Frequency Domain Imaging Research Articles

Diagnostic Accuracy of Optical Frequency Domain Imaging for Identifying Necrotic Cores with Intraplaque Hemorrhage in Advanced Human Carotid Plaques

This study investigated whether optical frequency domain imaging (OFDI) can identify carotid artery vulnerable plaque characteristics, focusing on lipid-rich necrotic core (NC) and intraplaque hemorrhage (IPH). Fourteen patients scheduled for carotid endarterectomy underwent OFDI scan during preoperative angiography. Atherosclerotic plaque specimens obtained from carotid endarterectomy were cut every 3-4 mm into 4-μm transverse cross-sections and stained with standard methods. Each cross-section was matched with OFDI, and histologically classified into either fibrous, calcific, pathological intimal thickening (PIT), and NC. Of 75 histologic cross-sections, 6 were categorized as fibrous (8%), 18 as calcific (24%), 9 as PIT (12%), and 42 as NC (56%). Tissues categorized as NC had significantly higher OFDI signal attenuation rates than the other tissues (p <0.001), followed by PIT, calcific, and fibrous tissues. The receiver operating characteristic analysis indicated that attenuation rates of >0.023 and >0.031 predicted the presence of NC and IPH with high areas under the curve of 0.91 and 0.88, respectively. OFDI provides potential capability for the detection of NCs with IPH of carotid artery plaques by quantitatively analyzing the attenuation rate.

Handheld spatial frequency domain imager for noninvasive Sjögren's syndrome labial salivary gland biopsy.

A labial salivary gland biopsy (LSGB) plays an essential role in diagnosing Sjögren's syndrome (SS), but its clinical application is limited due to its invasiveness. Here, we present a handheld single snapshot multiple-frequency demodulation-spatial frequency domain imaging (SSMD-SFDI) device for a rapid optical biopsy of labial salivary glands noninvasively. The structural and physiological parameters of lower lip mucosa were obtained from the light reflectance of the layered oral mucosa. The recovered parameters were found to correlate strongly with the progression of SS. In our pilot study on 15 healthy subjects and 183 SS patients, a support vector machine (SVM) classifier using the measured parameters distinguished healthy subjects, LSGB I, II, III, and IV patients in sequence with AUCs of 0.979, 0.898, 0.906, and 0.978, respectively. Critical structural and physiological alterations in the mucosa due to SS were further identified and used to assess its risk using an explainable neural network. The handheld spatial frequency domain imager may serve as a valuable label-free and noninvasive tool for early diagnosing and surveying SS.

Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform

Effective fault diagnosis of rotating machinery is essential for the predictive maintenance of modern industries. In this study, a novel framework that combines a residual network (ResNet) as a backbone and an extreme learning machine (ELM) as a classifier (RNELM) is proposed to diagnose faults of rotating machinery. Firstly, continuous wavelet transform (CWT) is used to convert the raw time-domain signal into time–frequency domain images. Subsequently, the ResNet backbone in the framework extracts advanced features from the images for the ELM classifier, substantially improving the fault diagnosis performance. The performance of the framework is compared with four other methods using four evaluation metrics on datasets from Case Western Reserve University (CWRU), laboratory results and industrial applications. The experimental results show that the RNELM achieves outstanding results on the test samples of the three datasets, demonstrating the excellent performance and practicability of the proposed framework for fault diagnosis.

Stratification of Microvascular Disease Severity in the Foot Using Spatial Frequency Domain Imaging.

