Approaches to denoise the diffuse optical signals for tissue blood flow measurement

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

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

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

Significance

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ a and reduced scattering coefficient μ s ' ) and scalp and skull thicknesses.

Aim

We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance.

Approach

We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β ) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.

Results

DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ a and μ s ' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.

Conclusions

DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques

Diffuse correlation spectroscopy is a non-invasive optical modality used to measure cerebral blood flow in real time, and it has important potential applications in clinical monitoring and neuroscience. As such, many research groups have recently been investigating methods to improve the signal-to-noise ratio, imaging depth, and spatial resolution of diffuse correlation spectroscopy. Such methods have included multispeckle, long wavelength, interferometric, depth discrimination, time-of-flight resolution, and acousto-optic detection strategies. In this review, we exhaustively appraise this plethora of recent advances, which can be used to assess limitations and guide innovation for future implementations of diffuse correlation spectroscopy that will harness technological improvements in the years to come.

Correlation and asynchronization of electroencephalogram and cerebral blood flow in active and passive stimulations

Objective.Real-time brain monitoring is of importance for intraoperative surgeries and intensive care unit, in order to take timely clinical interventions. Electroencephalogram (EEG) is a conventional technique for recording neural excitations (e.g. brain waves) in the cerebral cortex, and near infrared diffuse correlation spectroscopy (DCS) is an emerging technique that can directly measure the cerebral blood flow (CBF) in microvasculature system. Currently, the relationship between the neural activities and cerebral hemodynamics that reflects the vasoconstriction features of cerebral vessels, especially under both active and passive situation, has not been elucidated thus far, which triggers the motivation of this study.Approach.We used the verbal fluency test as an active cognitive stimulus to the brain, and we manipulated blood pressure changes as a passive challenge to the brain. Under both protocols, the CBF and EEG responses were longitudinally monitored throughout the cerebral stimulus. Power spectrum approaches were applied the EEG signals and compared with CBF responses.Main results.The results show that the EEG response was significantly faster and larger in amplitude during the active cognitive task, when compared to the CBF, but with larger individual variability. By contrast, CBF is more sensitive when response to the passive task, and with better signal stability. We also found that there was a correlation (p< 0.01,r= 0.866,R2= 0.751) between CBF and EEG in initial response during the active task, but no significant correlation (p> 0.05) was found during the passive task. The similar relations were also found between regional brain waves and blood flow.Significance.The asynchronization and correlation between the two measurements indicates the necessity of monitoring both variables for comprehensive understanding of cerebral physiology. Deep exploration of their relationships provides promising implications for DCS/EEG integration in the diagnosis of various neurovascular and psychiatric diseases.

Comprehensive investigations of cerebral hemodynamic responses in CSVD patients with mental disorders: a pilot study

Although a portion of patients with cerebral small vessel disease (CSVD) present mental disorders, there is currently a lack of appropriate technologies to evaluate brain functions that are relevant to neurovascular coupling. Furthermore, there are no established objective criteria for diagnosing and distinguishing CSVD-induced mental disorders and psychiatric diseases. In this study, we report the first comprehensive investigation of the cerebral hemodynamics of CSVD patients who also presented with mental disorders. Two CSVD patients with similar magnetic resonance imaging (MRI) outcomes but with non-identical mental symptoms participated in this study. The patients were instructed to perform the verbal fluency task (VFT), high-level cognition task (HCT), as well as voluntary breath holding (VBH). A functional near-infrared spectroscopy (fNIRS) was used to measure the cerebral oxygenation responses. Additionally, a diffuse correlation spectroscopy (DCS) was used to measure the cerebral blood flow (CBF) responses. Both technologies were also applied to a healthy subject for comparison. The fNIRS results showed that both CSVD patients presented abnormal cerebral oxygenation responses during the VFT, HCT, and VBH tasks. Moreover, the patient with cognition impairment showed fluctuations in CBF during these tasks. In contrast, the patient without cognition impairment mostly presented typical CBF responses during the tasks, which was consistent with the healthy subject. The cognitive impairment in CSVD patients may be due to the decoupling of the neurons from the cerebrovascular, subsequently affecting the autoregulation capacity. The results of the fNIRS and DCS combined provide a comprehensive evaluation of the neurovascular coupling and, hence, offer great potential in diagnosing cerebrovascular or psychiatric diseases.

Quantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architecture

Diffuse correlation spectroscopy (DCS) is a noninvasive technique that derives blood flow information from measurements of the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variation was demonstrated to be approximately proportional to absolute blood flow. We investigated and assessed the utility of a long short-term memory (LSTM) architecture for quantification of BFI in DCS. Phantom and in vivo experiments were established to measure normalized intensity autocorrelation function data. Improved accuracy and faster computational time were gained by the proposed LSTM architecture. The results support the notion of using proposed LSTM architecture for quantification of BFI in DCS. This approach would be especially useful for continuous real-time monitoring of blood flow.

