Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography

BACKGROUND

Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15 mm, or (ii) source modulated light of order ∼100 MHz.

PURPOSE

However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated.

METHODS

Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85 000 point spread functions in each model.

RESULTS

We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9 mm closest spacing. Further, for a given array density, 300 MHz source modulation provided the deepest reliable imaging compared to other frequencies.

CONCLUSIONS

Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

Revisiting the Rytov approximation in diffuse optics and its applications for the inverse and forward problems

We propose an overview of the Rytov approximation in diffuse optics of biological tissues, for the inverse and forward problems. First, we show a physical interpretation of the Rytov approximation as a type of partial pathlength (named fluence rate partial pathlength) which is distinct from the usual partial pathlength for reflectance measurements. Second, we study the accuracy of the Rytov approximation for the calculation of Jacobians considering absorption perturbations and reflectance measurements. For higher absorption and lower reduced scattering values the discrepancy between the true Jacobian (i.e., the reflectance partial pathlength) and that obtained with the Rytov approximation (i.e., the fluence rate partial pathlength) can be up to about 70% for diffusion theory calculations and up to about 25% for Monte Carlo simulations. For higher reduced scattering values, the discrepancies become less than 10%. Third, we propose a calibration method that can circumvent numerical inaccuracies when the calculation of Jacobians is carried out in presence of highly absorbing layers. Finally, fourth, we also propose an original formula derived from the Rytov approximation for reflectance measurements, and we show how it performs for the forward problem, when we consider defects with large absorption contrast with respect to the background.

Sensitivity analysis of transabdominal fetal pulse oximetry using MRI-based simulations

Transabdominal fetal pulse oximetry offers a promising approach to improve fetal monitoring and reduce unnecessary interventions. Utilizing realistic 3D geometries derived from MRI scans of pregnant women, we conducted photon simulations to determine optimal source-detector configurations for detecting fetal heart rate and oxygenation. Our findings demonstrate the theoretical feasibility of measuring fetal signals at depths up to 30 mm using source-detector (SD) distances greater than 100 mm and wavelengths between 730 and 850 nm. Furthermore, we highlight the importance of customizing SD configurations based on fetal position and maternal anatomy. These insights pave the way for enhanced non-invasive fetal monitoring in clinical application.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Spatial Sensitivity to Absorption Changes for Various Near-Infrared Spectroscopy Methods: A Compendium Review

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media, particularly for measurements relevant to near-infrared spectroscopy. The three temporal domains, continuous-wave, frequency-domain, and time-domain, each obtain different optical data-types whose changes may be related to effective homogeneous changes in the absorption coefficient. Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change. Therefore, spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data-types in the three temporal domains. The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media. These works are experimental and theoretical, presenting 1-, 2-, and 3-dimensional sensitivity maps for different near-infrared spectroscopy methods, domains, and data-types. Following this history, we present a compendium of sensitivity maps organized by temporal domain and then data-type. This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document. Methods for one to generate these maps are provided in the appendix, including code. This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize, investigate, compare, and generate sensitivity to localized absorption change maps.

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.

Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography

Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., "silent") language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., "out loud") language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces.

Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy

Significance

Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance.

Aim

Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously.

Approach

The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT.

Results

The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation.

Conclusions

The SS-DOT model improves the fNIRS image reconstruction quality.

How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?

SIGNIFICANCE

Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF.

AIMS

We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM.

APPROACH

We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with "short-separation regression" designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests.

RESULTS

We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment.

CONCLUSIONS

Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

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.

Lock-in functional near-infrared spectroscopy for measurement of the haemodynamic brain response

Here we show a method of the lock-in amplifying near-infrared signals originating within a human brain. It implies using two 90-degree rotated source-detector pairs fixed on a head surface. Both pairs have a joint sensitivity region located towards the brain. A direct application of the lock-in technique on both signals results in amplifying common frequency components, e.g. related to brain cortex stimulation and attenuating the rest, including all components not related to the stimulation: e.g. pulse, instrumental and biological noise or movement artefacts. This is a self-driven method as no prior assumptions are needed and the noise model is provided by the interfering signals themselves. We show the theory (classical modified Beer-Lambert law and diffuse optical tomography approaches), the algorithm implementation and tests on a finite element mathematical model and in-vivo on healthy volunteers during visual cortex stimulation. The proposed hardware and algorithm complexity suit the entire spectrum of (continuous wave, frequency domain, time-resolved) near-infrared spectroscopy systems featuring real-time, direct, robust and low-noise brain activity registration tool. As such, this can be of special interest in optical brain computer interfaces and high reliability/stability monitors of tissue oxygenation.