Microvascular disease (MVD) describes systemic changes in the small vessels (~100 um diameter) that impair tissue oxygenation and perfusion. MVD is a common but poorly monitored complication of diabetes. Recent studies have demonstrated that MVD: (i) is an independent risk factor for ulceration and amputation and (ii) increases risk of adverse limb outcomes synergistically with PAD. Despite the clinical relevance of MVD, microvascular evaluation is not standard in a vascular assessment. We evaluated 299 limbs from 153 patients seen clinically for possible lower extremity PAD. The patients were assessed by ankle brachial index (ABI), toe brachial index (TBI), and spatial frequency domain imaging (SFDI). These measurements were evaluated and compared to patient MVD status, defined by clinical diagnoses of (in ascending order of severity) no diabetes; diabetes; diabetes + neuropathy; diabetes + neuropathy + retinopathy. SFDI-derived parameters HbT1 and StO2 were significantly different across the MVD groups (P < .001). A logistic regression model based on HbT1 and StO2 differentiated limbs with severe MVD (diabetes+neuropathy+retinopathy) from the larger group of limbs from patients with only diabetes (P = .001, area under the curve = 0.844). Neither ABI nor TBI significantly differentiated these populations. Standard assessment of PAD using ABI and TBI are inadequate for detecting MVD in at-risk populations. SFDI-defined HbT1 and StO2 are promising tools for evaluating MVD. Prospective studies with wound-based outcomes would be useful to further evaluate the role MVD assessment could play in routine clinical evaluation of patients at risk for lower extremity complications.

The application of SFDI and LSI system to evaluate the blood perfusion in skin flap mouse model.

The aim of this study is to evaluate whether the blood perfusion to tissues for detecting ischemic necrosis can be quantitatively monitored by spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) in a skin flap mouse model. Skin flaps were made on Institute of Cancer Research (ICR) mice. Using SFDI and LSI, the following parameters were estimated: oxyhemoglobin (HbO2), deoxyhemoglobin (Hb), total hemoglobin (THb), tissue oxygen saturation (StO2), andspeckle flow index (SFI). Histologically, epithelium thickness, collagen deposition, and blood vessel count of skin flap tissues were analyzed. Then, the correlation of SFDI and histological results was assessed by application of Spearman rank correlation method. As the result, the number of blood vessels and the percentage of collagen areas showed significant difference between the necrotic tissue group and the non-necrotic one. Especially, the necrotic tissue had a complete epithelial loss and loses its normal structure. We identified that SFDI/LSI parameters were significantly different between non-necrotic and necrotic tissue groups. Especially, all SFDI and LSI parameters measured on the 1st day after surgery showed significant difference between necrotic tissue and non-necrotic tissue. In addition, the number of blood vessel and percentage of collagen area were positively correlated with HbO2 and StO2 among SFDI/LSI parameters. Meanwhile, the number of blood vessel and percentage of collagen area showed the negative correlation with Hb. By applying SFDI and LSI simultaneously to the skin flap, we could quantitatively monitor the blood perfusion and the tissue condition which can help us to detect ischemic necrosis objectively in early stage.

Tangential signal dropout artefact in optical frequency domain imaging.

Tangential signal dropout (TSD), which occurs when the optical frequency domain imaging (OFDI) beam strikes the vessel wall under a glancing angle and travels almost parallel to the vessel wall, is the most important imaging artefact leading to the erroneous diagnosis of lipid-rich plaques. This study aimed to evaluate the OFDI artefact of TSD, which mimics the appearance of lipid-rich plaque and macrophage (Mø) infiltration. A total of 1,019 histological cross-sections from 23 autopsy hearts were matched with the corresponding OFDI images. Of these, 232 OFDI cross-sections that contained signal-poor regions with diffuse borders were classified as lipid-rich plaques. The angle θ was calculated between the OFDI beam that strikes the edge of the luminal surface of the low-intensity region and that which strikes the surface line of the low-intensity region. On histological evaluation, 182 (78%) cross-sections were classified as histologically lipidic/Mø infiltration, while the remaining 50 (22%) cross-sections were classified as histologically non-lipidic/Mø infiltration. The angle θ was significantly smaller in the non-lipidic/Mø infiltration group than in the lipidic/Mø infiltration group (12±6° versus 37±14°, p<0.001). Receiver operating curve analysis revealed that the optimal cut-off value of the incident angle for predicting TSD was 23° with an area under the curve of 0.98. When the OFDI imaging beam strikes the tissue at an angle θ<23°, TSD artefact could occur. To eliminate image misinterpretation, our findings suggest that the OFDI catheter geometry should be considered for the accurate diagnosis of lipid-rich plaques and Mø infiltration.

