Spatial Sensitivity Profile Research Articles

Depth-selective method for time-domain diffuse reflectance measurements: validation study of the dual subtraction technique.

Research on the spatial distribution of sensitivity of time-domain near infrared diffuse reflectance measurement is reported in this paper. The main objective of the investigation is to validate theoretically calculated sensitivity profiles for a measurement geometry with two detectors and two sources in which sensitivity profiles of statistical moments of distributions of time of flight of photons (DTOFs) are spatially restricted to a region underneath the detectors. For this dual subtraction method, smaller sensitivities to changes appearing in the superficial layer of the medium were observed compared to the single distance and single subtraction methods. Experimental validation of this approach is based on evaluation of changes in the statistical moments of DTOFs measured on a liquid phantom with local absorption perturbations. The spatial distributions of sensitivities, depth-related sensitivity and depth selectivities were obtained from the dual subtraction method and compared with those from single distance and single subtraction approaches. Also, the contrast to noise ratio (CNR) was calculated for the dual subtraction technique and combined with depth selectivity in order to assess the overall performance (product of CNR and depth selectivity) of the method. Spatial sensitivity profiles from phantom experiments are in a good agreement with the results of theoretical studies and feature more locally restricted sensitivity volume with the point of maximal sensitivity located deeper. The highest value of overall performance was obtained experimentally for the second statistical moment in the dual subtraction method (∼10.8) surpassing that of the single distance method (∼8.7). This confirms the advantage of dual subtraction measurement geometries in the suppression of optical signals originated in the superficial layer of the medium.

Application of the SENSE Algorithm to Multimodal Switchable Metasurface Imaging

This work aims to provide a way of performing parallel imaging with a single-channel variable-frequency resonant device. Different spatial sensitivity profiles required for SENSE reconstruction are achieved by switching between the device eigenmodes. A device capable of such switching is manufactured and several k-spaces are acquired using the device different eigenmodes. The k-spaces are then subject to downsampling to obtain the sensitivity profiles of each eigenmode and then - to perform SENSE-based reconstruction of the unaliased images with different acceleration factors.

Depth-selective data analysis for time-domain fNIRS: moments vs. time windows.

Time-domain measurements facilitate the elimination of the influence of extracerebral, systemic effects, a key problem in functional near-infrared spectroscopy (fNIRS) of the adult human brain. The analysis of measured time-of-flight distributions of photons often relies on moments or time windows. However, a systematic and quantitative characterization of the performance of these measurands is still lacking. Based on perturbation simulations for small localized absorption changes, we compared spatial sensitivity profiles and depth selectivity for moments (integral, mean time of flight and variance), photon counts in time windows and their ratios for different time windows. The influence of the instrument response function (IRF) was investigated for all measurands and for various source-detector separations. Variance exhibits the highest depth selectivity among the moments. Ratios of photon counts in different late time windows can achieve even higher selectivity. An advantage of moments is their robustness against the shape of the IRF and instrumental drifts.

Inside Cover: Investigation of the source‐detector separation in near infrared spectroscopy for healthy and clinical applications (J. Biophotonics 11/2019)

Near infrared spectroscopy (NIRS) neuroimaging sensors use light source and detector pairs placed over scalp to measure underlying neurophysiology. Source detector separation (SDS) is a critical design parameter and should aim a balanced trade‐off for detected light intensity and sensitivity to brain tissue. In this study, we used multi‐layer digital head models to systematically evaluate the role of SDS on NIRS spatial sensitivity profiles within both healthy and clinical conditions.Further details can be found in the article by Lei Wang, Hasan Ayaz, and Meltem Izzetoglu (e201900175). image

Investigation of the source-detector separation in near infrared spectroscopy for healthy and clinical applications.

Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light-tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal-fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source-detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.

Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation

Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation

Evaluation of improvement of diffuse optical imaging of brain function by high-density probe arrangements and imaging algorithms

Diffuse optical imaging has been applied to measure the localized hemodynamic responses to brain activation. One of the serious problems with diffuse optical imaging is the limitation of the spatial resolution caused by the sparse probe arrangement and broadened spatial sensitivity profile for each probe pair. High-density probe arrangements and an image reconstruction algorithm considering the broadening of the spatial sensitivity can improve the spatial resolution of the image. In this study, the diffuse optical imaging of the absorption change in the brain is simulated to evaluate the effect of the high-density probe arrangements and imaging methods. The localization error, equivalent full-width half maximum and circularity of the absorption change in the image obtained by the mapping and reconstruction methods from the data measured by five probe arrangements are compared to quantitatively evaluate the imaging methods and probe arrangements. The simple mapping method is sufficient for the density of the measurement points up to the double-density probe arrangement. The image reconstruction method considering the broadening of the spatial sensitivity of the probe pairs can effectively improve the spatial resolution of the image obtained from the probe arrangements higher than the quadruple density, in which the distance between the neighboring measurement points is 10.6 mm.

