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Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models

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OBJECTIVES: Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification.

METHODS: CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10 % of their true volume was compared for VM and state-of-the-art algorithms.

RESULTS: BT in veins and hypoperfused tissue was observed in 9/40 (22.5 %) and 17/40 patients (42.5 %), respectively. Down-sampling to 128 × 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively).

CONCLUSIONS: BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification.

KEY POINTS: • Too-short imaging duration is common in clinical acute stroke CTP imaging. • The consequence is impaired identification of hypoperfused tissue in acute stroke patients. • The vascular model is less sensitive than current algorithms to imaging duration. • Noise reduction by image down-sampling improves identification of hypoperfused tissue by CTP.

Original languageEnglish
JournalEuropean Radiology
Volume25
Issue7
Pages (from-to)2080-8
Number of pages9
ISSN0938-7994
DOIs
Publication statusPublished - 1 Jul 2015

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