KEYWORDS: Image restoration, Feature extraction, Associative arrays, Data modeling, Brain tissue, Image processing, Magnetic resonance imaging, 3D modeling, Signal to noise ratio, Tissue optics
Diffuse optical tomography (DOT) guided by medical images can be used to achieve real-time reconstruction of subdural hematoma images for monitoring purposes. However, when the hematoma is irregular and the signal to noise ratio(SNR) of input signal is low, the reconstruction effect of this method is not ideal. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, we proposed a optical image mapping feature extraction method in the paper. The result of experiment shows that the mean relative volume error (VRE) of the hematoma reconstruction model optimized by the optical image mapping feature extraction method is only 0.79%, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0062mm-1. Compared with the model directly inputting optical information, the optical image mapping feature extraction method reduces the average VRE of the model by 93.3%, and the average AMD by 27.1%. This method provides a promising method for non-invasive continuous monitoring of clinical cerebral hematoma.
The stacked autoencoder (SAE) neural network applied to diffuse optical tomography (DOT) achieves accurate and stable detection of the position and size of tissue abnormality. The quality of modeling data influences the robustness and the accuracy of the model, the measurability of the model determines the effective range of the data cleaning method used in clinical practice. In order to determine the effective range of this method in clinical use, we analyze the measurability of anomaly detection based on DOT method. The analysis result is used as a priori information to clean the neural network sample data set used in this work. The results show that excluding the data outside the measurable range, the proposed method enables the network to achieve a prediction accuracy of 99% within the measurable range and achieves rapid and accurate detection of the position and size of abnormality in the tissue.
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