Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization

Miao, Shun; Tüysüzoğlu, Ahmet; Wang, Z J; Liao, Rui

doi:10.1007/s11548-016-1387-2

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…However, the method requires an initialization and has a restricted capture range. [5] propose a library based method to recover the pose of an articulated object in X-ray images. The method relies on decoupling the registration process into estimating first parameters affecting the geometry and then parameters not affecting the geometry of the object in question.…”

Section: Although Registration Methods Have Been Widely Researched Pr...mentioning

confidence: 99%

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

Grimm¹,

Esteban²,

Unberath³

et al. 2020

Preprint

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Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work adresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real Xrays. Then, for each patient CT, a patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained network's predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (± standard deviation) target registration error in millimetres is 4.1 ± 4.3 for simulated X-rays with a success rate of 92% and 4.2 ± 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30mm.

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Section: Although Registration Methods Have Been Widely Researched Pr...mentioning

confidence: 99%

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

Grimm¹,

Esteban²,

Unberath³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the present work, we extend this approach to allow geometric calibration using CAD data of a calibration phantom in terms of position and orientation of the rotation axis, the detector, the phantom and the position of the source (see Figure 1). These parameters are optimized using the Hill climbing algorithm [7]. Although this alignment procedure returns an accurate result, it is not applicable to in-line quality control due to its long execution time.…”

Section: Methodsmentioning

confidence: 99%

CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images

Presenti¹,

Sijbers²,

Dekker³

et al. 2019

eJNDT

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X-ray 3D computed tomography (CT) is a non-destructive method that allows inspection of internal components of an object. In the conventional approach, the comparison between the measured projections and the 3D model is performed by processing a CT reconstruction from projection data. The accuracy of the inspection analysis strictly depends on the reconstruction, which can suffer from numerous artifacts. To overcome this problem, we propose a new method that performs inspection directly in the projection space, simulating realistic projections from the computer aided design (CAD) model. Our method bases its strength on the prior knowledge from the CAD data and knowledge about the inspected object itself. When inspecting for defective or missing components, it is not restrictive to assume that the position of a potential deviation from the nominal geometry is approximately known. Therefore, we define regions of interest (ROIs) for feature extraction by simply projecting a component or a volume around it. Furthermore, based on the nominal geometry, we can identify the projection angles for which a certain component is most visible, to restrict the inspection analysis to projections obtained at these angles. Our procedure for quality control is composed of two main steps: the first one must be performed prior to in-line inspection and consists of i) an accurate alignment to calibrate the system geometry and ii) building of libraries of simulated projection data. These libraries are used in the second, in-line step to perform a fast 3D alignment with respect to the position and orientation of the sample. The knowledge of the sample’s orientation allows one to select only a few projection angles for which a potential defect is most visible and perform classification by extracting features and measures from merely the projections at this limited set of angles, for a predefined ROI. In this paper, we motivate and describe our procedure for limited view in-line inspection of defects and validate our method with experiments on real data.

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X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm

Zhang¹,

Du²,

Song³

et al. 2022

Computer Methods and Programs in Biomedicine

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Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization

Cited by 5 publications

References 21 publications

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images

X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm

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