SignificanceLight emitting diodes (LEDs) are commonly utilized for tissue spectroscopy due to their small size, low cost, and simplicity. However, LEDs are often approximated as single-wavelength devices despite having relatively broad spectral bandwidths. When paired with photodiodes, the wavelength information of detected light cannot be resolved. This can result in errors during chromophore concentration calculations. These errors are particularly apparent when analyzing water and fat in the 900 to 1000 nm window where the spectral bandwidth of LEDs can encompass much of the analysis region, resulting in intense crosstalk.AimWe utilize and present a spectral correction (SC) algorithm to correct for the spectral bandwidth of LEDs. We show the efficacy using a narrowband technique of spectrally broad and overlapping LEDs.ApproachNarrowband diffuse reflectance spectroscopy (nb-DRS), a technique capable of quantifying the hydration ratio (RH2O) of turbid media, was utilized. nb-DRS typically requires a broadband light source and spectrometer. We reduce the hardware to just five LEDs and a photodiode detector, relying on SC to compensate for spectral crosstalk. The effectiveness of our SC approach was tested in simulations as well as in an emulsion phantom and limited selection of human tissue.ResultsIn simulations, we show that calculated RH2O errors increased with the spectral bandwidth of LEDs but could be corrected using SC. Likewise, in emulsions, we found an average error of 8.7% (maximum error 14%) if SC was not used. By contrast, applying SC reduced the average error to 2.2% (maximum error of 6.4%). We show that despite utilizing multiple, spectrally broad, and overlapping LEDs, SC was still able to restore the performance of our narrowband method, making it comparable to a much larger full broadband system.
Frequency-domain near infrared spectroscopy (fd-NIRS) is used to noninvasively characterize in vivo tissue structure and molecular composition by exploiting the deep tissue penetration of red and near-infrared light. However, the size, complexity, expense, and lack of scalability of current fd-NIRS hardware has slowed its translation to clinical applications. Here we present a broad-bandwidth 1.2 x 1.2 mm fd-NIRS application-specific integrated circuit that represents a critical step toward ultrasmall, easily scalable, and wearable fd-NIRS. We present the fd-NIRS integrated circuit design as well as results showing its optical property measurements are comparable to those measured with a standard reference system.
Significance: Diffuse optical spectroscopic imaging (DOSI) is a versatile technology sensitive to changes in tissue composition and hemodynamics and has been used for a wide variety of clinical applications. Specific applications have prompted the development of versions of the DOSI technology to fit specific clinical needs. This work describes the development and characterization of a multi-modal DOSI (MM-DOSI) system that can acquire metabolic, compositional, and pulsatile information at multiple penetration depths in a single hardware platform. Additionally, a 3D tracking system is integrated with MM-DOSI, which enables registration of the acquired data to the physical imaging area.
Aim: We demonstrate imaging, layered compositional analysis, and metabolism tracking capabilities using a single MM-DOSI system on optical phantoms as well as in vivo human tissue.
Approach: We characterize system performance with a silicone phantom containing an embedded object. To demonstrate multi-layer sensitivity, we imaged human calf tissue with a 4.8-mm skin-adipose thickness. Human thenar tissue was also measured using a combined broadband DOSI and continuous-wave near-infrared spectroscopy method (∼15 Hz acquisition rate).
Results: High-resolution optical property maps of absorption (μa) and reduced scattering (μs ′ ) were recovered on the phantom by capturing over 1000 measurement points in under 5 minutes. On human calf tissue, we show two probing depth layers have significantly different (p < 0.001) total-hemo/myoglobin and μs ′ composition. On thenar tissue, we calculate tissue arterial oxygen saturation, venous oxygen saturation, and tissue metabolic rate of oxygen consumption during baseline and after release of an arterial occlusion.
Conclusions: The MM-DOSI can switch between collection of broadband spectra, high-resolution images, or multi-depth hemodynamics without any hardware reconfiguration. We conclude that MM-DOSI enables acquisition of high resolution, multi-modal data consolidated in a single platform, which can provide a more comprehensive understanding of tissue hemodynamics and composition for a wide range of clinical applications.
Diffuse optical spectroscopic imaging (DOSI) and diffuse correlation spectroscopy (DCS) are model-based near-infrared (NIR) methods that measure tissue optical properties (broadband absorption, μa, and reduced scattering, μs′) and blood flow (blood flow index, BFI), respectively. DOSI-derived μa values are used to determine composition by calculating the tissue concentration of oxy- and deoxyhemoglobin (HbO2, HbR), water, and lipid. We developed and evaluated a combined, coregistered DOSI/DCS handheld probe for mapping and imaging these parameters. We show that uncertainties of 0.3 mm−1 (37%) in μs′ and 0.003 mm−1 (33%) in μa lead to ∼53% and 9% errors in BFI, respectively. DOSI/DCS imaging of a solid tissue-simulating flow phantom and a breast cancer patient reveals well-defined spatial distributions of BFI and composition that clearly delineates both the flow channel and the tumor. BFI reconstructed with DOSI-corrected μa and μs′ values had a tumor/normal contrast of 2.7, 50% higher than the contrast using commonly assumed fixed optical properties. In conclusion, spatially coregistered imaging of DOSI and DCS enhances intrinsic tumor contrast and information content. This is particularly important for imaging diseased tissues where there are significant spatial variations in μa and μs′ as well as potential uncoupling between flow and metabolism.
Near Infrared (NIR) optical spectroscopy and imaging technologies are increasingly applied to clinical and pre-clinical applications due to their strong metabolic sensitivity. These technologies rely upon calibrations to remove system artifacts. The most common calibration method requires a tissue-simulating phantom with known absorption (μa) and reduced scattering (μs’) coefficients to remove temporal and/or spatial artifacts from the measured data. While this method can be effective under certain conditions, there is no universal agreement on how to measure calibration phantom optical properties. An independent method for measuring calibration phantom broadband optical spectra is desired, especially without calibration with reference to known optical properties. We developed a broadband instrument that recovers absolute μa and μs’ spectra from 620 to 1050 nm in tissue-simulating phantoms. The instrument scans a supercontinuum laser beam across the phantom surface and collects diffuse reflectance via a spectrometercoupled optical fiber. A Monte Carlo algorithm was used to fit the reflectance as a function of source-detector separation and recover absorption and scattering spectra. The only calibration required is a measurement of the laser spot positions on the phantom surface; no optical properties need be known. Spectral constraints were used to create a robust fitting procedure. We demonstrate experimental repeatability by measuring the same phantom multiple times and recover absolute absorption and scattering spectra with ~10% precision across the entire NIR spectral region.
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