en
Scientific article
Open access
English

Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network

Published inPhysics in medicine and biology, vol. 68, no. 16, 165005
Publication date2023-07-31
First online date2023-07-31
Abstract

Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time.

Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions.

Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%–15%, 11%–28% and 7%–22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%–76%, 76%–86% and 83%–92%.

Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively.

eng
Keywords
  • CT perfusion imaging
  • Acute ischaemic stroke
  • Deep learning
  • Radiation dose reduction
  • Scan duration reduction
  • Brain Ischemia
  • Cerebrovascular Circulation / physiology
  • Humans
  • Neural Networks, Computer
  • Perfusion Imaging / methods
  • Radiation Dosage
  • Stroke / diagnostic imaging
  • Tomography, X-Ray Computed / methods
Citation (ISO format)
DASHTBANI MOGHARI, Mahdieh et al. Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network. In: Physics in medicine and biology, 2023, vol. 68, n° 16, p. 165005. doi: 10.1088/1361-6560/acdf3a
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Article (Published version)
Identifiers
ISSN of the journal0031-9155
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Creation07/31/2023 8:27:11 PM
First validation11/13/2023 6:03:59 PM
Update time11/13/2023 6:03:59 PM
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