Recipes for diffuse correlation spectroscopy instrument design using commonly utilized hardware based on targets for signal-to-noise ratio and precision

Over the recent years, a typical implementation of diffuse correlation spectroscopy (DCS) instrumentation has been adapted widely. However, there are no detailed and accepted recipes for designing such instrumentation to meet pre-defined signal-to-noise ratio (SNR) and precision targets. These require specific attention due to the subtleties of the DCS signals. Here, DCS experiments have been performed using liquid tissue simulating phantoms to study the effect of the detected photon count-rate, the number of parallel detection channels and the measurement duration on the precision and SNR to suggest scaling relations to be utilized for device design.

Collimating micro-lens fiber array for noncontact near-infrared diffuse correlation tomography

Near-infrared diffuse correlation spectroscopy/tomography (DCS/DCT) has recently emerged as a noninvasive measurement/imaging technology for tissue blood flow. In DCT studies, the high-dense collection of light temporal autocorrelation curves (g ₂(τ)) via fiber array are critical for image reconstruction of blood flow. Previously, the camera-based fiber array limits the field of view (FOV), precluding its applications on large-size human tissues. The line-shape fiber probe based on lens combination, which is predominantly used in current DCT studies, requires rotated-scanning over the surface of target tissue, substantially prolonging the measurement time and increasing the system instability. In this study, we design a noncontact optical probe for DCT based on collimating micro-lens fiber array, termed as FA-nc-DCT system. For each source/detector fiber, a single optical path was collimated by coupling with one micro-lens in the fiber array that is integrated in a square-shape base. Additionally, an 8×8 optical switch is used to share the hardware laser and detectors without spatial scanning. The FA-nc approach for the precise collection of g ₂(τ) curves was validated through a speed-varied phantom experiment and the human experiments of cuff occlusion, from which the expected value of the blood flow index (BFI) was obtained. Furthermore, the flow anomaly in the phantom and the ischemic muscle in human were accurately reconstructed from the FA-nc-DCT system, which is combined with the imaging framework based on the Nth-order linear algorithm that we recently created. Those outcomes demonstrated the great potential of FA-nc-DCT technology for fast and robust imaging of various diseases such as human breast cancers.

Enhancement of diffuse correlation spectroscopy tissue blood flow measurement by acoustic radiation force

The current research on acousto-optic effects focuses on the interactions of acoustic waves with static optical properties rather than dynamic features such as tissue blood flow. Diffuse correlation spectroscopy (DCS) is an emerging technology capable of direct measurements of tissue blood flow by probing the movements of red blood cells (RBCs). In this article, we investigated the relations between the acoustic radiation force (ARF) and ultrasonic patterns by the finite element simulations. Based on the outcomes, we experimentally explored how the ultrasound-generated ARF enhance the DCS data as well as the blood flow measurements. The results yield the optimal pattern to generate ARF and elucidate the relations between the ultrasonic emission and flow elevations. The flow modality combing the DCS with ARF modulations, which was proposed in this study for the first time, would promote disease diagnosis and therapeutic assessment in the situation wherein the blood flow contrast between healthy and pathological tissues is insufficient.

Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles

In the last 20 yr, near-infrared diffuse correlation spectroscopy (DCS) has been developed for providing a noninvasive estimate of microvascular blood flow (BF) as a BF index (BF_i) in the human skin, muscle, breast, brain, and other tissue types. In this study, we proposed a new motion correction algorithm for DCS-derived BF_i able to remove motion artifacts during cycling exercise. We tested this algorithm on DCS data collected during cycling exercise and demonstrated that DCS can be used to quantify muscle BF_i during dynamic high-intensity exercise. In addition, we measured tissue regional oxygen metabolic rate (MRO_2i) by combining frequency-domain multidistance near-infrared spectroscopy (FDNIRS) oximetry with DCS flow measures. Recreationally active subjects (n = 12; 31 ± 8 yr, 183 ± 4 cm, 79 ± 10 kg) pedaled at 80-100 revolutions/min until volitional fatigue with a work rate increase of 30 W every 4 min. Exercise intensity was normalized in each subject to the cycling power peak (W_peak). Both rectus femoris BF_i and MRO_2i increased from 15% up to 75% W_peak and then plateaued to the end of the exercise. During the recovery at 30 W cycling power, BF_i remained almost constant, whereas MRO_2i started to decrease. The BF_i/MRO_2i plateau was associated with the rising of the lactate concentration, indicating the progressive involvement of the anaerobic metabolism. These findings further highlight the utility of DCS and FDNIRS oximetry as effective, reproducible, and noninvasive techniques to assess muscle BF_i and MRO_2i in real time during a dynamic exercise such as cycling.NEW & NOTEWORTHY To the best of our knowledge, this study is the first to demonstrate that diffuse correlation spectroscopy in combination with frequency-domain near-infrared spectroscopy can monitor human quadriceps microvascular blood flow and oxygen metabolism with high temporal resolution during a cycling exercise. The optically measured parameters confirm the expected relationship between blood flow, muscle oxidative metabolism, and lactate production during exercise.