Selection of optimal wavelengths for spectral reconstruction in diffuse optical tomography

Wide-Field Optical Imaging in Mouse Models of Ischemic Stroke

Functional neuroimaging is a powerful tool for evaluating how local and global brain circuits evolve after focal ischemia and how these changes relate to functional recovery. For example, acutely after stroke, changes in functional brain organization relate to initial deficit and are predictive of recovery potential. During recovery, the reemergence and restoration of connections lost due to stroke correlate with recovery of function. Thus, information gleaned from functional neuroimaging can be used as a proxy for behavior and inform on the efficacy of interventional strategies designed to affect plasticity mechanisms after injury. And because these findings are consistently observed across species, bridge measurements can be made in animal models to enrich findings in human stroke populations. In mice, genetic engineering techniques have provided several new opportunities for extending optical neuroimaging methods to more direct measures of neuronal activity. These developments are especially useful in the context of stroke where neurovascular coupling can be altered, potentially limiting imaging measures based on hemodynamic activity alone. This chapter is designed to give an overview of functional wide-field optical imaging (WFOI) for applications in rodent models of stroke, primarily in the mouse. The goal is to provide a protocol for laboratories that want to incorporate an affordable functional neuroimaging assay into their current research thrusts, but perhaps lack the background knowledge or equipment for developing a new arm of research in their lab. Within, we offer a comprehensive guide developing and applying WFOI technology with the hope of facilitating accessibility of neuroimaging technology to other researchers in the stroke field.

Fourier transform acousto-optic imaging with off-axis holographic detection

Acousto-optic (AO) imaging is an in-depth optical imaging technique of highly scattering media. One challenging end-application for this technique is to perform imaging of living biological tissues. Indeed, because it relies on coherent illumination, AO imaging is sensitive to speckle decorrelation occurring on the millisecond time scale. Camera-based detections are well suited for in vivo imaging provided their integration time is lower than those decorrelation time scales. We present Fourier transform acousto-optic imaging combined with off-axis holography, which relies on plane waves and long-duration pulses. We demonstrate, for the first time to the best of our knowledge, a two-dimensional imaging system fully compatible with in vivo imaging prerequisites. The method is validated experimentally by performing in-depth imaging inside a multiple scattering sample.

Wavelength censoring for spectroscopy in optical functional neuroimaging

Optical neuromonitoring provides insight into neurovascular physiology and brain structure and function. These methods rely on spectroscopy to relate light absorption changes to variation of concentrations of physiologic chromophores such as oxy- and deoxyhemoglobin. In clinical or preclinical practice, data quality can vary significantly across wavelengths. In such situations, standard spectroscopic methods may perform poorly, resulting in data loss and limiting field-of-view. To address this issue, and thereby improve the robustness of optical neuromonitoring, we develop, in this manuscript, novel methods to perform spectroscopy even when data quality exhibits wavelength-dependent spatial variation. We sought to understand the impact of spatial, wavelength-based censoring on the physiologic accuracy and utility of hemoglobin spectroscopy. The principles of our analysis are quite general, but to make the methodology tangible we focused on optical intrinsic signal imaging of resting-state functional connectivity in mice. Starting with spectroscopy using four sources, all possible subset spectroscopy matrices were assessed theoretically, using simulated data, and using experimental data. These results were compared against the use of the full spectroscopy matrix to determine which subsets yielded robust results. Our results demonstrated that accurate calculation of changes in hemoglobin concentrations and the resulting functional connectivity network maps was possible even with censoring of some wavelengths. Additionally, we found that the use of changes in total hemoglobin (rather than oxy- or deoxyhemoglobin) yielded results more robust to experimental noise and allowed for the preservation of more data. This new and rigorous image processing method should improve the fidelity of clinical and preclinical functional neuroimaging studies.

Direct mapping from diffuse reflectance to chromophore concentrations in multi-fx spatial frequency domain imaging (SFDI) with a deep residual network (DRN)

Spatial frequency domain imaging (SFDI) is an emerging technology that enables label-free, non-contact, and wide-field mapping of tissue chromophore contents, such as oxy- and deoxy-hemoglobin concentrations. It has been shown that the use of more than two spatial frequencies (multi-fx ) can vastly improve measurement accuracy and reduce chromophore estimation uncertainties, but real-time multi-fx SFDI for chromophore monitoring has been limited in practice due to the slow speed of available chromophore inversion algorithms. Existing inversion algorithms have to first convert the multi-fx diffuse reflectance to optical absorptions, and then solve a set of linear equations to estimate chromophore concentrations. In this work, we present a deep learning framework, noted as a deep residual network (DRN), that is able to directly map from diffuse reflectance to chromophore concentrations. The proposed DRN is over 10x faster than the state-of-the-art method for chromophore inversion and enables 25x improvement on the frame rate for in vivo real-time oxygenation mapping. The proposed deep learning model will help enable real-time and highly accurate chromophore monitoring with multi-fx SFDI.

Measuring hemoglobin spectra: searching for carbamino-hemoglobin

SIGNIFICANCE

The arterial carbon dioxide (CO2) partial pressure PaCO2 is a clinically relevant variable. However, its measurement requires arterial blood sampling or bulky and expensive transcutaneous PtcCO2 meters. While the spectrophotometric determination of hemoglobin species-such as oxy-hemoglobin (O2Hb) and deoxy-hemoglobin (HHb)-allowed for the development of pulse oximetry, the measurement of CO2 blood content with minimal discomfort has not been addressed yet.

AIM

Characterizing human carbamino-hemoglobin (CO2Hb) absorption spectrum, which is missing from the literature. Providing the theoretical background that will allow for transcutaneous, noninvasive PaCO2 measurements.

