Optical imaging and spectroscopy for the study of the human brain: status report

Neurophotonics, Год журнала: 2022, Номер 9(S2)

Опубликована: Авг. 30, 2022

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit novel methods to explore brain health function. While first focused on neurophotonic tools mostly applicable animal studies, here, we highlight optical spectroscopy imaging relevant noninvasive human studies. We outline current state-of-the-art technologies software advances, most recent impact these neuroscience clinical applications, identify areas where innovation needed, provide outlook for future directions.

Язык: Английский

---

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

Biomedical Optics Express, Год журнала: 2022, Номер 13(3), С. 1131 - 1131

Опубликована: Янв. 21, 2022

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used bedside perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived models were to identify high regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on head. Significant spatial variations in sensitivity, consistent subjects, found. also identified correlates of such differences suitable real-time assessment. These can be largely attributed changes extracerebral thickness and should taken into account optimize probe placement experimental settings.

Язык: Английский

---

Portable, high speed blood flow measurements enabled by long wavelength, interferometric diffuse correlation spectroscopy (LW-iDCS)

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Май 31, 2023

Diffuse correlation spectroscopy (DCS) is an optical technique that can be used to characterize blood flow in tissue. The measurement of cerebral hemodynamics has arisen as a promising use case for DCS, though traditional implementations DCS exhibit suboptimal signal-to-noise ratio (SNR) and sensitivity make robust measurements adults. In this work, we present long wavelength, interferometric (LW-iDCS), which combines the longer illumination wavelength (1064 nm), multi-speckle, detection, improve both SNR. Through direct comparison with based on superconducting nanowire single photon detectors, demonstrate approximate 5× improvement SNR over channel LW-DCS measured signals human subjects. We show equivalence extracted between LW-iDCS, feasibility LW-iDCS at 100 Hz source-detector separation 3.5 cm. This performance potential enable unlock novel cases diffuse spectroscopy.

Язык: Английский

---

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Journal of Biomedical Optics, Год журнала: 2024, Номер 29(01)

Опубликована: Янв. 27, 2024

SignificanceDiffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the flow index (BFi) derived through nonlinear least-square fitting measured intensity autocorrelation function (ACF). However, process computationally intensive, susceptible to measurement noise, and easily influenced by properties (absorption coefficient μa reduced scattering μs′) scalp skull thicknesses.AimWe aim develop data-driven method that enables rapid robust analysis of multiple-scattered light’s temporal ACFs. Moreover, proposed can be applied range source–detector distances instead being limited specific distance.ApproachWe present deep learning architecture with one-dimensional convolution neural networks, called DCS network (DCS-NET), BFi coherent factor (β) estimation. This DCS-NET was performed using simulated data based on three-layer brain model. We quantified impact from physiologically relevant property variations, layer thicknesses, realistic noise levels, multiple (5, 10, 15, 20, 25, 30 mm) β estimations among DCS-NET, semi-infinite, models.ResultsDCS-NET shows much faster speed, around 17,000-fold 32-fold than traditional semi-infinite models, respectively. It offers higher intrinsic sensitivity tissues compared methods. excellent anti-noise features less sensitive variations μs′ at separation mm. Also, we have demonstrated relative (rBFi) extracted lower error 8.35%. By contrast, models result in significant errors rBFi 43.76% 19.66%, respectively.ConclusionsDCS-NET robustly quantify measurements considerable distances, corresponding deeper biological tissues. has potential hardware implementation, promising continuous real-time measurements.

Язык: Английский

---

Microvascular cerebral blood flow response to intrathecal nicardipine is associated with delayed cerebral ischemia

Frontiers in Neurology, Год журнала: 2023, Номер 14

Опубликована: Март 17, 2023

One of the common complications non-traumatic subarachnoid hemorrhage (SAH) is delayed cerebral ischemia (DCI). Intrathecal (IT) administration nicardipine, a calcium channel blocker (CCB), upon detection large-artery vasospasm holds promise as treatment that reduces incidence DCI. In this observational study, we prospectively employed non-invasive optical modality called diffuse correlation spectroscopy (DCS) to quantify acute microvascular blood flow (CBF) response IT nicardipine (up 90 min) in 20 patients with medium-high grade SAH. On average, CBF increased significantly time post-administration. However, was heterogeneous across subjects. A latent class mixture model able classify 19 out into two distinct classes response: Class 1 (n = 6) showed no significant change CBF, while 2 13) pronounced increase nicardipine. The DCI 5 6 and 13 (p < 0.001). These results suggest (<90 DCS-measured associated intermediate-term 3 weeks) development

Язык: Английский

---

Non-invasive diffuse optical monitoring of cerebral physiology in an adult swine-model of impact traumatic brain injury

Biomedical Optics Express, Год журнала: 2023, Номер 14(6), С. 2432 - 2432

Опубликована: Апрель 13, 2023

In this study, we used diffuse optics to address the need for non-invasive, continuous monitoring of cerebral physiology following traumatic brain injury (TBI). We combined frequency-domain and broadband optical spectroscopy with correlation monitor oxygen metabolism, blood volume, water content in an established adult swine-model impact TBI. Cerebral was monitored before after TBI (up 14 days post injury). Overall, our results suggest that non-invasive can assess physiologic impairments post-TBI, including initial reduction development hemorrhage/hematoma, swelling.

