Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy.

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method.

Microscopy cell nuclei segmentation with enhanced U-Net.

Background: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams.

Deep learning-based mesoscopic fluorescence molecular tomography: an study.

Fluorescence molecular tomography (FMT), as well as mesoscopic FMT (MFMT) is widely employed to investigate molecular level processes or . However, acquiring depth-localized and less blurry reconstruction still remains challenging, especially when fluorophore (dye) is located within large scattering coefficient media. Herein, a two-stage deep learning-based three-dimensional (3-D) reconstruction algorithm is proposed.

Radiative transfer with delta-Eddington-type phase functions.

The radiative transfer equation (RTE) arises in a wide variety of applications, in particular, in biomedical imaging applications associated with the propagation of light through the biological tissue. However, highly forward-peaked scattering feature in a biological medium makes it very challenging to numerically solve the RTE problem accurately. One idea to overcome the difficulty associated with the highly forward-peaked scattering is through the use of a delta-Eddington phase function.

Dental optical tomography with upconversion nanoparticles-a feasibility study.

Upconversion nanoparticles (UCNPs) have the unique ability to emit multiple colors upon excitation by near-infrared (NIR) light. Herein, we investigate the potential use of UCNPs as contrast agents for dental optical tomography, with a focus on monitoring the status of fillings after dental restoration. The potential of performing tomographic imaging using UCNP emission of visible or NIR light is established.

Radiative transfer equation modeling by streamline diffusion modified continuous Galerkin method.

Optical tomography has a wide range of biomedical applications. Accurate prediction of photon transport in media is critical, as it directly affects the accuracy of the reconstructions. The radiative transfer equation (RTE) is the most accurate deterministic forward model, yet it has not been widely employed in practice due to the challenges in robust and efficient numerical implementations in high dimensions.

[Evaluation of heart impact in the 100 m extreme intensity sport using near-infrared non-invasive muscle oxygen detecting device and sports heart rate detection technology].

Using continuous two wavelength near-infrared technology to detect the variation in the consistency of oxygen hemoglobin in the muscle and the sports heart rate wireless real time collection technology, we devised the real time muscle tissue oxygenation and instantaneous heart rate experiment scheme and implemented it for the process of the 100 m run with two parameters given simultaneously. The experiment shows that the concentration of the oxygen hemoglobin in the muscle tissue continues decreasing after the end of the 100 m run, and the time interval between the moment when the concentration of the oxygen hemoglobin attains the minimum value and the moment when the athletes finish the 100 m run is (6.65 +/- 1.