Computational models for functional near-infrared spectroscopy and imaging

Functional near-infrared spectroscopy (fNIRS) is a neuro-monitoring tool that is non-invasive, non-ionising, cost efficient, and portable. Its application for the traumatic brain injury patients is a well suggested approach due to its role in being able to continuously monitor key biomarkers such as the tissue oxygenation and blood haemoglobin level to understand the flow of blood supply to the tissue in the brain to assess injury in patients. In light of the great potential that fNIRS has to offer in neuro-monitoring in critical care, it is hindered by the inconsistency seldom seen in multiple research works that can be attributed to the assumptions made on tissue scattering properties to decouple their dependency along with absorption properties that can provide information about the key biomarkers useful in neuro-monitoring. These inconsistencies can also be attributed to the application of an inaccurate model to represent photon migration in underlying the biological tissue, or it can also be attributed to the unavoidable contamination of the measured fNIRS data by the superficial (skin and scalp) tissue, which is intended to probe the brain tissue, due to the typical placing of measurement probes on the head. The possibility to overcome these challenges in fNIRS methodology is examined in this thesis, and the proposed methods to overcome these are derived theoretically and validated on numerical simulation and experimental data to demonstrate better performance as compared to existing methods. A spectrally constrained approach is designed to efficiently circumvent the coupling of absorption and scattering properties to directly yield more accurate estimates of oxygenation levels for the cerebral tissue showing an average improvement of 6.6% as compared to a conventional and widely used approach of spatially resolved spectroscopy, in estimating the tissue oxygenation level. The uncertainty factor in the knowledge of scattering coefficient of the tissue, which is a key limitation in the conventional approach, is shown to be removed in the proposed spectrally constrained approach, therefore maintaining the methodology of subject and tissue-type independence. With the demonstration of better performance on spectral constrained approach, the role of more spectral information i.e., broadband intensity data, to allow recovery of more information is also explored and is demonstrated that when the data is measured on a complex tissue such as the human head, an often used simple semi-infinite model based layered recovery can lead to uncertain results, whereas, by using an appropriate model accounting for the tissue-boundary structure and geometry, the tissue oxygenation levels are recovered with an error of 4.2%, and brain depth with an error of 11.8%. The algorithm is finally used together with human subject data, to demonstrate the robustness in application and repeatability in the recovered parameters that adhere well to expected published parameters. Finally, the signal regression of fNIRS data to reduce superficial signal contamination which is well defined for a continuous wave (CW) fNIRS system is expanded to another data-types, namely phase data as used in frequency-domain (FD) fNIRS systems, by proposing a new approach for FD fNIRS that utilizes a short-separation intensity signal directly to regress both intensity and phase measurements. This is shown to provide a better regression of superficial signal contamination from both intensity and phase data-types. Intensity-based phase regression is shown to achieve a better suppression of superficial signal contamination by 68% whereas for phase-based phase regression the suppression is only by 13%. Phase-based phase regression is also shown to generate false-positives in the image reconstruction of haemodynamic activations from the cortex, which is not desirable and therefore this work provides a better methodology for minimizing the superficial signal contamination for FD fNIRS. All the parameter recovery models and signal processing methods presented in this work, in addition to their better performance that is shown, carry an additional and most prominent advantage of being able to be applied to all existing NIRS systems without any additional instrumentation or measurement for the purpose of providing a more accurate and robust neuro-monitoring tool

Hyper-spectral recovery of cerebral and extra-cerebral tissue properties using continuous wave near-infrared spectroscopic data

Near-infrared spectroscopy (NIRS) is widely used as a non-invasive method to monitor the hemodynamics of biological tissue. A common approach of NIRS relies on continuous wave (CW) methodology, i.e. utilizing intensity-only measurements, and, in general, assumes homogeneity in the optical properties of the biological tissue. However, in monitoring cerebral hemodynamics within humans, this assumption is not valid due to complex layered structure of the head. The NIRS signal that contains information about cerebral blood hemoglobin levels is also contaminated with extra-cerebral tissue hemodynamics, and any recovery method based on such a priori homogenous approximation would lead to erroneous results. In this work, utilization of hyper-spectral intensity only measurements (i.e., CW) at multiple distances are presented and are shown to recover two-layered tissue properties along with the thickness of top layer, using an analytical solution for a two-layered semi-infinite geometry. It is demonstrated that the recovery of tissue oxygenation index (TOI) of both layers can be achieved with an error of 4.4%, with the recovered tissue thickness of 4% error. When the data is measured on a complex tissue such as the human head, it is shown that the semi-infinite recovery model can lead to uncertain results, whereas, when using an appropriate model accounting for the tissue-boundary structure, the tissue oxygenation levels are recovered with an error of 4.2%, and the extra-cerebral tissue thickness with an error of 11.8%. The algorithm is finally used together with human subject data, demonstrating robustness in application and repeatability in the recovered parameters that adhere well to expected published parameters

Multi-laboratory performance assessment of diffuse optics instruments: the BitMap exercise

SIGNIFICANCE: Multi-laboratory initiatives are essential in performance assessment and standardization-crucial for bringing biophotonics to mature clinical use-to establish protocols and develop reference tissue phantoms that all will allow universal instrument comparison. AIM: The largest multi-laboratory comparison of performance assessment in near-infrared diffuse optics is presented, involving 28 instruments and 12 institutions on a total of eight experiments based on three consolidated protocols (BIP, MEDPHOT, and NEUROPT) as implemented on three kits of tissue phantoms. A total of 20 synthetic indicators were extracted from the dataset, some of them defined here anew. APPROACH: The exercise stems from the Innovative Training Network BitMap funded by the European Commission and expanded to include other European laboratories. A large variety of diffuse optics instruments were considered, based on different approaches (time domain/frequency domain/continuous wave), at various stages of maturity and designed for different applications (e.g., oximetry, spectroscopy, and imaging). RESULTS: This study highlights a substantial difference in hardware performances (e.g., nine decades in responsivity, four decades in dark count rate, and one decade in temporal resolution). Agreement in the estimates of homogeneous optical properties was within 12% of the median value for half of the systems, with a temporal stability of <5  %   over 1 h, and day-to-day reproducibility of <3  %  . Other tests encompassed linearity, crosstalk, uncertainty, and detection of optical inhomogeneities. CONCLUSIONS: This extensive multi-laboratory exercise provides a detailed assessment of near-infrared Diffuse optical instruments and can be used for reference grading. The dataset-available soon in an open data repository-can be evaluated in multiple ways, for instance, to compare different analysis tools or study the impact of hardware implementations

Spectrally constrained approach of spatially resolved spectroscopy:Towards a better estimate of tissue oxygenation

A method based on spatially resolved spectroscopy is developed, which directly recovers the scattering parameter and scaled chromophore concentrations from measured gradient of light-attenuation at multiple wavelengths. This method provides a more accurate estimate of tissue oxygenation, regardless of the unknown tissue scatter.</p

Spectral parameter recovery of cerebral and extra-cerebral tissues using broadband near-infrared spectroscopy

A continuous wave broadband near-infrared spectroscopy method is developed based on two-layered fitting of optical properties, to recover the haemoglobin concentrations and scattering parameters in cerebral and extra-cerebral tissues along with fitting for the extra-cerebral layer thickness. It is shown that tissue oxygenation for deep tissue and superficial tissue thickness is recovered with less than 4% error, whereas a homogeneous fit having an error of 15%.</p

The BitMap dataset: an open dataset on performance assessment of diffuse optics instruments

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