Mr. Xiaoliu Zhang Profile

Xiaoliu Zhang

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Publications (8)

Proceedings Article | 4 April 2022 Presentation + Paper

Clinical CT-based assessment of trabecular bone shear modulus using nonlinear finite element modelling

Indranil Guha, Xiaoliu Zhang, Punam Saha

Proceedings Volume 12036, 120361E (2022) https://doi.org/10.1117/12.2611648

KEYWORDS: Bone, Finite element methods, Computed tomography, In vivo imaging

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Proceedings Article | 15 February 2021 Presentation + Paper

Unsupervised GAN-CIRCLE for high-resolution reconstruction of bone microstructure from low-resolution CT scans

Indranil Guha, Syed Ahmed Nadeem, Xiaoliu Zhang, Steven Levy, James Torner, Punam Saha

Proceedings Volume 11600, 116001F (2021) https://doi.org/10.1117/12.2581068

KEYWORDS: Bone, Computed tomography, Scanners, Minerals, In vivo imaging

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Proceedings Article | 15 February 2021 Presentation + Paper

Finite element modelling of trabecular bone microstructure using emerging CT images

Indranil Guha, Chamith Rajapakse, Xiaoliu Zhang, Gregory Chang, Punam Saha

Proceedings Volume 11600, 116001C (2021) https://doi.org/10.1117/12.2580788

KEYWORDS: Bone, Computed tomography, Terbium, Finite element methods, Modeling, Image segmentation, Image resolution, Stereoscopy, Sensors, Performance modeling

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Proceedings Article | 28 February 2020 Presentation + Paper

Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE

Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven Levy, Ge Wang, James Torner, Punam Saha

Proceedings Volume 11317, 113170U (2020) https://doi.org/10.1117/12.2549318

KEYWORDS: Computed tomography, Bone, X-ray computed tomography, Network architectures, Convolution, CT reconstruction

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Proceedings Article | 28 February 2020 Presentation + Paper

CT-based characterization of transverse and longitudinal trabeculae and its applications

Xiaoliu Zhang, Elena Letuchy, Steven Levy, James Torner, Punam Saha

Proceedings Volume 11317, 113171F (2020) https://doi.org/10.1117/12.2549881

KEYWORDS: Bone, Terbium, Computed tomography, Image segmentation, Minerals, Image processing, Magnetic resonance imaging, Algorithm development, X-ray computed tomography, In vivo imaging

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Proceedings Article | 12 March 2018 Paper

MRI-based active shape model of the human proximal femur using fiducial and secondary landmarks and its validation

Xiaoliu Zhang, Cheng Chen, Sean Boone, Vivek Joshi, Arakua Welbeck, Guoyuan Liang, Gregory Chang, Punam Saha

Proceedings Volume 10578, 105781L (2018) https://doi.org/10.1117/12.2293732

KEYWORDS: Bone, 3D modeling, Magnetic resonance imaging, Image segmentation, Shape analysis, Prototyping, Error analysis, Visualization, Principal component analysis

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Proceedings Article | 13 March 2017 Paper

Direct biomechanical modeling of trabecular bone using a nonlinear manifold-based volumetric representation

Dakai Jin, Jia Lu, Xiaoliu Zhang, Cheng Chen, ErWei Bai, Punam Saha

Proceedings Volume 10137, 101371U (2017) https://doi.org/10.1117/12.2254705

KEYWORDS: Bone, Image segmentation, In vivo imaging, Modeling, 3D image reconstruction, 3D image processing, Biological research, 3D modeling, Algorithm development, Finite element methods, Reconstruction algorithms

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Osteoporosis is associated with increased fracture risk. Recent advancement in the area of in vivo imaging allows segmentation of trabecular bone (TB) microstructures, which is a known key determinant of bone strength and fracture risk. An accurate biomechanical modelling of TB micro-architecture provides a comprehensive summary measure of bone strength and fracture risk. In this paper, a new direct TB biomechanical modelling method using nonlinear manifold-based volumetric reconstruction of trabecular network is presented. It is accomplished in two sequential modules. The first module reconstructs a nonlinear manifold-based volumetric representation of TB networks from three-dimensional digital images. Specifically, it starts with the fuzzy digital segmentation of a TB network, and computes its surface and curve skeletons. An individual trabecula is identified as a topological segment in the curve skeleton. Using geometric analysis, smoothing and optimization techniques, the algorithm generates smooth, curved, and continuous representations of individual trabeculae glued at their junctions. Also, the method generates a geometrically consistent TB volume at junctions. In the second module, a direct computational biomechanical stress-strain analysis is applied on the reconstructed TB volume to predict mechanical measures. The accuracy of the method was examined using micro-CT imaging of cadaveric distal tibia specimens (N = 12). A high linear correlation (r = 0.95) between TB volume computed using the new manifold-modelling algorithm and that directly derived from the voxel-based micro-CT images was observed. Young’s modulus (YM) was computed using direct mechanical analysis on the TB manifold-model over a cubical volume of interest (VOI), and its correlation with the YM, computed using micro-CT based conventional finite-element analysis over the same VOI, was examined. A moderate linear correlation (r = 0.77) was observed between the two YM measures. This preliminary results show the accuracy of the new nonlinear manifold modelling algorithm for TB, and demonstrate the feasibility of a new direct mechanical strain-strain analysis on a nonlinear manifold model of a highly complex biological structure.

Proceedings Article | 13 March 2017 Paper

Robust segmentation of trabecular bone for in vivo CT imaging using anisotropic diffusion and multi-scale morphological reconstruction

Cheng Chen, Dakai Jin, Xiaoliu Zhang, Steven Levy, Punam Saha

Proceedings Volume 10137, 101371T (2017) https://doi.org/10.1117/12.2254546

KEYWORDS: Image segmentation, Bone, In vivo imaging, Computed tomography, Anisotropic diffusion, Image processing, Image registration, Diffusion, Image processing algorithms and systems, Image resolution, Algorithm development

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Showing 5 of 8 publications

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