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Proceedings Paper

Low-dose cerebral CT perfusion restoration via non-local convolution neural network: initial study
Author(s): Sui Li; Dong Zeng; Zhaoying Bian; Jianhua Ma Sr.
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Paper Abstract

Computed tomography perfusion (CTP) imaging can be used to detect ischemic stroke via high-resolution and quantitative hemodynamic maps. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks. Therefore, reducing radiation dose in CTP has raised significant research interests. In this work, we present a non-local convolution neural network (NL-Net) to yield high quality CTP images and high precision hemodynamic maps at low-dose cases. Specifically, different from the traditional network in CT imaging, this NL-Net takes into consideration the non-local information from adjacent frames as one of the input. Then, the low-dose CTP images combining with the non-local information feeds into the pre-trained network to produce desired CTP images with high quality. The clinical patient data are used to demonstrate the performance of the NL-Net, and corresponding results indicate that the presented NL-Net can obtain better CTP images and more accurate hemodynamic maps compared with the competing approaches.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107224 (28 May 2019); doi: 10.1117/12.2534800
Show Author Affiliations
Sui Li, Southern Medical Univ. (China)
Dong Zeng, South China Univ. of Technology (China)
Southern Medical Univ. (China)
Zhaoying Bian, Southern Medical Univ. (China)
Jianhua Ma Sr., Southern Medical Univ. (China)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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