Efficient Ring Artifact Remove Scheme of Cone-Beam CT Image

The element response inconsistency of flat-panel detector may result in ring artefacts in cone-beam CT image reconstruction. Ring artefacts severely affect the quality of CT reconstructed image, thus removing ring artefacts is very meaningful for image analysis and subsequent processing. This paper presents an algorithm for processing reconstructed image, and has achieved a result through simulating. This approach proves very effective in removing ring artefacts from images.

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Association of cortical shape of the mandible on panoramic radiographs with mandibular trabecular bone structure in Japanese adults: a cone-beam CT-image analysis

Oral Radiology ◽

10.1007/s11282-013-0155-z ◽

2013 ◽

Vol 30 (2) ◽

pp. 160-167 ◽

Cited By ~ 4

Author(s):

Noriyasu Mochizuki ◽

Noriyuki Sugino ◽

Tadashi Ninomiya ◽

Nobuo Yoshinari ◽

Nobuyuki Udagawa ◽

...

Keyword(s):

Image Analysis ◽

Trabecular Bone ◽

Bone Structure ◽

Cone Beam Ct ◽

Trabecular Bone Structure ◽

Cone Beam ◽

Ct Image ◽

Panoramic Radiographs ◽

Japanese Adults ◽

Ct Image Analysis

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Image quality of flat-panel cone beam CT

10.1117/12.479973 ◽

2003 ◽

Cited By ~ 8

Author(s):

Georg Rose ◽

Jens Wiegert ◽

Dirk Schaefer ◽

Klaus Fiedler ◽

Norbert Conrads ◽

...

Keyword(s):

Image Quality ◽

Cone Beam Ct ◽

Cone Beam ◽

Flat Panel

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GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

International Journal of Biomedical Imaging ◽

10.1155/2009/149079 ◽

2009 ◽

Vol 2009 ◽

pp. 1-8 ◽

Cited By ~ 19

Author(s):

Xing Zhao ◽

Jing-jing Hu ◽

Peng Zhang

Keyword(s):

Image Reconstruction ◽

Cone Beam Ct ◽

Large Data ◽

Circular Cone ◽

Cone Beam ◽

Projection Data ◽

Ct Image ◽

Reconstruction Process ◽

Data Volume ◽

Ct Image Reconstruction

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.

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