Magnetic resonance imaging appropriate for construction of subject-specific head models for diffuse optical tomography.

Subject-specific head models of which their geometry is based on structural magnetic resonance images are essential to accurately estimate the spatial sensitivity profiles for image reconstruction in diffuse optical tomography. T1-weighted magnetic resonance images, which are commonly used for structural imaging, are not sufficient for the threshold-based segmentation of the superficial tissues. Two types of pulse sequences, which provide a high contrast among the superficial tissues, are introduced to complement the segmentation to construct the subject-specific head models. The magnetic resonance images acquired by the proposed pulse sequences are robust to the threshold level and adequate for the threshold-based segmentation of the superficial tissues compared to the T1- and T2-weighted images. The total scan time of the proposed pulse sequences is less than one-fourth of that for the T2-weighted pulse sequence.

Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template

Understanding the spatial and depth sensitivity of non-invasive near-infrared spectroscopy (NIRS) measurements to brain tissue–i.e., near-infrared neuromonitoring (NIN) – is essential for designing experiments as well as interpreting research findings. However, a thorough characterization of such sensitivity in realistic head models has remained unavailable. In this study, we conducted 3,555 Monte Carlo (MC) simulations to densely cover the scalp of a well-characterized, adult male template brain (Colin27). We sought to evaluate: (i) the spatial sensitivity profile of NIRS to brain tissue as a function of source-detector separation, (ii) the NIRS sensitivity to brain tissue as a function of depth in this realistic and complex head model, and (iii) the effect of NIRS instrument sensitivity on detecting brain activation. We found that increasing the source-detector (SD) separation from 20 to 65 mm provides monotonic increases in sensitivity to brain tissue. For every 10 mm increase in SD separation (up to ∼45 mm), sensitivity to gray matter increased an additional 4%. Our analyses also demonstrate that sensitivity in depth (S) decreases exponentially, with a “rule-of-thumb” formula S = 0.75*0.85depth. Thus, while the depth sensitivity of NIRS is not strictly limited, NIN signals in adult humans are strongly biased towards the outermost 10–15 mm of intracranial space. These general results, along with the detailed quantitation of sensitivity estimates around the head, can provide detailed guidance for interpreting the likely sources of NIRS signals, as well as help NIRS investigators design and plan better NIRS experiments, head probes and instruments.

The influence of frontal sinus in brain activation measurements by near-infrared spectroscopy analyzed by realistic head models

Adequate modeling of light propagation in the head is important to predict the sensitivity of NIRS signal and the spatial sensitivity profile of source-detector pairs. The 3D realistic head models of which the geometry is based upon the anatomical images acquired by magnetic resonance imaging and x-ray computed tomography are constructed to investigate the influence of the frontal sinus on the NIRS signal and spatial sensitivity. Light propagation in the head is strongly affected by the presence of the frontal sinus. The light tends to propagate around the frontal sinus. The influence of the frontal sinus on the sensitivity of the NIRS signal to the brain activation is not consistent and depends on the depth of the frontal sinus, the optical properties of the superficial tissues and the relative position between the source-detector pair and the frontal sinus. The frontal sinus located in the shallow region of the skull tends to reduce the sensitivity of the NIRS signal while the deep frontal sinus can increase the sensitivity of the NIRS signal.

Brain structure and spatial sensitivity profile assessing by near‐infrared spectroscopy modeling based on 3D MRI data

The goal of this study is to prove that the light propagation in the head by used the 3-D optical model from in vivo MRI data set can also provide significant characteristics on the spatial sensitivity of cerebral cortex folding geometry based on Monte Carlo simulation. Thus, we proposed a MRI based approach for 3-D brain modeling of near-infrared spectroscopy (NIRS). In the results, the spatial sensitivity profile of the cerebral cortex folding geometry and the arrangement of source-detector separation have being necessarily considered for applications of functional NIRS. The optimal choice of source-detector separation is suggested within 3-3.5 cm by the received intensity with different source-detector separations and the ratio of received light from the gray and white matter layer is greater than 50%. Additionally, this study has demonstrated the capability of NIRS in not only assessing the functional but also detecting the structural change of the brain by taking advantage of the low scattering and absorption coefficients observed in CSF of sagittal view.

Patient-oriented simulation based on Monte Carlo algorithm by using MRI data

BackgroundAlthough Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.MethodsIn this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.ResultsOur results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.ConclusionsIn this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.

