Evidence map›Paper›PMID 26105146›Full record

ArticleMedical & biological engineering & computing2016

Dynamic CT perfusion image data compression for efficient parallel processing.

Renan Sales Barros, Silvia Delgado Olabarriaga, Jordi Borst, Marianne A A van Walderveen, Jorrit S Posthuma, Geert J Streekstra, Marcel van Herk, Charles B L M Majoie, Henk A Marquering

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Renan Sales BarrosBiomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Location L0, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands. r.salesbarros@amc.uva.nl.
Silvia Delgado OlabarriagaDepartment of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Location B0, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Jordi BorstDepartment of Radiology, Academic Medical Center, University of Amsterdam, Location B0, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Marianne A A van WalderveenDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Jorrit S PosthumaBiomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Location L0, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
Geert J StreekstraBiomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Location L0, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
Marcel van HerkBiomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Location L0, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
Charles B L M MajoieDepartment of Radiology, Academic Medical Center, University of Amsterdam, Location B0, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Henk A MarqueringBiomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Location L0, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
Academic Medical Center · NLUniversity of Amsterdam · NLLeiden University Medical Center · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Computer GraphicsPerfusionAlgorithmsData CompressionHumansImage Processing, Computer-AssistedTime FactorsTomography, X-Ray ComputedAcute careCT perfusionGPULossless compressionParallel processing

Identifiers

PMID26105146
PMCPMC4799275
OpenAlexW2148098172

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.