APPROACH

A tonometry-based approach was used to obtain gas-equilibrated, lysed, diluted human blood. Equilibration was performed with both CO2, dinitrogen (N2), and ambient air. Spectrophotometric measurements were carried out on the 235- to 1000-nm range. A theoretical background was also derived from that of pulse oximetry.

RESULTS

The absorption spectra of both CO2Hb and HHb were extremely close and comparable with that of state-of-the-art HHb. The above-mentioned theoretical background led to an estimated relative error above 30% on the measured amount of CO2Hb in a subject's blood. Auxiliary measurements revealed that the use of ethylene diamine tetraacetic acid did not interfere with spectrophotometric measurements, whereas sodium metabisulfite did.

CONCLUSIONS

CO2Hb absorption spectrum was measured for the first time. Such spectrum being close to that of HHb, the use of a theoretical background based on pulse oximetry theory for noninvasive PaCO2 measurement seems extremely challenging.

Wavelength optimization using available laser diodes in spectral near-infrared optical tomography

For employing optimized wavelengths, a near-infrared (NIR) tomographic imaging system with multiwavelengths in a continuous wave (CW) enables us to provide accurate information of chromophores. In this paper, we discuss wavelength optimization with a selection from commercial laser diodes. Through theoretical analysis, the residual norm (R) and the condition number (κ) represent the uniqueness of a matrix problem and the smooth singular-value distribution of each chromophore, respectively. The optimum wavelengths take place for large R and small κ. We considered a total of 38 wavelengths of laser diodes in the range of 633-980 nm commercially available to discover optimum sets for a broad range of chromophore combinations. In the 38 wavelengths, there exists 501,942 (C538), 2,760,681 (C638), and 12,620,256 (C738) combinations of five, six, and seven wavelength sets, respectively, for accurately estimating chromophores (HbO₂, HbR, H₂O, and lipids), water, lipids, and the scattering prefactor A. With the numerical calculation, the top 10 wavelength sets were selected based on the principle of large R and small κ. In the study, the chromophore concentration for young and elderly women are investigated; finally, choosing the laser diodes with a wavelength of 650, 690, 705, 730, 870/880, 915, and 937 nm is recommended either for young or elderly women to construct a spectral NIR tomographic imaging system in the CW domain. Simulated data were used to validate the claims.

Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been show to be particularly well suited for ill-posed inverse problems such as the one of interest here. We report on reconstructions for multiple chromophores from simulated and experimental data where the PaLS method provides a more accurate estimation of chromophore concentrations compared to a pixel-based method.

Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography

In this study, we first propose the use of edge-preserving regularization in optimizing an ill-conditioned problem in the reconstruction procedure for diffuse optical tomography to prevent unwanted edge smoothing, which usually degrades the attributes of images for distinguishing tumors from background tissues when using Tikhonov regularization. In the edge-preserving regularization method presented here, a potential function with edge-preserving properties is introduced as a regularized term in an objective function. With the minimization of this proposed objective function, an iterative method to solve this optimization problem is presented in which half-quadratic regularization is introduced to simplify the minimization task. Both numerical and experimental data are employed to justify the proposed technique. The reconstruction results indicate that edge-preserving regularization provides a superior performance over Tikhonov regularization.

Hyperspectral image reconstruction for diffuse optical tomography

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.

Wavelength optimization for rapid chromophore mapping using spatial frequency domain imaging

Spatial frequency-domain imaging (SFDI) utilizes multiple-frequency structured illumination and model-based computation to generate two-dimensional maps of tissue absorption and scattering properties. SFDI absorption data are measured at multiple wavelengths and used to fit for the tissue concentration of intrinsic chromophores in each pixel. This is done with a priori knowledge of the basis spectra of common tissue chromophores, such as oxyhemoglobin (ctO(2)Hb), deoxyhemoglobin (ctHHb), water (ctH(2)O), and bulk lipid. The quality of in vivo SFDI fits for the hemoglobin parameters ctO(2)Hb and ctHHb is dependent on wavelength selection, fitting parameters, and acquisition rate. The latter is critical because SFDI acquisition time is up to six times longer than planar two-wavelength multispectral imaging due to projection of multiple-frequency spatial patterns. Thus, motion artifact during in vivo measurements compromises the quality of the reconstruction. Optimal wavelength selection is examined through matrix decomposition of basis spectra, simulation of data, and dynamic in vivo measurements of a human forearm during cuff occlusion. Fitting parameters that minimize cross-talk from additional tissue chromophores, such as water and lipid, are determined. On the basis of this work, a wavelength pair of 670 nm∕850 nm is determined to be the optimal two-wavelength combination for in vivo hemodynamic tissue measurements provided that assumptions for water and lipid fractions are made in the fitting process. In our SFDI case study, wavelength optimization reduces acquisition time over 30-fold to 1.5s compared to 50s for a full 34-wavelength acquisition. The wavelength optimization enables dynamic imaging of arterial occlusions with improved spatial resolution due to reduction of motion artifacts.

Diffuse Optics for Tissue Monitoring and Tomography

This review describes the diffusion model for light transport in tissues and the medical applications of diffuse light. Diffuse optics is particularly useful for measurement of tissue hemodynamics, wherein quantitative assessment of oxy- and deoxy-hemoglobin concentrations and blood flow are desired. The theoretical basis for near-infrared or diffuse optical spectroscopy (NIRS or DOS, respectively) is developed, and the basic elements of diffuse optical tomography (DOT) are outlined. We also discuss diffuse correlation spectroscopy (DCS), a technique whereby temporal correlation functions of diffusing light are transported through tissue and are used to measure blood flow. Essential instrumentation is described, and representative brain and breast functional imaging and monitoring results illustrate the workings of these new tissue diagnostics.