Method for Improving the Lateral Resolution of Near-Infrared (NIR) Single Optods: Application to Subcutaneous Vein Detection and Localization

NIR backscattering measurements using single source-detector optical probe (optods) can detect absorption areas within deep tissue layer. However, such optods, are characterized by large separation distance between the source and detectors (>2 cm) and poor lateral resolution (>1 cm), which limits its usage for the localization of small absorption volumes embedded deep within the tissue such as subcutaneous veins. In this work a method to improve the accuracy of locating such absorption volumes (areas) using backscattered NIR measurements is suggested and investigated with the aim of developing an optical sensor for detecting and localizing large subcutaneous veins. The method is based on measuring the differential signal from three overlapping source-detector pairs arranged within the probe such that the total photon sensitivity profile of the probe is maximized along a narrow width area (within the central of the probe) and minimized along its sides. The location of the absorption areas is then determined when a peak maximum of the measured signal is detected. Monte Carlo simulation and light transport modeling was used to determine the optimum arrangement of each source-detector pair within the probe to create the required spatial sensitivity profile and demonstrate the validity of the method. The results showed that the differential optode has more than two times improvement in the lateral resolution compared to the standard optode. The result also showed that the differential probe can locate subcutaneous veins with diameter ~5 mm and embedded at ~1.5 cm depth. The method could have a potential for designing and developing an optical backscattering sensors for detecting and localizing large subcutaneous veins embedded 2 cm depths

Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy

Using the visible Chinese human data set, which faithfully represents human anatomy, we visualize the light propagation in the head in detail based on Monte Carlo simulation. The simulation is verified to agree with published experimental results in terms of a differential path-length factor. The spatial sensitivity profile turns out to seem like a fat tropical fish with strong distortion along the folding cerebral surface. The sensitive brain region covers the gray matter and extends to the superficial white matter, leading to a large penetration depth (>3 cm). Finally, the optimal source-detector separation is suggested to be narrowed down to 3-3.5 cm, while the sensitivity of the detected signal to brain activation reaches the peak of 8%. These results indicate that the cerebral cortex folding geometry actually has substantial effects on light propagation, which should be necessarily considered for applications of functional near-infrared spectroscopy.

Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging

Theoretical analysis of spatial distribution of near-infrared light propagation in head tissues is very important in brain function measurement, since it is impossible to measure the effective optical path length of the detected signal or the effect of optical fibre arrangement on the regions of measurement or its sensitivity. In this study a realistic head model generated from structure data from magnetic resonance imaging (MRI) was introduced into a three-dimensional Monte Carlo code and the sensitivity of functional near-infrared measurement was analysed. The effects of the distance between source and detector, and of the optical properties of the probed tissues, on the sensitivity of the optical measurement to deep layers of the adult head were investigated. The spatial sensitivity profiles of photons in the head, the so-called banana shape, and the partial mean optical path lengths in the skin-scalp and brain tissues were calculated, so that the contribution of different parts of the head to near-infrared spectroscopy signals could be examined. It was shown that the signal detected in brain function measurements was greatly affected by the heterogeneity of the head tissue and its scattering properties, particularly for the shorter interfibre distances.

Validation of practical diffusion approximation for virtual near infrared spectroscopy using a digital head phantom

Light propagation in the digital head phantom for virtual near infrared spectroscopy and imaging is calculated by diffusion theory. In theory, diffusion approximation is not valid in a low-scattering cerebrospinal fluid (CSF) layer around the brain. The optical path length and spatial sensitivity profile predicted by the finite element method based upon the diffusion theory are compared with those predicted by the Monte Carlo method to validate a practical implementation of diffusion approximation to light propagation in an adult head. The transport scattering coefficient of the CSF layer is varied from 0.01 to 1.0 mm−1 to evaluate the influence of that layer on the error caused by diffusion approximation. The error is practically ignored and the geometry of the brain surface such as the sulcus structure in the digital head phantom scarcely affects the error when the transport scattering coefficient of the CSF layer is greater than 0.3 mm−1.

The ON–OFF dichotomy in visual processing: From receptors to perception

Vision scientists long ago pointed to black and white as separate sensations and saw confirmation in the fact that in the absence of light, one perceives the visual field as gray against which the negative after-image of a bright light appeared blacker. The first recordings from optic nerve fibers in vertebrates revealed ON and OFF signals, later associated with separate streams, arising already at the synapse between receptors and bipolar cells. These can be identified anatomically and physiologically and remain distinct all the way to the lateral geniculate nucleus, whose fibers form the input to the primary visual cortex. The dichotomy has been probed by electroretinography and analyzed by means of pharmacological agents and dysfunction due to genetic causes. The bi- rather than a unidirectional nature of the retinal output has advantages in allowing small signals to remain prominent over a greater dynamic range. The two streams innervate cortical neurons in a push-pull manner, generating receptive fields with spatial sensitivity profiles featuring ON and OFF subregions. Manifestations of the dichotomy appear in a variety of simple visual discriminations where there are often profound threshold differences in patterns with same polarity as compared with mixed contrast-polarity components. But even at levels in which the spatial, contrast and color attributes have already been securely established and black and white elements participate equally, a categorical difference between blackness and whiteness of a percept persists. It is an opponency, akin to the ones in the color domain, derived from the original ON and OFF signals and subsequently bound with the other attributes to yield a feature's unitary percept.

