FleXCT: a flexible X-ray CT scanner with 10 degrees of freedom

Samber, Björn De; Renders, Jens; Elberfeld, Tim; Maris, Yves; Sanctorum, Jonathan; Six, Nathanaël; Liang, Z.; Beenhouwer, Jan De; Sijbers, Jan

doi:10.1364/oe.409982

Cited by 38 publications

(13 citation statements)

References 29 publications

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“…It also maintains the mapping of the 3D landmark coordinates throughout the 3D models as they can be computed with respect to the transformation parameters. In this work, a 3D CT volume of a piglet hindlimb acquired with a high-quality X-ray imaging system, FlexCT ( 33 ), is used as the base model for the simulation. The 3D model is with a size of 1, 416 × 1, 416 × 416 voxels, and voxel size of 45μ m .…”

Section: Methodsmentioning

confidence: 99%

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

Nguyen

Pereira²,

Liang³

et al. 2022

Front. Vet. Sci.

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The 3D musculoskeletal motion of animals is of interest for various biological studies and can be derived from X-ray fluoroscopy acquisitions by means of image matching or manual landmark annotation and mapping. While the image matching method requires a robust similarity measure (intensity-based) or an expensive computation (tomographic reconstruction-based), the manual annotation method depends on the experience of operators. In this paper, we tackle these challenges by a strategic approach that consists of two building blocks: an automated 3D landmark extraction technique and a deep neural network for 2D landmarks detection. For 3D landmark extraction, we propose a technique based on the shortest voxel coordinate variance to extract the 3D landmarks from the 3D tomographic reconstruction of an object. For 2D landmark detection, we propose a customized ResNet18-based neural network, BoneNet, to automatically detect geometrical landmarks on X-ray fluoroscopy images. With a deeper network architecture in comparison to the original ResNet18 model, BoneNet can extract and propagate feature vectors for accurate 2D landmark inference. The 3D poses of the animal are then reconstructed by aligning the extracted 2D landmarks from X-ray radiographs and the corresponding 3D landmarks in a 3D object reference model. Our proposed method is validated on X-ray images, simulated from a real piglet hindlimb 3D computed tomography scan and does not require manual annotation of landmark positions. The simulation results show that BoneNet is able to accurately detect the 2D landmarks in simulated, noisy 2D X-ray images, resulting in promising rigid and articulated parameter estimations.

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Section: Methodsmentioning

confidence: 99%

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

Nguyen

Pereira²,

Liang³

et al. 2022

Front. Vet. Sci.

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“…The UAntwerp FleXCT scanner [19] was used to acquire Xray projections of a 3D printed steel cylinder (2.5 mm radius and 19 mm height). The cylinder was scanned at 230 KVp with a copper filter of 1 mm, source detector distance of 650 mm and source object distance of 43.33 mm.…”

Section: Methodsmentioning

confidence: 99%

CAD-Based Scatter Compensation For Polychromatic Reconstruction Of Additive Manufactured Parts

Iuso¹,

Nazemi

Six

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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High density materials, such as metals, strongly scatter Xray photons during X-ray Computed Tomography (CT) scans, which is detrimental to the quality of the reconstructed images. In this study, a scatter compensation method for X-ray CT, based on Monte-Carlo (MC) simulations from the object's CAD model, is presented and employed in conjunction with polychromatic reconstructions. The estimation of the scatter contributions is accelerated by 1) reducing the number of simulated projections accordingly to the Nyquist theorem, 2) noise reduction 3) angular interpolation. The method was applied to enhance CT images of a steel object produced via Additive Manufacturing, whose CAD model is known. Results show that, in conjunction with polychromatic reconstruction, our method can efficiently reduce beam hardening, scattering artifacts and increase the contrast of defects within the object.

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“…After detachment from the build plate by wire electrical discharge machining (EDM), the object was CT scanned. The UAntwerp FleXCT scanner [20] was used to acquire and reconstruct the x-ray projections. The CT scans were performed with a peak voltage of 220 kV and 23 W as tube power, using a 1 mm thick copper filter.…”

Section: Description Of the Data Setmentioning

confidence: 99%

3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

Heylen

Thanki

Verhees

et al. 2022

Meas. Sci. Technol.

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X-ray computed tomography (X-CT) plays an important role in non-destructive quality inspection and process evaluation in metal additive manufacturing, as several types of defects such as keyhole and lack of fusion pores can be observed in these 3D images as local changes in material density. Segmentation of these defects often relies on threshold methods applied to the reconstructed attenuation values of the 3D image voxels. However, the segmentation accuracy is aﬀected by unavoidable X-CT reconstruction features such as partial volume eﬀects, voxel noise and imaging artefacts. These eﬀects create false positives, diﬃculties in threshold value selection and unclear or jagged defect edges. In this paper, we present a new X-CT defect segmentation method based on preprocessing the X-CT image with a 3D total variation denoising method. By comparing the changes in the histogram, threshold selection can be signiﬁcantly better, and the resulting segmentation is of much higher quality. We derive the optimal algorithm parameter settings and demonstrate robustness for deviating settings. The technique is presented on simulated data sets, compared between low- and high-quality X-CT scans, and evaluated with optical microscopy after destructive tests.

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FleXCT: a flexible X-ray CT scanner with 10 degrees of freedom

Cited by 38 publications

References 29 publications

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

CAD-Based Scatter Compensation For Polychromatic Reconstruction Of Additive Manufactured Parts

3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

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