Literature DB >> 26464343

Iterative CT shading correction with no prior information.

Pengwei Wu1, Xiaonan Sun, Hongjie Hu, Tingyu Mao, Wei Zhao, Ke Sheng, Alice A Cheung, Tianye Niu.   

Abstract

Shading artifacts in CT images are caused by scatter contamination, beam-hardening effect and other non-ideal imaging conditions. The purpose of this study is to propose a novel and general correction framework to eliminate low-frequency shading artifacts in CT images (e.g. cone-beam CT, low-kVp CT) without relying on prior information. The method is based on the general knowledge of the relatively uniform CT number distribution in one tissue component. The CT image is first segmented to construct a template image where each structure is filled with the same CT number of a specific tissue type. Then, by subtracting the ideal template from the CT image, the residual image from various error sources are generated. Since forward projection is an integration process, non-continuous shading artifacts in the image become continuous signals in a line integral. Thus, the residual image is forward projected and its line integral is low-pass filtered in order to estimate the error that causes shading artifacts. A compensation map is reconstructed from the filtered line integral error using a standard FDK algorithm and added back to the original image for shading correction. As the segmented image does not accurately depict a shaded CT image, the proposed scheme is iterated until the variation of the residual image is minimized. The proposed method is evaluated using cone-beam CT images of a Catphan©600 phantom and a pelvis patient, and low-kVp CT angiography images for carotid artery assessment. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 200 HU to be less than 30 HU and increases the spatial uniformity by a factor of 1.5. Low-contrast object is faithfully retained after the proposed correction. An effective iterative algorithm for shading correction in CT imaging is proposed that is only assisted by general anatomical information without relying on prior knowledge. The proposed method is thus practical and attractive as a general solution to CT shading correction.

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Year:  2015        PMID: 26464343     DOI: 10.1088/0031-9155/60/21/8437

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  9 in total

1.  Local filtration based scatter correction for cone-beam CT using primary modulation.

Authors:  Lei Zhu
Journal:  Med Phys       Date:  2016-11       Impact factor: 4.071

2.  Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

Authors:  Tonghe Wang; Yang Lei; Nivedh Manohar; Sibo Tian; Ashesh B Jani; Hui-Kuo Shu; Kristin Higgins; Anees Dhabaan; Pretesh Patel; Xiangyang Tang; Tian Liu; Walter J Curran; Xiaofeng Yang
Journal:  Med Dosim       Date:  2019-04-01       Impact factor: 1.482

3.  A model-based scatter artifacts correction for cone beam CT.

Authors:  Wei Zhao; Don Vernekohl; Jun Zhu; Luyao Wang; Lei Xing
Journal:  Med Phys       Date:  2016-04       Impact factor: 4.071

4.  Fast shading correction for cone-beam CT via partitioned tissue classification.

Authors:  Linxi Shi; Adam Wang; Jikun Wei; Lei Zhu
Journal:  Phys Med Biol       Date:  2019-03-13       Impact factor: 3.609

5.  Deep learning-based thoracic CBCT correction with histogram matching.

Authors:  Richard L J Qiu; Yang Lei; Joseph Shelton; Kristin Higgins; Jeffrey D Bradley; Walter J Curran; Tian Liu; Aparna H Kesarwala; Xiaofeng Yang
Journal:  Biomed Phys Eng Express       Date:  2021-10-29

6.  Correction of Bowtie-Filter Normalization and Crescent Artifacts for a Clinical CBCT System.

Authors:  Hong Zhang; Vic Kong; Ke Huang; Jian-Yue Jin
Journal:  Technol Cancer Res Treat       Date:  2016-06-23

7.  Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.

Authors:  Xiaokun Liang; Na Li; Zhicheng Zhang; Shaode Yu; Wenjian Qin; Yafen Li; Shupeng Chen; Huailing Zhang; Yaoqin Xie
Journal:  Quant Imaging Med Surg       Date:  2019-07

8.  A Projection Quality-Driven Tube Current Modulation Method in Cone-Beam CT for IGRT: Proof of Concept.

Authors:  Kuo Men; Jianrong Dai
Journal:  Technol Cancer Res Treat       Date:  2017-11-12

9.  Cone-Beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures.

Authors:  SayedMasoud Hashemi; Christopher Huynh; Arjun Sahgal; William Y Song; Håkan Nordström; Markus Eriksson; James G Mainprize; Young Lee; Mark Ruschin
Journal:  J Appl Clin Med Phys       Date:  2018-10-19       Impact factor: 2.102

  9 in total

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