Diffuse optical tomography (DOT) has shown promise in biomedical research, such as breast cancer diagnostics and brain imaging, by reconstructing hidden objects within scattering media. However, the conventional reconstruction framework faces challenges due to the highly ill-posed inverse problem of reconstructing optical properties. This work introduces a novel approach, neural field-based diffuse optical tomography (NeuDOT), which leverages a multi-layer perceptron (MLP) to learn an implicit function that maps spatial coordinates to their corresponding optical absorption coefficients. The performance of the NeuDOT method has been evaluated through several phantom studies, demonstrating its potential for high spatial resolution DOT reconstruction
Macroscopic-level diffuse optical imaging has been widely used in small animal imaging for preclinical research. Due to severe light scattering, 3D reconstruction in diffuse optics is highly ill-posed and sensitive to small noise in measurement. Bringing prior information such as the inner structural or surface information of the imaging object may largely reduce the ill-posed nature of the inverse problem and improve the reconstruction accuracy. Most existing solutions use additional equipment or multimodal techniques (e.g., CT, MRI, etc.). However, these methods pose new challenges such as increased cost and image alignment between different modalities. Herein, we present a novel compact optical tomography system that enables surface extraction using a single programmable scanning module and pinhole modeling. Experiments on phantom and mice show that the system is capable of achieving high-fidelity surface extraction with a minimal error of less than 0.1 mm, which in turn improves the accuracy of 3D fluorescence reconstruction
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