Abstract：Sparse constrained and GREIT algorithms were two hot optimized reconstruction algorithms
for brain Electrical Impedance Tomography (EIT). With different mathematical models, these two
algorithms had different performances. To give recommendations to brain EIT algorithm selection, based
on the three dimensional head model, a comparison of the performances of conventional quadratic norm
regularization algorithm (L2 algorithm), sparse constrained algorithm and GREIT algorithm was made
in this paper. On the basis of evaluation results, it could be concluded that sparse constrained algorithm
and GREIT demonstrated better performances than L2 algorithm in image noise, location error and shape
error. With threshold function, the sparse constrained algorithm suppressed the image noise by multi-step
iteration, and highlighted the reconstruction object. Sparse constrained algorithm could greatly improve
the performances of brain EIT and was suitable for brain EIT, which was of great reference for the
researches on the brain EIT reconstruction algorithm EIT in the future.
李昊庭，徐灿华，刘本源，
杨琳，董秀珍，付峰. 稀疏加权算法与GREIT算法在颅脑电阻抗成像中的对比研究[J]. 中国医疗设备, 2016, 31(11): 23-27.
LI Hao-ting, XU Can-hua,
LIU Ben-yuan, YANG Lin,
DONG Xiu-zhen, FU Feng. Comparative Study on the Application of Sparse Constrained Algorithm
and GREIT in Brain Electrical Impedance Tomography
[. China Medical Devices, 2016, 31(11): 23-27.
