Diffuse optical tomography (DOT) has emerged over the past few decades as a non-invasive imaging tool to quantitatively assess deep tissue's functional and anatomical information. It has seen widespread use in various preclinical and clinical research fields, leading to a cumulative understanding of the technique and its potential applications. Over the years, the field of diffuse optics has encountered increasingly complex limitations, including ill-posedness, processing time, limited optodes density, etc., giving rise to novel and more sophisticated developments on the theoretical, algorithmic, computational, and instrumentation levels. In this chapter, we aim to present the theoretical basis of near-infrared diffuse optical tomography and diffuse correlation tomography. We introduce the state-of-the-art in computational and algorithmic perspectives, which seeks to improve the spatial resolution of reconstructed images while concurrently reducing the computational burden of solving high-dimensional inverse problems. We conclude by providing a survey of the most relevant applications of DOT currently undergoing clinical testing.
Diffuse optical tomography (DOT) has been investigated for diagnosing malignant breast lesions, but its accuracy relies on model-based image reconstructions, which in turn depends on the accuracy of breast shape acquisition. In this work, we have developed a dual-camera structured light imaging (SLI) breast shape acquisition system tailored for a mammography-like compression setting. Illumination pattern intensity is dynamically adjusted to account for skin tone differences, while thickness-informed pattern masking reduces artifacts due to specular reflections. This compact system is affixed to a rigid mount that can be installed into existing mammography or parallel-plate DOT systems without the need for camera-projector re-calibration. Our SLI system produces sub-millimeter resolution with a mean surface error of 0.26 mm. This breast shape acquisition system results in more accurate surface recovery, with an average 1.6-fold reduction in surface estimation errors over a reference method via contour extrusion. Such improvement translates to 25% to 50% reduction in mean squared error in the recovered absorption coefficient for a series of simulated tumors 1-2 cm below the skin.
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