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A sparse-projection computed tomography reconstruction method for in vivo application of in-line phase-contrast imaging.

Wang L, Li X, Wu M, Zhang L, Luo S - Biomed Eng Online (2013)

Bottom Line: In recent years, X-ray phase-contrast imaging techniques have been extensively studied to visualize weakly absorbing objects.Combined with computed tomography (CT), phase-contrast CT can produce 3D volumetric images of samples.To date, the most common reconstruction method for phase-contrast X-ray CT imaging has been filtered back projection (FBP).

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Biomedical Engineering, Capital Medical University, You An Men, Beijing 100069, People's Republic of China.

ABSTRACT

Background: In recent years, X-ray phase-contrast imaging techniques have been extensively studied to visualize weakly absorbing objects. One of the most popular methods for phase-contrast imaging is in-line phase-contrast imaging (ILPCI). Combined with computed tomography (CT), phase-contrast CT can produce 3D volumetric images of samples. To date, the most common reconstruction method for phase-contrast X-ray CT imaging has been filtered back projection (FBP). However, because of the impact of respiration, lung slices cannot be reconstructed in vivo for a mouse using this method. Methods for reducing the radiation dose and the sampling time must also be considered.

Methods: This paper proposes a novel method of in vivo mouse lung in-line phase-contrast imaging that has two primary improvements compared with recent methods: 1) using a compressed sensing (CS) theory-based CT reconstruction method for the in vivo in-line phase-contrast imaging application and 2) using the breathing phase extraction method to address the lung and rib cage movement caused by a live mouse's breathing.

Results: Experiments were performed to test the breathing phase extraction method as applied to the lung and rib cage movement of a live mouse. Results with a live mouse specimen demonstrate that our method can reconstruct images of in vivo mouse lung.

Conclusions: The results demonstrate that our method could deal with vivo mouse's breathing and movements, meanwhile, using less sampling data than FBP while maintaining the same high quality.

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Three in-line phase-contrast experimental projection images of the mouse’s chest taken from several different angles.
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Figure 7: Three in-line phase-contrast experimental projection images of the mouse’s chest taken from several different angles.

Mentions: Figure 7 shows three in-line phase-contrast projection images of the mouse’s chest taken at different angles. Figure 8 shows the CT reconstructed images for one slice reconstructed using two algorithms under three sampling conditions. All the images come from the same slice, and the total number of slices is 105. The images are (a) the CT image reconstructed via the CS-based algorithm from 60 views, (b) the CT image reconstructed via the CS-based algorithm from 180 views, (c) the CT image reconstructed via the CS-based algorithm from 30 views, and (d) the CT image reconstructed via the FBP algorithm from 180 views. From the results, we can see that the CT images reconstructed via the CS-based algorithm have better quality than the CT images from the FBP algorithm. Even the CS-based method with less views, such as 30, has better performance than the FBP method with more views, such as 180. This result implies that the proposed novel CS-based CT reconstruction method could be applied with a lower exposure time and dose. Our algorithm could address the respiration of a live mouse and reconstruct the 3D rib cage using very sparse image data.


A sparse-projection computed tomography reconstruction method for in vivo application of in-line phase-contrast imaging.

Wang L, Li X, Wu M, Zhang L, Luo S - Biomed Eng Online (2013)

Three in-line phase-contrast experimental projection images of the mouse’s chest taken from several different angles.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750831&req=5

Figure 7: Three in-line phase-contrast experimental projection images of the mouse’s chest taken from several different angles.
Mentions: Figure 7 shows three in-line phase-contrast projection images of the mouse’s chest taken at different angles. Figure 8 shows the CT reconstructed images for one slice reconstructed using two algorithms under three sampling conditions. All the images come from the same slice, and the total number of slices is 105. The images are (a) the CT image reconstructed via the CS-based algorithm from 60 views, (b) the CT image reconstructed via the CS-based algorithm from 180 views, (c) the CT image reconstructed via the CS-based algorithm from 30 views, and (d) the CT image reconstructed via the FBP algorithm from 180 views. From the results, we can see that the CT images reconstructed via the CS-based algorithm have better quality than the CT images from the FBP algorithm. Even the CS-based method with less views, such as 30, has better performance than the FBP method with more views, such as 180. This result implies that the proposed novel CS-based CT reconstruction method could be applied with a lower exposure time and dose. Our algorithm could address the respiration of a live mouse and reconstruct the 3D rib cage using very sparse image data.

Bottom Line: In recent years, X-ray phase-contrast imaging techniques have been extensively studied to visualize weakly absorbing objects.Combined with computed tomography (CT), phase-contrast CT can produce 3D volumetric images of samples.To date, the most common reconstruction method for phase-contrast X-ray CT imaging has been filtered back projection (FBP).

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Biomedical Engineering, Capital Medical University, You An Men, Beijing 100069, People's Republic of China.

ABSTRACT

Background: In recent years, X-ray phase-contrast imaging techniques have been extensively studied to visualize weakly absorbing objects. One of the most popular methods for phase-contrast imaging is in-line phase-contrast imaging (ILPCI). Combined with computed tomography (CT), phase-contrast CT can produce 3D volumetric images of samples. To date, the most common reconstruction method for phase-contrast X-ray CT imaging has been filtered back projection (FBP). However, because of the impact of respiration, lung slices cannot be reconstructed in vivo for a mouse using this method. Methods for reducing the radiation dose and the sampling time must also be considered.

Methods: This paper proposes a novel method of in vivo mouse lung in-line phase-contrast imaging that has two primary improvements compared with recent methods: 1) using a compressed sensing (CS) theory-based CT reconstruction method for the in vivo in-line phase-contrast imaging application and 2) using the breathing phase extraction method to address the lung and rib cage movement caused by a live mouse's breathing.

Results: Experiments were performed to test the breathing phase extraction method as applied to the lung and rib cage movement of a live mouse. Results with a live mouse specimen demonstrate that our method can reconstruct images of in vivo mouse lung.

Conclusions: The results demonstrate that our method could deal with vivo mouse's breathing and movements, meanwhile, using less sampling data than FBP while maintaining the same high quality.

Show MeSH