Open AccessEvaluation of video compression methods for cone‐beam computerized tomography
Author(s) -
Yan Hui,
Li Yexiong,
Dai Jianrong
Publication year - 2019
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12596
Subject(s) - jpeg , computer science , lossless compression , computer vision , data compression , cone beam computed tomography , artificial intelligence , compression ratio , lossy compression , motion compensation , image compression , compression (physics) , image processing , medicine , radiology , computed tomography , image (mathematics) , automotive engineering , engineering , internal combustion engine , materials science , composite material
Purpose Cone‐beam computerized tomography ( CBCT ) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data. Materials and methods At first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI , and MPEG ‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients. Results Among three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG ‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG ‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT . Conclusions Video compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.