A Novel Stacked Denoising Autoencoder-Based Reconstruction Framework for Cerenkov Luminescence Tomography

Cerenkov luminescence tomography (CLT) is a promising imaging modality in the field of optical molecular imaging (OMI), which successfully bridges the OMI and tradition nuclear medical imaging and provides the location and quantitative analysis of the distribution of radionuclide probes inside the b...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 85178 - 85189
Main Authors: Cao, Xin, Wei, Xiao, Yan, Feng, Wang, Lin, Su, Linzhi, Hou, Yuqing, Geng, Guohua, He, Xiaowei
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Biological tissues
Cerenkov luminescence tomography
Experiments
Image reconstruction
Inverse problems
Luminescence
Medical imaging
Noise reduction
optical molecular imaging
Radioisotopes
Reconstruction
reconstruction algorithm
stacking denoising autoencoders
Tomography
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Cerenkov luminescence tomography (CLT) is a promising imaging modality in the field of optical molecular imaging (OMI), which successfully bridges the OMI and tradition nuclear medical imaging and provides the location and quantitative analysis of the distribution of radionuclide probes inside the biological objects. As the CLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Here, we proposed a novel reconstruction framework based on stacking denoising autoencoders (SDAE), which serve as one famous structure of the artificial neural network (ANN). In our framework, the initial permission region is the whole domain and then a traditional reconstruction algorithm is used to reconstruct each node's energy. Then these nodes are clustered into two regions: permission region and non-permission region, and the permission region is used to start a new reconstruction loop where a new result can be obtained. The procedures above are repeated before the result meets the stop conditions. The numerical simulation experiments, physical phantom experiments and in vivo experiments are all carried out to validate the feasibility and potential of our framework. Results demonstrate that the proposed framework can indeed achieve a good performance in CLT reconstruction.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2924042