Dynamic CT perfusion image data compression for efficient parallel processing.
The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 2/3; pp. 463 - 474 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113881184&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113881184 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2016 vid: 54 iid: 2/3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113881184 113881184 NLM26105146 10.1007/s11517-015-1331-6 NLM26105146 PMC4799275 113881184 ppf: 463 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Dynamic CT perfusion image data compression for efficient parallel processing. aug: au: Barros, Renan Olabarriaga, Silvia Borst, Jordi Walderveen, Marianne Posthuma, Jorrit Streekstra, Geert Herk, Marcel Majoie, Charles Marquering, Henk Barros, Renan Sales Olabarriaga, Silvia Delgado van Walderveen, Marianne A A Posthuma, Jorrit S Streekstra, Geert J van Herk, Marcel Majoie, Charles B L M Marquering, Henk A affil: Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Location B0, Meibergdreef 9 1105 AZ Amsterdam The Netherlands sug: subj: Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Methods Computer Graphics Perfusion Time Factors Algorithms Clinical Assessment Tools Scales ab: The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes transfers to cloud infrastructures time-consuming and therefore not suitable in acute situations. To reduce this transfer time, this work proposes a fast and lossless compression algorithm for CTP data. The algorithm exploits redundancies in the temporal dimension and keeps random read-only access to the image elements directly from the compressed data on the GPU. To the best of our knowledge, this is the first work to present a GPU-ready method for medical image compression with random access to the image elements from the compressed data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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