Literature DB >> 32583276

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

Hui Yan1, Yexiong Li1, Jianrong Dai2.   

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.

Entities:  

Keywords:  4D-CBCT; Image compression; Video coding algorithm

Mesh:

Year:  2020        PMID: 32583276      PMCID: PMC7573099          DOI: 10.1007/s10278-020-00363-9

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  20 in total

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Review 4.  An overview of digital compression of medical images: can we use lossy image compression in radiology?

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5.  The impact of image information on compressibility and degradation in medical image compression.

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Journal:  Med Phys       Date:  2006-08       Impact factor: 4.071

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Journal:  Biomedica       Date:  2013 Jan-Mar       Impact factor: 0.935

7.  Compress compound images in H.264/MPGE-4 AVC by exploiting spatial correlation.

Authors:  Cuiling Lan; Guangming Shi; Feng Wu
Journal:  IEEE Trans Image Process       Date:  2009-12-15       Impact factor: 10.856

Review 8.  Four-dimensional computed tomography (4DCT): A review of the current status and applications.

Authors:  Yune Kwong; Alexandra Olimpia Mel; Greg Wheeler; John M Troupis
Journal:  J Med Imaging Radiat Oncol       Date:  2015-06-03       Impact factor: 1.735

9.  Establishing a framework to implement 4D XCAT phantom for 4D radiotherapy research.

Authors:  Raj K Panta; Paul Segars; Fang-Fang Yin; Jing Cai
Journal:  J Cancer Res Ther       Date:  2012 Oct-Dec       Impact factor: 1.805

10.  Exploring correlation information for image compression of four-dimensional computed tomography.

Authors:  Hui Yan; Yexiong Li; Jianrong Dai
Journal:  Quant Imaging Med Surg       Date:  2019-07
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