Near-infrared spectroscopy (NIRS) has received extensive attention in the field of brain functional investigation because of its noninvasiveness, safety and environmental adaptation. Nevertheless, this modality still demands an enhancement in the measurement reliability and the channel availability for broader applications and quantitative accomplishment. In this study we have developed a three-wavelength, 240-channel continuous-wave NIRS-DOT system of lock-in photoncounting mode. The system combines high-superiority of the lock-in detection in noise suppression and parallelism capability with ultra-high sensitivity of the photon-counting technology, and provides 20 source-fiber optodes connecting to their respective three-wavelength laser diode sets and 12 detection-fiber optodes connecting to their respective photoncounting photomultiplier-tubes. The light intensity can be automatically adjusted according to the custom configurations for an optimal operation. The system has been validated using a series of static and dynamic phantom experiments, demonstrating appealing performances in stability, linearity, anti-noise, inter-channel crosstalk and temporal resolution.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.
During the past two decades there has been a dramatic rise in the use of functional near-infrared spectroscopy (fNIRS) as a neuroimaging technique in cognitive neuroscience research. Diffuse optical tomography (DOT) and optical topography (OT) can be employed as the optical imaging techniques for brain activity investigation. However, most current imagers with analogue detection are limited by sensitivity and dynamic range. Although photon-counting detection can significantly improve detection sensitivity, the intrinsic nature of sequential excitations reduces temporal resolution. To improve temporal resolution, sensitivity and dynamic range, we develop a multi-channel continuous-wave (CW) system for brain functional imaging based on a novel lock-in photon-counting technique. The system consists of 60 Light-emitting device (LED) sources at three wavelengths of 660nm, 780nm and 830nm, which are modulated by current-stabilized square-wave signals at different frequencies, and 12 photomultiplier tubes (PMT) based on lock-in photon-counting technique. This design combines the ultra-high sensitivity of the photon-counting technique with the parallelism of the digital lock-in technique. We can therefore acquire the diffused light intensity for all the source-detector pairs (SD-pairs) in parallel. The performance assessments of the system are conducted using phantom experiments, and demonstrate its excellent measurement linearity, negligible inter-channel crosstalk, strong noise robustness and high temporal resolution.
Near-infrared (NIR) brain imaging is one of the most promising techniques for brain research in recent years. As a significant supplement to the clinical imaging technique, such as CT and MRI, the NIR technique can achieve a fast, non-invasive, and low cost imaging of the brain, which is widely used for the brain functional imaging and hematoma detection. NIR imaging can achieve an imaging depth up to only several centimeters due to the reduced optical attenuation. The structure of the human brain is so particularly complex, from the perspective of optical detection, the measurement light needs go through the skin, skull, cerebrospinal fluid (CSF), grey matter, and white matter, and then reverses the order reflected by the detector. The more photons from the Depth of Interest (DOI) in brain the detector capture, the better detection accuracy and stability can be obtained. In this study, the Equivalent Signal to Noise Ratio (ESNR) was defined as the proportion of the photons from the DOI to the total photons the detector evaluated the best Source and Detector (SD) separation. The Monte-Carlo (MC) simulation was used to establish a multi brain layer model to analyze the distribution of the ESNR along the radial direction for different DOIs and several basic brain optical and structure parameters. A map between the best detection SD separation, in which distance the ESNR was the highest, and the brain parameters was established for choosing the best detection point in the NIR brain imaging application. The results showed that the ESNR was very sensitivity to the SD separation. So choosing the best SD separation based on the ESNR is very significant for NIR brain imaging application. It provides an important reference and new thinking for the brain imaging in the near infrared.
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