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Geometric correction method for 3D in-line X-ray phase contrast image reconstruction.

Wu G, Wu M, Dong L, Luo S - Biomed Eng Online (2014)

Bottom Line: Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods.The method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI.It is easy to implement and can also be extended to other XPCI techniques.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Biomedical Engineering, Capital Medical University, Beijing, China. shuqian_luo@aliyun.com.

ABSTRACT

Background: Mechanical system with imperfect or misalignment of X-ray phase contrast imaging (XPCI) components causes projection data misplaced, and thus result in the reconstructed slice images of computed tomography (CT) blurred or with edge artifacts. So the features of biological microstructures to be investigated are destroyed unexpectedly, and the spatial resolution of XPCI image is decreased. It makes data correction an essential pre-processing step for CT reconstruction of XPCI.

Methods: To remove unexpected blurs and edge artifacts, a mathematics model for in-line XPCI is built by considering primary geometric parameters which include a rotation angle and a shift variant in this paper. Optimal geometric parameters are achieved by finding the solution of a maximization problem. And an iterative approach is employed to solve the maximization problem by using a two-step scheme which includes performing a composite geometric transformation and then following a linear regression process. After applying the geometric transformation with optimal parameters to projection data, standard filtered back-projection algorithm is used to reconstruct CT slice images.

Results: Numerical experiments were carried out on both synthetic and real in-line XPCI datasets. Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods.

Conclusions: The method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI. It is easy to implement and can also be extended to other XPCI techniques.

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Related in: MedlinePlus

Central slice reconstructed from projections of modified 3D Shepp-Logan phantom. From left to right: (a) the original, (b) reconstructed without geometric transform, (c) reconstructed with δ = 2 pixels and (d) reconstructed with θ = -5°.
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Figure 4: Central slice reconstructed from projections of modified 3D Shepp-Logan phantom. From left to right: (a) the original, (b) reconstructed without geometric transform, (c) reconstructed with δ = 2 pixels and (d) reconstructed with θ = -5°.

Mentions: Figure 4 compares central slices reconstructed from projections of a modified 3D Shepp-Logan phantom [18] without and with geometric transforms by using FBP. Translational transform with δ = 2 pixels and rotation transform with θ = 5° are applied to projections respectively. Compared to the result without geometric transform, translational transform makes the slice image blurred while rotational transform introduces obviously edge artifacts.


Geometric correction method for 3D in-line X-ray phase contrast image reconstruction.

Wu G, Wu M, Dong L, Luo S - Biomed Eng Online (2014)

Central slice reconstructed from projections of modified 3D Shepp-Logan phantom. From left to right: (a) the original, (b) reconstructed without geometric transform, (c) reconstructed with δ = 2 pixels and (d) reconstructed with θ = -5°.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4119299&req=5

Figure 4: Central slice reconstructed from projections of modified 3D Shepp-Logan phantom. From left to right: (a) the original, (b) reconstructed without geometric transform, (c) reconstructed with δ = 2 pixels and (d) reconstructed with θ = -5°.
Mentions: Figure 4 compares central slices reconstructed from projections of a modified 3D Shepp-Logan phantom [18] without and with geometric transforms by using FBP. Translational transform with δ = 2 pixels and rotation transform with θ = 5° are applied to projections respectively. Compared to the result without geometric transform, translational transform makes the slice image blurred while rotational transform introduces obviously edge artifacts.

Bottom Line: Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods.The method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI.It is easy to implement and can also be extended to other XPCI techniques.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Biomedical Engineering, Capital Medical University, Beijing, China. shuqian_luo@aliyun.com.

ABSTRACT

Background: Mechanical system with imperfect or misalignment of X-ray phase contrast imaging (XPCI) components causes projection data misplaced, and thus result in the reconstructed slice images of computed tomography (CT) blurred or with edge artifacts. So the features of biological microstructures to be investigated are destroyed unexpectedly, and the spatial resolution of XPCI image is decreased. It makes data correction an essential pre-processing step for CT reconstruction of XPCI.

Methods: To remove unexpected blurs and edge artifacts, a mathematics model for in-line XPCI is built by considering primary geometric parameters which include a rotation angle and a shift variant in this paper. Optimal geometric parameters are achieved by finding the solution of a maximization problem. And an iterative approach is employed to solve the maximization problem by using a two-step scheme which includes performing a composite geometric transformation and then following a linear regression process. After applying the geometric transformation with optimal parameters to projection data, standard filtered back-projection algorithm is used to reconstruct CT slice images.

Results: Numerical experiments were carried out on both synthetic and real in-line XPCI datasets. Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods.

Conclusions: The method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI. It is easy to implement and can also be extended to other XPCI techniques.

Show MeSH
Related in: MedlinePlus