Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography
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Date
2015-08-01Author
Yao, Jixing
Tian, Fenghua
Rakvongthai, Yothin
Oraintara, Soontorn
Liu, Hanli
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Show full item recordAbstract
Conventional reconstruction of diffuse optical tomography
(DOT) is based on the Tikhonov regularization and the white Gaussian
noise assumption. Consequently, the reconstructed DOT images usually
have a low spatial resolution. In this work, we have derived a novel
quantification method for noise variance based on the linear Rytov
approximation of the photon diffusion equation. Specifically, we have
implemented this quantification of noise variance to normalize the
measurement signals from all source-detector channels along with sparsity
regularization to provide high-quality DOT images. Multiple experiments
from computer simulations and laboratory phantoms were performed to
validate and support the newly developed algorithm. The reconstructed
images demonstrate that quantification and normalization of noise variance
with sparsity regularization (QNNVSR) is an effective reconstruction
approach to greatly enhance the spatial resolution and the shape fidelity for
DOT images. Since noise variance can be estimated by our derived
expression with relatively limited resources available, this approach is
practically useful for many DOT applications.