is a natural choice for intra-operative seed localization in prostate brachytherapy. Resolving the correspondence of seeds in the projection images can be modeled as an assignment problem that is NP-hard. Our approach rests on the practical observation that the optimal solution has almost zero cost if the pose of the C-arm is known accurately. This allowed us to to derive an equivalent problem of reduced dimensionality that, with linear programming, can be solved efficiently in polynomial time. Additionally, our method demonstrates significantly increased robustness to C-arm pose errors when compared to the prior art. Because under actual clinical circumstances it is exceedingly difficult to track the C-arm, easing on this constraint has additional practical utility.

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