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Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy

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Abstract

Four dimensional cone-beam computed tomography (4D-CBCT) images were widely used for patient positing and target localization in radiotherapy. As consisting of multiple CBCT sets, it needs more time and space for data transferring and storage. In this study the feasibility of applying video coding algorithms for 4D-CBCT image compression was investigated. Prior to compression 4D-CBCT images were arranged in an order based on breathing phase or slice location for input sequence of video encoder. Median filtering was applied to suppress noise and artifact of 4D-CBCT for improved image quality. Three popular video coding algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG-4) were tested and their performances were evaluated on a publicly available 4D-CBCT database. The average compression ratio of MPEG-4 was 135, while the values of Motion JPEG AVI and Motion JPEG 2000 were 16 and 7, respectively. The compression rate of two ordering methods was comparable and the location-based ordering method was slightly higher. With pre-processing of median filtering, the inter-frame similarity of input sequence was improved and the resulting compression rate was increased. MPEG-4 provided extremely higher compression rate for 4D-CBCT images. The ordering method based on slice location resulted in higher compression rate than the ordering method based on breathing phase. The median filtering was effective in improving inter-frame similarity and resulted in higher compression rate. The video coding algorithms are not only applicable for 4D image modalities but also feasible for serial 3D image modalities.

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Funding

This work was partially supported by National Key R&D Program of China (2016YFC0904600) and China National Science Foundation (CNSF11875320).

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Correspondence to Jianrong Dai.

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Yan, H., Li, Y. & Dai, J. Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy. J Digit Imaging 33, 1292–1300 (2020). https://doi.org/10.1007/s10278-020-00363-9

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  • DOI: https://doi.org/10.1007/s10278-020-00363-9

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