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Abstract
Nowadays four-dimensional computed tomography (4DCT) is popularly used in evaluating respiration-related organ motion for patients under radiotherapy. As consisting of multiple subsets of CT images, a larger storage space is needed for 4DCT. In this study, the correlations information within these subsets was explored and the popular video encoders were used for 4DCT image compression. The images of 4DCT subsets were arranged in an order and put into a sequence for the input of video encoder. The effects of two ordering methods on the compression performance of video encoder were compared. One ordering method, phase-prioritized (PP) sequence, arranged 4DCT images according to their respiration phases. Another ordering method, location-prioritized (LP) sequence, arranged 4DCT images by their slice locations. Three popular video encoders were selected including one lossless compression algorithm and two lossy compression algorithms. Based on a publicly available database consisting of 82 4DCT datasets of 20 lung cancer patients, the performance of two ordering methods and three video encoders was quantitatively assessed. The highest compression ratios were 310 and 260 for LP and PP ordering methods respectively and achieved by one video encoder employing inter-frame prediction coding algorithm. The highest compression ratios for the other two video encoders employing intra-frame coding algorithms were 16 and 7. The LP ordering method showed less inter-frame variation and higher inter-frame similarity over the PP ordering method. The LP ordering method would result in higher compression ratio than the PP ordering method for 4DCT image compression. The compression performance of video encoder employing inter-frame prediction coding algorithm is superior to those of video encoders employing intra-frame coding algorithms. The video encoder with inter-frame prediction coding algorithm and LP ordering method would be highly suitable for 4DCT image compression.
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