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

被引:10
作者
Moghari, Mahdieh Dashtbani [1 ]
Sanaat, Amirhossein [2 ]
Young, Noel [3 ,4 ]
Moore, Krystal [3 ]
Zaidi, Habib [2 ]
Evans, Andrew [5 ,6 ]
Fulton, Roger R. [6 ,7 ,8 ]
Kyme, Andre Z. [1 ,8 ]
机构
[1] Univ Sydney, Fac Engn & Informat Technol, Sch Biomed Engn, Sydney, Australia
[2] Geneva Univ Hosp, Div Nucl Med & Mol Imaging, CH-1205 Geneva, Switzerland
[3] Westmead Hosp, Dept Radiol, Sydney, Australia
[4] Western Sydney Univ, Sch Med, Med imaging Grp, Sydney, Australia
[5] Westmead Hosp, Dept Aged Care & Stroke, Sydney, Australia
[6] Univ Sydney, Sch Hlth Sci, Sydney, Australia
[7] Westmead Hosp, Dept Med Phys, Sydney, Australia
[8] Univ Sydney, Brain & Mind Ctr, Sydney, Australia
关键词
deep learning; radiation dose reduction; scan duration reduction; CT perfusion imaging; acute ischaemic stroke; COMPUTED-TOMOGRAPHY; NOISE-REDUCTION; QUALITY; PATIENT;
D O I
10.1088/1361-6560/acdf3a
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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 & PLUSMN; 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.
引用
收藏
页数:13
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