Nth-order linear algorithm for diffuse correlation tomography

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.

High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts

A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.

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.

Diffuse optical assessment of cerebral-autoregulation in older adults stratified by cerebrovascular risk

Diagnosis of cerebrovascular disease (CVD) at early stages is essential for preventing sequential complications. CVD is often associated with abnormal cerebral microvasculature, which may impact cerebral-autoregulation (CA). A novel hybrid near-infrared diffuse optical instrument and a finger plethysmograph were used to simultaneously detect low-frequency oscillations (LFOs) of cerebral blood flow (CBF), oxy-hemoglobin concentration ([HbO₂ ]), deoxy-hemoglobin concentration ([Hb]) and mean arterial pressure (MAP) in older adults before, during and after 70° head-up-tilting (HUT). The participants with valid data were divided based on Framingham risk score (FRS, 1-30 points) into low-risk (FRS ≤15, n = 13) and high-risk (FRS >15, n = 11) groups for developing CVD. The LFO gains were determined by transfer function analyses with MAP as the input, and CBF, [HbO₂ ] and [Hb] as the outputs (CA ∝ 1/Gain). At resting-baseline, LFO gains in the high-risk group were relatively lower compared to the low-risk group. The lower baseline gains in the high-risk group may attribute to compensatory mechanisms to maintain stronger steady-state CAs. However, HUT resulted in smaller gain reductions in the high-risk group compared to the low-risk group, suggesting weaker dynamic CAs. LFO gains are potentially valuable biomarkers for early detection of CVD based on associations with CAs.

Recovery of the diffuse correlation spectroscopy data-type from speckle contrast measurements: towards low-cost, deep-tissue blood flow measurements

A multi-step Volterra integral equation-based algorithm was developed to measure the electric field auto-correlation function from multi-exposure speckle contrast data. This enabled us to derive an estimate of the full diffuse correlation spectroscopy data-type from a low-cost, camera-based system. This method is equally applicable for both single and multiple scattering field auto-correlation models. The feasibility of the system and method was verified using simulation studies, tissue mimicking phantoms and subsequently in in vivo experiments.

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

Various diseases are relevant to the abnormal blood flow in tissue. Diffuse correlation spectroscopy (DCS) is an emerging technology to extract the blood flow index (BFI) from light electric field temporal autocorrelation data. To account for tissue heterogeneity and irregular geometry, we developed an innovative DCS algorithm (i.e., the Nth order linear algorithm, or simply the NL algorithm) previously, in which the DCS signals are fully utilized through iterative linear regressions. Under the framework of NL algorithm, the BFI to be extracted is significantly influenced by the linear regression approach adopted. In this study, three approaches were proposed and evaluated for performing the iterative linear regressions, in order to understand what are the appropriate regression methods for BFI estimation. The three methods are least-squared minimization (L₂ norm), least-absolute minimization (L₁ norm) and support vector regression (SVR), where L₂ norm is a conventional approach to perform linear regression. L₁ norm and SVR are the approaches newly introduced here to process the DCS data. Computer simulations and the autocorrelation data collected from liquid phantom and human tissues are utilized to evaluate the three approaches. The results show that the best performance is achieved by the SVR approach in extracting the BFI values, with an error rate of 2.23% at 3.0 cm source-detector separation. The L₁ norm method gives a medium error of 2.81%. In contrast, the L₂ norm method leads to the largest error (3.93%) in extracting the BFI values. The outcomes derived from this study will be very helpful for the tissue blood flow measurements, which is critical for translating the DCS technology to the clinic.