Acta Photonica Sinica, Volume. 51, Issue 12, 1217001(2022)
Diffuse Optical Tomography Method Based on Multi-information Fusion and Stacked Auto-encoder Network
Diffuse Optical Tomography (DOT) is a new functional optical imaging technique that demonstrates the potential applications in breast tumor imaging and functional brain imaging. It has the advantages of being non-invasive, non-ionizing radiation, and providing physiological information about biological tissues. The image reconstruction process of DOT, that is, the inverse problem, is ill-posed, resulting in a low degree of quantification and spatial resolution of the reconstructed image. Traditional DOT image reconstruction methods cannot completely solve the problem of low imaging accuracy, mainly reconstruct the target with a regular shape (circle). In recent years, artificial neural networks have been widely used in the field of image reconstruction with their strong feature extraction and recognition capabilities. In this paper, Stacked Auto-Encoder (SAE) neural network is proposed to improve the DOT image quality. SAE is a relatively simple model where fewer network parameters need to be adjusted, and therefore the training speed is fast. SAE network consists of two autoencoders, which are unsupervised learning neural network models, including encoder and decoder. The encoder works by extracting the features of the input data to the hidden layer, while the decoder reconstructs the input data from the hidden layer. A fully connected layer after the autoencoder is added as the output layer of SAE. The SAE network training process consists of two stages. The first stage is unsupervised pre-training for getting initial weights and bias values. The second stage is that based on the principle of error backpropagation, the network minimizes the loss function by calculating the mean squared error between the predicted output value and the expected output value, then optimizes the weights and biases in the network model, and finally reconstructs the optical parameter image. For practical applications, anatomical prior information incorporated into SAE neural network is utilized to reconstruct DOT images with the targets of different shapes (circle and ellipse). To simulate the tumor in breast tissues, a two-dimensional circular phantom with a radius of 40 mm is used to simulate background tissue, and the circular and elliptical targets are respectively embedded in the background phantom to simulate breast tumors. Sixteen coaxial source-detector optodes are equally arranged around the phantom boundary. For each illumination, except the 4 detectors nearest to the source position, the remaining 11 detectors are used to measure the light intensities on the boundary, leading to a total of 16×11 (176) measurements. To more closely simulate the real case, 40 dB noise is added to the measurement data. Based on the numerical optical phantoms, the corresponding MRI images with the size of 51
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Zhilong SUN, Jie ZHANG, Zongyang LIU, Feng GAO, Limin ZHANG. Diffuse Optical Tomography Method Based on Multi-information Fusion and Stacked Auto-encoder Network[J]. Acta Photonica Sinica, 2022, 51(12): 1217001
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Received: May. 13, 2022
Accepted: Jun. 22, 2022
Published Online: Feb. 6, 2023
The Author Email: ZHANG Limin (zhanglm@tju.edu.cn)