In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fast acquisition time, and be easy to use. Since many tissues cannot be penetrated by near-infrared light, such tissue needs to be measured in reflection mode, i.e., where light emission and detection components are placed on the same side. Thanks to the recent advance in single-photon avalanche diode (SPAD) array technology, we have developed a compact reflection-mode time-domain (TD) NIROT system with a large number of channels, which is expected to substantially increase the resolution and depth sensitivity of the oxygenation images. The aim was to test this experimentally for our SPAD camera-empowered TD NIROT system. Experiments with one and two inclusions, i.e., optically dense spheres of 5mm radius, immersed in turbid liquid were conducted. The inclusions were placed at depths from 10mm to 30mm and moved across the field-of-view. In the two-inclusion experiment, two identical spheres were placed at a lateral distance of 8mm. We also compared short exposure times of 1s, suitable for dynamic processes, with a long exposure of 100s. Additionally, we imaged complex geometries inside the turbid medium, which represented structural elements of a biological object. The quality of the reconstructed images was quantified by the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and dice similarity. The two small spheres were successfully resolved up to a depth of 30mm. We demonstrated robust image reconstruction even at 1s exposure. Furthermore, the complex geometries were also successfully reconstructed. The results demonstrated a groundbreaking level of enhanced performance of the NIROT system based on a SPAD camera.

Near infrared optical tomography (NIROT) a promising imaging modality for early detection of oxygenation in the brain of preterm infants requires data acquisition at the tissue surface and thus an image reconstruction adaptable to cephalometric variations and surface topologies. Widely used model-based reconstruction methods come with the drawback of a huge computational cost. Neural networks move this computational load to an offline training phase, allowing a much faster reconstruction. Our aim is a data-driven volumetric image reconstruction that generalizes well to different surfaces, increases reconstruction speed, localization accuracy and image quality. We propose a hybrid convolutional neural network (hCNN) based on the well-known V-net architecture to learn inclusion localization and absorption coefficients of heterogenous arbitrary shapes via a joint cost function. We achieved an average reconstruction time of 30.45 seconds, a time reduction of 89% and inclusion detection with an average Dice score of 0.61. The CNN is flexible to surface topologies, compares well in quantitative metrics with the traditional model-based (MB) approach and state-of-the-art neuronal networks for NIROT. The proposed hCNN was successfully trained, validated, and tested on in-silico data, excels MB methods in localization accuracy and provides a remarkable increase in reconstruction speed.

In near-infrared spectroscopy (NIRS), it is crucial to have an accurate and realistic model of photon transport in the adult head for obtaining accurate brain oxygenation values. There are several studies on the influence of thickness, the morphology of extracerebral layers, and source-detector distance on the sensitivity of NIRS to the brain. However, the optical properties of the different layers vary between different publications. How is the performance of NIRS affected, when the real optical properties differ from the assumed ones? We aim to investigate the influence of variation in scattering and absorption in a five-layered head model (scalp, skull, CSF, grey and white matter). We performed Monte Carlo simulations focusing on a five-layered slab mesh. The range of optical properties is based on a review of the published literature. We assessed the effect on light propagation by measuring the difference in the mean partial path lengths, attenuation, and the number of the detected photons between the different optical properties performing Monte Carlo simulations. For changes in the reduced scattering, we found that the upper layers tend to have a negative impact. In contrast, changes in lower layers tend to impact the brain's influence metrics positively. Furthermore, for small source-detector distances, the relative percentage difference between lower and higher values is greater than larger distances. Conclusions: We conclude that the assumption of different optical properties has a substantial effect on the sensitivity to the brain. This means that it is important to determine the correct optical properties for NIRS measurements in vitro and in vivo.