Scatter correction for cone-beam computedtomography using self-adaptive scatterkernel superposition

XIE Shi-Peng; LUO Li-Min

doi:10.1088/1674-1137/36/6/015

Chinese Physics C> 2012, Vol. 36> Issue(6) : 566-572 DOI: 10.1088/1674-1137/36/6/015

Scatter correction for cone-beam computedtomography using self-adaptive scatterkernel superposition

XIE Shi-Peng ,
LUO Li-Min

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Abstract：
The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.

References

[1]

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XIE Shi-Peng and LUO Li-Min. Scatter correction for cone-beam computedtomography using self-adaptive scatterkernel superposition[J]. Chinese Physics C, 2012, 36(6): 566-572. doi: 10.1088/1674-1137/36/6/015

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Received: 2011-08-24
Revised: 1900-01-01

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

Scatter correction for cone-beam computedtomography using self-adaptive scatterkernel superposition

Received Date: 2011-08-24

Accepted Date: 1900-01-01

Available Online: 2012-06-05

Abstract: The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.

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