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...

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Bibliographic Details
Published inMedical & biological engineering & computing Vol. 54; no. 2-3; pp. 463 - 473
Main Authors Barros, Renan Sales, Olabarriaga, Silvia Delgado, Borst, Jordi, van Walderveen, Marianne A. A., Posthuma, Jorrit S., Streekstra, Geert J., van Herk, Marcel, Majoie, Charles B. L. M., Marquering, Henk A.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016
Springer Nature B.V
Subjects
African American studies
Algorithms
Analysis
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Circulatory system
Clouds
Compressed
Computation
Computer Applications
Computer Graphics
Data compression
Data Compression - methods
Datasets
Graphics processing units
Health care
Human Physiology
Humans
Image Processing, Computer-Assisted - methods
Imaging
Medical imaging
Original
Original Article
Parallel processing
Patients
Perfusion
Pipelines
Radiology
Scanners
Stroke
Time Factors
Tomography, X-Ray Computed - methods
Lossless compression
Parallel processing
CT perfusion
GPU
Acute care
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Summary: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.
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ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-015-1331-6