Язык: Английский

---

A comprehensive overview of diffuse correlation spectroscopy: theoretical framework, recent advances in hardware, analysis, and applications

NeuroImage, Год журнала: 2024, Номер 298, С. 120793 - 120793

Опубликована: Авг. 15, 2024

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances sensors, lasers, and learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by already-complex theoretical framework but also broad range component options system architectures. To facilitate entry to this exciting field, we present comprehensive review hardware architectures (continuous-wave, frequency-domain, time-domain) summarize corresponding models. Further, discuss applications highly integrated silicon single-photon avalanche diode (SPAD) sensors DCS, compare SPADs with existing other components (lasers, correlators), as well data analysis tools, including learning. Potential medical diagnosis are discussed an outlook future directions provided, offer effective guidance embark on research.

Язык: Английский

---

Influence of source–detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Neurophotonics, Год журнала: 2022, Номер 9(03)

Опубликована: Июль 1, 2022

Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technology for bedside monitoring of cerebral blood flow. However, extracerebral hemodynamics can significantly influence DCS estimations perfusion. Advanced analytical models be used to remove the contribution hemodynamics; however, these are highly sensitive measurement noise. There a need empirical determination optimal source-detector separation(s) (SDS) that improves accuracy and reduces sensitivity noise in estimation flow with models. Aim: To determine SDS on solution uniqueness, accuracy, inaccuracies model parameters when using three-layer estimate DCS. Approach: We performed series silico simulations samples spanning wide range physiologically-relevant layer properties, thicknesses, Data were simulated at ranging from 0.5 3.0 cm diffusion equation (with without added) slab Monte Carlo simulations. quantified inverse model. Results: Two required ensure unique index (CBFi). Combinations 0.5/1.0/1.5 2.5 provide choice balancing depth penetration signal-to-noise ratio minimize error CBFi across varying dynamics. Conclusions: These results suggest critical minimizing estimated analyze data.

Язык: Английский

---

Fast Blood Flow Index Reconstruction of Diffuse Correlation Spectroscopy Using a Back-Propagation-Free Data-Driven Algorithm

Biomedical Optics Express, Год журнала: 2025, Номер 16(3), С. 1254 - 1254

Опубликована: Фев. 13, 2025

This study introduces a fast and accurate online training method for blood flow index (BFI) relative BFI (rBFI) reconstruction in diffuse correlation spectroscopy (DCS). We implement rigorous mathematical models to simulate the auto-correlation functions (g 2) semi-infinite homogeneous three-layer human brain models. implemented algorithm known as random vector functional link (RVFL) reconstruct from noisy g 2. extensively evaluated RVFL regarding both speed accuracy inference. Moreover, we compared with extreme learning machine (ELM) architecture, conventional convolutional neural network (CNN), three fitting algorithms. Results indicate that achieves higher than other algorithms, evidenced by comprehensive metrics. While offers comparable CNNs, it boosts speeds are 3900-fold faster inference 19.8-fold faster, enhancing its generalizability across different experimental settings. also used 2 one- Monte Carlo (MC)-based in-silico simulations, well analytical models, compare consistency of results obtained ELM. Furthermore, discuss how is more suitable embedded hardware due lower computational complexity ELM CNN

Язык: Английский

---

Influence of Tissue Curvature on the Absolute Quantification in Frequency-Domain Diffuse Optical Spectroscopy

Spectroscopy Journal, Год журнала: 2025, Номер 3(2), С. 14 - 14

Опубликована: Апрель 13, 2025

Accurate estimation of optical properties and hemodynamic parameters is critical for advancing frequency-domain diffuse spectroscopy (FD-DOS) techniques in clinical neuroscience. However, conventional FD-DOS models often assume planar air–tissue interfaces, introducing errors anatomically curved regions such as the forehead or infant heads. This study evaluates impact incorporating tissue curvature into forward analysis. Using simulations phantoms, we demonstrate that reduce absorption coefficient from 20% to less than 10% high-curvature scenarios. Within curvatures tested, even minor mismatches resulted significantly lower those observed approximations (p < 0.001). In low-curvature regions, yielded comparable (<5% both cases). When applied human data, our proposed model increased hemoglobin concentration estimates by 10–15% compared standard semi-infinite models, closer physiological expectations. Overall, these results quantitatively accounting improves accuracy property estimation. We propose a numerical framework achieves this fast reliable manner, robust tool research applications complex regions.

Язык: Английский

---