Shading correction algorithm for cone-beam CT in radiotherapy: extensive clinical validation of image quality improvement

K. D. Joshi; T. E. Marchant; C. J. Moore

doi:10.1117/12.2254035

9 March 2017 Shading correction algorithm for cone-beam CT in radiotherapy: extensive clinical validation of image quality improvement

K. D. Joshi, T. E. Marchant, C. J. Moore

Author Affiliations +

Proceedings Volume 10132, Medical Imaging 2017: Physics of Medical Imaging; 101322A (2017) https://doi.org/10.1117/12.2254035
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

A shading correction algorithm for the improvement of cone-beam CT (CBCT) images (Phys. Med. Biol. 53 5719{33) has been further developed, optimised and validated extensively using 135 clinical CBCT images of patients undergoing radiotherapy treatment of the pelvis, lungs and head and neck. An automated technique has been developed to efficiently analyse the large number of clinical images. Small regions of similar tissue (for example fat tissue) are automatically identified using CT images. The same regions on the corresponding CBCT image are analysed to ensure that they do not contain pixels representing multiple types of tissue. The mean value of all selected pixels and the non-uniformity, defined as the median absolute deviation of the mean values in each small region, are calculated. Comparisons between CT and raw and corrected CBCT images are then made. Analysis of fat regions in pelvis images shows an average difference in mean pixel value between CT and CBCT of 136:0 HU in raw CBCT images, which is reduced to 2:0 HU after the application of the shading correction algorithm. The average difference in non-uniformity of fat pixels is reduced from 33:7 in raw CBCT to 2:8 in shading-corrected CBCT images. Similar results are obtained in the analysis of lung and head and neck images.

Citation Download Citation

K. D. Joshi, T. E. Marchant, and C. J. Moore "Shading correction algorithm for cone-beam CT in radiotherapy: extensive clinical validation of image quality improvement", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322A (9 March 2017); https://doi.org/10.1117/12.2254035

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