Enhanced diffuse optical tomography image reconstruction with dilated convolution and densely connected network

Diffuse optical tomography (DOT) is a non-invasive imaging technique with promising biomedical applications. However, its reconstruction quality is severely limited by the ill-posed inverse problem, leading to low spatial resolution and quantitative accuracy. This study aims to improve DOT image reconstruction performance by enhancing multi-scale feature extraction and feature reuse through an advanced deep learning framework.

A deep neural network-based DOT reconstruction framework integrating dilated convolution and densely connected networks (DenseNet) is proposed. To systematically evaluate the contributions of dense connectivity and dilated convolution, four models involving ResNet, ResNet with dilation convolution (DResNet), DenseNet, and DenseNet with dilation convolution (DDenseNet) are constructed. Numerical simulations and physical phantom experiments are conducted under varying target sizes and absorption contrasts. Reconstruction performance was quantitatively assessed using Mean Absolute Error (MAE), Quantitativeness Ratio (QR), Contrast-to-Noise Ratio (CNR), and Structural Similarity Index Measure (SSIM).

Both simulation and phantom results demonstrate that the proposed DDenseNet consistently outperforms the other methods. It achieves the lowest Mean Absolute Error, the highest Contrast-to-Noise Ratio, and values of Quantitativeness Ratio and Structural Similarity Index Measure closest to 1.0 across different experimental conditions.

By combining dense connections with dilated convolution, the proposed DDenseNet effectively enhances multi-scale feature learning and reconstruction accuracy. This framework provides a robust and accurate solution for high-quality DOT imaging and shows strong potential for future clinical applications.