High-density diffuse optical tomography for imaging human brain function

This review describes the unique opportunities and challenges for noninvasive optical mapping of human brain function. Diffuse optical methods offer safe, portable, and radiation free alternatives to traditional technologies like positron emission tomography or functional magnetic resonance imaging (fMRI). Recent developments in high-density diffuse optical tomography (HD-DOT) have demonstrated capabilities for mapping human cortical brain function over an extended field of view with image quality approaching that of fMRI. In this review, we cover fundamental principles of the diffusion of near infrared light in biological tissue. We discuss the challenges involved in the HD-DOT system design and implementation that must be overcome to acquire the signal-to-noise necessary to measure and locate brain function at the depth of the cortex. We discuss strategies for validation of the sensitivity, specificity, and reliability of HD-DOT acquired maps of cortical brain function. We then provide a brief overview of some clinical applications of HD-DOT. Though diffuse optical measurements of neurophysiology have existed for several decades, tremendous opportunity remains to advance optical imaging of brain function to address a crucial niche in basic and clinical neuroscience: that of bedside and minimally constrained high fidelity imaging of brain function

Evaluation of rigid registration methods for whole head imaging in diffuse optical tomography

Functional brain imaging has become an important neuroimaging technique for the study of brain organization and development. Compared to other imaging techniques, diffuse optical tomography (DOT) is a portable and low-cost technique that can be applied to infants and hospitalized patients using an atlas-based light model. For DOT imaging, the accuracy of the forward model has a direct effect on the resulting recovered brain function within a field of view and so the accuracy of the spatially normalized atlas-based forward models must be evaluated. Herein, the accuracy of atlas-based DOT is evaluated on models that are spatially normalized via a number of different rigid registration methods on 24 subjects. A multileveled approach is developed to evaluate the correlation of the geometrical and sensitivity accuracies across the full field of view as well as within specific functional subregions. Results demonstrate that different registration methods are optimal for recovery of different sets of functional brain regions. However, the “nearest point to point” registration method, based on the EEG 19 landmark system, is shown to be the most appropriate registration method for image quality throughout the field of view of the high-density cap that covers the whole of the optically accessible cortex

Multi-modulated frequency domain high density diffuse optical tomography

Frequency domain (FD) high density diffuse optical tomography (HD-DOT) utilising varying or combined modulation frequencies (mFD) has shown to theoretically improve the imaging accuracy as compared to conventional continuous wave (CW) measurements. Using intensity and phase data from a solid inhomogeneous phantom (NEUROPT) with three insertable rods containing different contrast anomalies, at modulation frequencies of 78 MHz, 141 MHz and 203 MHz, HD-DOT is applied and quantitatively evaluated, showing that mFD outperforms FD and CW for both absolute (iterative) and temporal (linear) tomographic imaging. The localization error (LOCA), full width half maximum (FWHM) and effective resolution (ERES) were evaluated. Across all rods, the LOCA of mFD was 61.3% better than FD and 106.1% better than CW. For FWHM, CW was 6.0% better than FD and mFD and for ERES, mFD was 1.20% better than FD and 9.83% better than CW. Using mFD data is shown to minimize the effect of inherently noisier FD phase data whilst maximising its strengths through improved contrast

Lightweight high-density diffuse optical tomography using sCMOS detection

The widespread adoption of optical neuroimaging has been restricted by the tradeoff between cap wearability and brain coverage [1]. Increased coverage requires more fibers and larger imaging consoles, however these changes drastically reduce the wearability of the imaging cap and the portability of the entire system. The size of the detection fibers, which is driven by signal-to-noise considerations, is the primary obstacle to fabricating more wearable and portable optical neuroimaging arrays. Here we report on a design that leverages the low-noise of scientific CMOS cameras, along with binning and noise reduction algorithms to use fibers with approximately 30x smaller cross-sectional area than current high-density diffuse optical tomography (HD-DOT) systems [2]. We have developed a Super-Pixel sCMOS Diffuse Optical Tomography (SP-DOT) system (Fig. 1a) that uses 200um diameter source and detector fibers, with a lightweight low-profile, wearable design. A super-pixel algorithm leverages pixel binning to provide dynamic range (DNR), Noise Equivalent Power (NEP), and cross- talk (CT) specifications comparable to previous HD-DOT [2]. We have demonstrated retinotopic mapping with a SP-DOT system (Fig. 1). The system has a high DNR (\u3e105), high frame rate (\u3e6Hz) and low NEP (\u3c 9fW/√Hz). Please click Additional Files below to see the full abstract

Singular value decomposition based regularization prior to spectral mixing improves crosstalk in dynamic imaging using spectral diffuse optical tomography

The spectrally constrained diffuse optical tomography (DOT) method relies on incorporating spectral prior information directly into the image reconstruction algorithm, thereby correlating the underlying optical properties across multiple wavelengths. Although this method has been shown to provide a solution that is stable, the use of conventional Tikhonov-type regularization techniques can lead to additional crosstalk between parameters, particularly in linear, single-step dynamic imaging applications. This is due mainly to the suboptimal regularization of the spectral Jacobian matrix, which smoothes not only the image-data space, but also the spectral mapping space. In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters. Using simulated data, images of changes in oxygenated and deoxygenated hemoglobin concentrations are reconstructed via the SVD-based approach and compared with images reconstructed by using non-spectral and conventional spectral methods. In a 2D, two wavelength example, it is shown that the proposed approach provides a 98% reduction in crosstalk between recovered parameters as compared with conventional spectral reconstruction algorithms, and 60% as compared with non-spectrally constrained algorithms. Using a subject specific multilayered model of the human head, a noiseless dynamic simulation of cortical activation is performed to further demonstrate such improvement in crosstalk. However, with the addition of realistic noise in the data, both non-spectral and proposed algorithms perform similarly, indicating that the use of spectrally constrained reconstruction algorithms in dynamic DOT may be limited by the contrast of the signal as well as the noise characteristics of the system

Image Quality Analysis of High-Density Diffuse Optical Tomography Incorporating a Subject-Specific Head Model

High-density diffuse optical tomography (HD-DOT) methods have shown significant improvement in localization accuracy and image resolution compared to traditional topographic near infrared spectroscopy of the human brain. In this work we provide a comprehensive evaluation of image quality in visual cortex mapping via a simulation study with the use of an anatomical head model derived from MRI data of a human subject. A model of individual head anatomy provides the surface shape and internal structure that allow for the construction of a more realistic physical model for the forward problem, as well as the use of a structural constraint in the inverse problem. The HD-DOT model utilized here incorporates multiple source-detector separations with continuous-wave data with added noise based on experimental results. To evaluate image quality we quantify the localization error and localized volume at half maximum (LVHM) throughout a region of interest within the visual cortex and systematically analyze the use of whole-brain tissue spatial constraint within image reconstruction. Our results demonstrate that an image quality with less than 10 mm in localization error and 1000 m3 in LVHM can be obtained up to 13 mm below the scalp surface with a typical unconstrained reconstruction and up to 18 mm deep when a whole-brain spatial constraint based on the brain tissue is utilized