TH‐D‐L100J‐04: Dose and Image Quality of Flat‐Panel Detector Volume Computed Tomography for Sinus Imaging

Purpose: CT of the sinuses is commonly used for diagnosis and assessment of chronic sinusitis and preoperative planning for otolaryngological and dental procedures. Sinus imaging is a high‐contrast procedure requiring high spatial resolution. The purpose of this study is to compare the dose and image quality for typical sinus CT clinical protocols using conventional 16‐ and 64‐slice CT scanners with a flat‐panel volume CT scanner. Method and Materials: Three systems were chosen: a MiniCAT (Xoran Technologies, Inc.) flat‐panel detector unit, a Lightspeed 16 Pro, and a Lightspeed VCT (GE Medical Systems). A typical sinus technique of 120 Kvp, 150 mAs, and 2.5 mm slice thickness was selected for the GE scanners. The MiniCAT used a preset sinus technique of 120 kVp, 45 mAs, and 0.4 mm voxel size. The doses from 20 TLDs (Landauer, Inc.) inserted in a Rando head phantom were used to estimate the effective dose for each technique. The spatial resolution and slice sensitivity profiles (SSP) were measured for these same techniques. Results: The flat‐panel detector volume CT scanner gave an effective dose of 0.17 mSv while the 16‐ and 64‐slice scanners gave 0.96 and 0.87 mSv, respectively. The Lightspeed scanners had a trans‐axial spatial resolution of 7 lp/mm (standard filter) and 11 lp/cm (bone filter). The flat‐panel CT scanner had isotropic spatial resolution of 12 lp/cm. The FWHM of the SSP along the z‐axis was 0.49 mm for the MiniCAT, and 2.32 and 2.34 mm for the 16‐ and 64‐slice scanners, respectively. Conclusion: Flat‐panel volume CT with submillimeter isotropic spatial resolution is a low dose alternative for high‐contrast sinus imaging. While the conventional CT scanners have advantages for low‐contrast resolution, this can be sacrificed for a five‐fold dose reduction and increased spatial resolution in a volume image. Conflict of Interest: Research sponsored by Xoran Technologies, Inc.

Self‐calibrated GRAPPA method for 2D and 3D radial data

A fast parallel MRI (pMRI) reconstruction method is presented for 2D and 3D radial trajectories. A limitation of the radial generalized autocalibrating partially parallel acquisitions (GRAPPA) method is the need to acquire training data prior to the actual scan. This can be eliminated by the use of self-calibration when synthesizing the missing data for each coil reconstruction. The training data for each coil are estimated by multiplying the conventionally reconstructed composite image, which contains the aliasing artifacts from undersampling, with each coil's spatial sensitivity profile. An estimate of the individual receiver spatial sensitivity profiles is obtained from the k-space data that fulfill the Nyquist sampling criterion. The frequency domain representation of the training data is then calculated at the acquired k-space sample points and at the unacquired locations at which we desire to synthesize k-space data. Fitting the acquired k-space samples to the unacquired points creates reconstruction weights that are used to synthesize unacquired radial lines. The in vivo feasibility of the method for 2D radial trajectories is illustrated with an example of 2D abdominal imaging. Preliminary results obtained after applying the method on a 3D radial steady-state free precession (SSFP) data set are also demonstrated.

Semiconductor-metal hybrid structures as local magnetic-field probes: Magnetoresistance and spatial sensitivity profile

Tailored nonmagnetic semiconductor-metal hybrid structures exhibit a large magnetoresistance effect in a homogeneous magnetic field. This is the so-called extraordinary magnetoresistance effect. Here, we study numerically the magnetoresistance of such hybrid structures in the inhomogeneous field of a magnetic dot. Surprisingly, the four-point resistance R versus magnetic field B changes its symmetry if compared to the case of a homogeneous field and is strongly dependent on the position of the local magnetic field. Interestingly, the active device area is not defined by the voltage probe separation, but by the positioning of voltage probes and current leads. We find a magnetoresistance effect as large as 18% although only 1∕60 of the device area is subject to a small magnetic field of ±50mT. These results are promising for sensing magnetic-field distributions in the nanoscale regime such as the stray fields of magnetic recording media.