PURPOSE: The purpose of this work is twofold: First, to characterize the artifacts occurring in helical 4D-CT imaging; second, to propose a method that can automatically identify the artifacts in 4D-CT images. The authors have designed a process that can automatically identify the artifacts in 4D-CT images, which may be invaluable in quantifying the quality of 4D-CT images and reducing the artifacts from the reconstructed images on a large dataset. METHODS: Given two adjacent stacks obtained from the same respiration phase, the authors determine if there are artifacts between them. The proposed method uses a "bridge" stack strategy to connect the two stacks. Using normalized cross correlation convolution (NCCC), the two stacks are mapped to the bridge stack and the best matching positions can be located. Using this position information, the authors can then determine if there are artifacts between the two stacks. By combining the matching positions with NCCC values, the performance can be improved. RESULTS: To validate the method, three expert observers independently labeled over 600 stacks on five patients. The results confirmed that high performance was obtained using the proposed method. The average sensitivity was about 0.87 and the average specificity was 0.82. The proposed method also outperformed the method of using respiratory signal (sensitivity increased from 0.50 to 0.87 and specificity increased from 0.70 to 0.82). CONCLUSIONS: This study shows that the spatial artifacts during 4D-CT imaging are characterized and can be located automatically by the proposed method. The method is relatively simple but effective. It provides a way to evaluate the artifacts more objectively and accurately. The reported approach has promising potential for automatically identifying the types and frequency of artifacts on large scale 4D-CT image dataset.
PURPOSE: The purpose of this work is twofold: First, to characterize the artifacts occurring in helical 4D-CT imaging; second, to propose a method that can automatically identify the artifacts in 4D-CT images. The authors have designed a process that can automatically identify the artifacts in 4D-CT images, which may be invaluable in quantifying the quality of 4D-CT images and reducing the artifacts from the reconstructed images on a large dataset. METHODS: Given two adjacent stacks obtained from the same respiration phase, the authors determine if there are artifacts between them. The proposed method uses a "bridge" stack strategy to connect the two stacks. Using normalized cross correlation convolution (NCCC), the two stacks are mapped to the bridge stack and the best matching positions can be located. Using this position information, the authors can then determine if there are artifacts between the two stacks. By combining the matching positions with NCCC values, the performance can be improved. RESULTS: To validate the method, three expert observers independently labeled over 600 stacks on five patients. The results confirmed that high performance was obtained using the proposed method. The average sensitivity was about 0.87 and the average specificity was 0.82. The proposed method also outperformed the method of using respiratory signal (sensitivity increased from 0.50 to 0.87 and specificity increased from 0.70 to 0.82). CONCLUSIONS: This study shows that the spatial artifacts during 4D-CT imaging are characterized and can be located automatically by the proposed method. The method is relatively simple but effective. It provides a way to evaluate the artifacts more objectively and accurately. The reported approach has promising potential for automatically identifying the types and frequency of artifacts on large scale 4D-CT image dataset.
Authors: G S Mageras; E Yorke; K Rosenzweig; L Braban; E Keatley; E Ford; S A Leibel; C C Ling Journal: J Appl Clin Med Phys Date: 2001 Impact factor: 2.102
Authors: Min Li; Sarah Joy Castillo; Richard Castillo; Edward Castillo; Thomas Guerrero; Liang Xiao; Xiaolin Zheng Journal: Int J Comput Assist Radiol Surg Date: 2017-02-14 Impact factor: 2.924
Authors: William S Ferris; Edward H Chao; Jennifer B Smilowitz; Randall J Kimple; John E Bayouth; Wesley S Culberson Journal: J Appl Clin Med Phys Date: 2022-04-29 Impact factor: 2.243
Authors: Eric M Wallat; Mattison J Flakus; Antonia E Wuschner; Wei Shao; Gary E Christensen; Joseph M Reinhardt; Andrew M Baschnagel; John E Bayouth Journal: Med Phys Date: 2020-04-13 Impact factor: 4.506