Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme.
Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Xiumei
X
Tian
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Dong
D
Zeng
The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Shanli
S
Zhang
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Jing
J
Huang
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Hua
H
Zhang
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Ji
J
He
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Lijun
L
Lu
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Weiwen
W
Xi
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Jianhua
J
Ma
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Zhaoying
Z
Bian
D000465:Algorithms; D002560:Cerebrovascular Circulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D055420:Perfusion Imaging / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
Low-dose; computed tomography; dictionary learning; dynamic cerebral perfusion; image restoration