GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images

The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images

Estimation of skin optical parameters for real-time hyperspectral imaging applications

Hyperspectral imaging combines high spectral and spatial resolution in one modality. This imaging technique is a promising tool for objective medical diagnostics. However, to be attractive in a clinical setting, the technique needs to be fast and accurate. Hyperspectral imaging can be used to analyze tissue properties using spectroscopic methods, and is thus useful as a general purpose diagnostic tool. We combine an analytic diffusion model for photon transport with real-time analysis of the hyperspectral images. This is achieved by parallelizing the inverse photon transport model on a graphics processing unit to yield optical parameters from diffuse reflectance spectra. The validity of this approach was verified by Monte Carlo simulations. Hyperspectral images of human skin in the wavelength range 400–1000 nm, with a spectral resolution of 3.6 nm and 1600 pixels across the field of view (Hyspex VNIR-1600), were used to develop the presented approach. The implemented algorithm was found to output optical properties at a speed of 3.5 ms per line of image data. The presented method is thus capable of meeting the defined real-time requirement, which was 30 ms per line of data.The algorithm is a proof of principle, which will be further developed

Pulsed photothermal temperature profiling of agar tissue phantoms.

We determine experimentally the accuracy of pulsed photothermal radiometric (PPTR) temperature depth profiling in water-based samples. We use custom tissue phantoms composed of agar gel layers separated by very thin absorbing layers. Two configurations of the acquisition system are compared, one using the customary spectral band of the InSb radiation detector (3.0-5.5 microm) and the other with a spectrally narrowed acquisition band (4.5-5.5 microm). The laser-induced temperature depth profiles are reconstructed from measured radiometric signals using a custom minimization algorithm. The results correlate very well with phantom geometry as determined by optical coherence tomography (OCT) and histology in all evaluated samples. Determination of the absorbing layer depth shows good repeatability with spatial resolution decreasing with depth. Spectral filtering improves the accuracy and resolution, especially for shallow absorption layers (~120 microm) and more complex structures (e.g., with two absorbing layers). The average full width at half maximum (FWHM) of the temperature peaks equals 23% of the layer depth

Spectral filtering for improved pulsed photothermal temperature profiling in agar tissue phantoms.

We present a systematic experimental comparison of pulsed photothermal temperature profiling utilizing the customary spectral band of the InSb radiation detector (lambda=3.0 to 5.6 microm) and a narrowed acquisition band (4.5 to 5.6 microm). We use custom tissue phantoms composed of agar gel layers separated by thin absorbing layers. The laser-induced temperature profiles are reconstructed within the customary monochromatic approximation, using a custom minimization algorithm. In a detailed numerical simulation of the experimental procedure, we consider several acquisition spectral bands with the lower wavelength limit varied between 3.0 and 5.0 microm (imitating application of different long-pass filters). The simulated PPTR signals contain noise with amplitude and spectral characteristics consistent with our experimental system. Both experimental and numerical results indicate that spectral filtering reduces reconstruction error and broadening of temperature peaks, especially for shallower and more complex absorbing structures. For the simulated PPTR system and watery tissues, numerical results indicate an optimal lower wavelength limit of 3.8 to 4.2 microm

Pulsed photothermal temperature profiling of agar tissue phantoms.

We determine experimentally the accuracy of pulsed photothermal radiometric (PPTR) temperature depth profiling in water-based samples. We use custom tissue phantoms composed of agar gel layers separated by very thin absorbing layers. Two configurations of the acquisition system are compared, one using the customary spectral band of the InSb radiation detector (3.0-5.5 microm) and the other with a spectrally narrowed acquisition band (4.5-5.5 microm). The laser-induced temperature depth profiles are reconstructed from measured radiometric signals using a custom minimization algorithm. The results correlate very well with phantom geometry as determined by optical coherence tomography (OCT) and histology in all evaluated samples. Determination of the absorbing layer depth shows good repeatability with spatial resolution decreasing with depth. Spectral filtering improves the accuracy and resolution, especially for shallow absorption layers (~120 microm) and more complex structures (e.g., with two absorbing layers). The average full width at half maximum (FWHM) of the temperature peaks equals 23% of the layer depth

Imaging microvascular changes in nonocular oncological clinical applications by optical coherence tomography angiography: a literature review

Optical coherence tomography angiography (OCTA) is an emerging imaging modality that enables noninvasive visualization and analysis of tumor vasculature. OCTA has been particularly useful in clinical ocular oncology, while in this article, we evaluated OCTA in assessing microvascular changes in clinical nonocular oncology through a systematic review of the literature

Hyperspectral imaging for detection of arthritis: Feasibility and prospects

Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients’ quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints

Suitability of diffusion approximation for an inverse analysis of diffuse reflectance spectra from human skin in vivo

Diffusion approximation (DA) of the radiative transport equation allows derivation of enclosed solutions for diffuse reflectance from multi-layer scattering structures, such as human skin. Although the DA is known to be inadequate near tissue boundaries and light sources, analytical tractability makes such solutions very attractive for use in noninvasive characterization of biological organs based on measured diffuse reflectance spectra (DRS). For the presented three-layer model of human skin, which enables a good match with DRS in visible spectral range measured with an integrating sphere, the DA solutions systematically overshoot numerically simulated DRS (using Monte Carlo approach) by 1–2 percentage points. However, using the former in inverse analysis of the latter can result in much larger artifacts, most notably overestimations of the melanin and blood contents by up to 15%, which must be considered when analyzing experimental DRS. Despite such systematic errors, the described approach allows simple and robust monitoring of physiological changes in human skin, as demonstrated in tests involving temporary obstruction of blood circulation and seasonal variations due to extensive sun exposure

Wavelet based feature extraction and visualization in hyperspectral tissue characterization

Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p <0.02) between the chosen tissue parameters and the selected wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition.© 2014 Optical Society of Americ