Narrowband diffuse reflectance spectroscopy in the 900-1000 nm wavelength region to quantify water and lipid content of turbid media

Advancing Patient Care: Innovative Use of Near-Infrared Spectroscopy for Monitoring Urine Volume in Neurogenic Bladder

PURPOSE

Current guidelines recommend clean intermittent catheterization (CIC) at regular time intervals for patients with spinal cord injuries; however, many patients experience difficulties. Performing time-based CIC outside the home is a significant burden for patients. In this study, we aimed to overcome the limitations of the current guidelines by developing a digital device to monitor bladder urine volume in real-time.

METHODS

The optode sensor is a near-infrared spectroscopy (NIRS)-based wearable device intended to be attached to the skin of the lower abdomen where the bladder is located. The sensor's primary function is to detect changes in urine volume within the bladder. An in vitro study was conducted using a bladder phantom that mimicked the optical properties of the lower abdomen. To validate the data in the human body at the proof-of-concept level, one volunteer attached the device to the lower abdomen to measure the light intensity between the first voiding and immediately before the second voiding.

RESULTS

The degree of attenuation at the maximum test volume was equivalent across experiments, and the optode sensor with multiplex measurements demonstrated robust performance for patient diversity. Moreover, the symmetric feature of the matrix was deemed a potential parameter for identifying the accuracy of sensor localization in a deep-learning model. The validated feasibility of the sensor showed almost the same results as an ultrasound scanner, which is routinely used in the clinical field.

CONCLUSION

The optode sensor of the NIRS-based wearable device can measure the urine volume in the bladder in real-time.

Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution

SIGNIFICANCE

Melanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive.

AIM

We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions. The model is structurally extensible for various light sources and cameras and maintains the input image resolution for medical applications.

APPROACH

A facial image is divided into multiple patches and decomposed into melanin, hemoglobin, shading, and specular maps. The outputs are reconstructed into a facial image by solving the forward problem over skin areas. As learning progresses, the difference between the reconstructed image and input image is reduced, resulting in the melanin and hemoglobin maps becoming closer to their distribution of the input image.

RESULTS

The proposed approach was evaluated on 30 subjects using the professional clinical system, VISIA VAESTRO. The correlation coefficients for melanin and hemoglobin were found to be 0.932 and 0.857, respectively. Additionally, this approach was applied to simulated images with varying amounts of melanin and hemoglobin.

CONCLUSION

The proposed approach showed high correlation with the clinical system for analyzing melanin and hemoglobin distribution, indicating its potential for accurate diagnosis. Further calibration studies using clinical equipment can enhance its diagnostic ability. The structurally extensible model makes it a promising tool for various image acquisition conditions.

Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region

SIGNIFICANCE

Water and lipid are key participants of many biological processes, but there are few label-free, non-contact optical methods that can spatially map these components in-vivo. Shortwave infrared meso-patterned imaging (SWIR-MPI) is an emerging technique that successfully addresses this need. However, it requires a dedicated SWIR camera to probe the 900- to 1300-nm wavelength region, which hinders practical translation of the technology.

AIM

Compared with SWIR-MPI, we aim to develop a new technique that can dramatically reduce the cost in detector while maintaining high accuracy for the quantification of tissue water and lipid content.

APPROACH

By utilizing water and lipid absorption features in the 900- to 1000-nm wavelength region as well as optimal wavelength and spatial frequency combinations, we develop a new imaging technique based on spatial frequency domain imaging to quantitatively map tissue water and lipid content using a regular silicon-based camera.

RESULTS

The proposed method is validated with a phantom study, which shows average error of 0.9 ± 1.2 % for water content estimation, and -0.4 ± 0.7 % for lipid content estimation, respectively. The proposed method is also demonstrated for ex vivo porcine tissue lipid mapping as well as in-vivo longitudinal water content monitoring.

CONCLUSIONS

The proposed technique enables spatial mapping of tissue water and lipid content with the cost in detector reduced by two orders of magnitude compared with SWIR-MPI while maintaining high accuracy. The experimental results highlight the potential of this technique for substantial impact in both scientific and industrial applications.