Frequency-Domain Common Image Gathers for Quickly Checking the Accuracy of Migration Velocity

The common image gather (CIG) method enables qualitative and quantitative evaluation of the velocity model through the image. The most common such methods are offset-domain common image gather (ODCIG) and angle-domain common image gather (ADCIG). The challenge is that it requires a great deal of additional computation besides migration. We, therefore, introduce a new CIG method that has low computational cost: frequency-domain common image gather (FDCIG). FDCIG simply rearranges data using a gradient (partial image) calculated in the process of obtaining a migration image to represent it in the frequency-depth domain. We apply the FDCIG method to the layered model to show how FDCIGs behave when the velocity model is inaccurate. We also introduced the 3-D SEG/EAGE salt model to show how to apply the FDCIG method in the hybrid domain. Last, we applied 2-D real data. These sample field data also indicate that even in a complex velocity model, deviant behavior by FDCIG appears intuitively if the background velocity is inaccurate.

Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning

Background and aimsWe developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations. MethodsA total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). The DL model was developed based on pyramid scene parsing network (PSPNet). Given an input image, a convolutional neural network (ResNet50) was used as an encoder to generate feature maps of the last convolutional layer. ResultsFor the quantitative evaluation, the mean F-score and IoU values, which are used to evaluate how close the predicted results are to the ground truth, were used. The validation and test dataset had F-score and IoU values of 0.63, 0.49, and 0.66, 0.52, respectively. For the section-level diagnostic accuracy, the areas under the receiver-operating characteristic curve produced by the DL model for FC, PIT, FA, and HER were 0.91, 0.85, 0.86, and 0.86, respectively, and were comparable to those of an expert observer. ConclusionsDL semantic segmentation of coronary plaques in OFDI images was used as a tool to automatically categorize atherosclerotic plaques using histological findings as the gold standard. The proposed method can support interventional cardiologists in understanding histological properties of plaques.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models.

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

New slant range model and azimuth perturbation resampling based high-squint maneuvering platform SAR imaging

Strong spatial variance of the imaging parameters and serious geometric distortion of the image are induced by the acceleration and vertical velocity in a high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms. In this paper, a frequency-domain imaging algorithm is proposed based on a novel slant range model and azimuth perturbation resampling. First, a novel slant range model is presented for mitigating the geometric distortion according to the equal squint angle curve on the ground surface. Second, the correction of azimuth-dependent range cell migration (RCM) is achieved by introducing a high-order time-domain perturbation function. Third, an azimuth perturbation resampling method is proposed for azimuth compression. The azimuth resampling and the time-domain perturbation are used for correcting first-order and high-order azimuthal spatial-variant components, respectively. Experimental results illustrate that the proposed algorithm can improve the focusing quality and the geometric distortion correction accuracy of the imaging scene effectively.

Two-layer spatial frequency domain imaging of compression-induced hemodynamic changes in breast tissue

.Significance: Longitudinal tracking of hemodynamic changes in the breast has shown potential for neoadjuvant chemotherapy (NAC) outcome prediction. Spatial frequency domain imaging (SFDI) could be suitable for frequent monitoring of shallow breast tumors, but strong sensitivity to superficial absorbers presents a challenge.Aim: We investigated the efficacy of a two-layer SFDI inverse model that accounts for varying melanin concentration in the skin to improve discrimination of optical properties of deep tissue of the breast.Approach: Hemodynamic changes in response to localized breast compression were measured in 13 healthy volunteers using a handheld SFDI device. Epidermis optical thickness was determined based on spectral fitting of the model output and used to calculate subcutaneous optical properties.Results: Optical properties from a homogeneous model yielded physiologically unreasonable absorption and scattering coefficients for highly pigmented volunteers. The two-layer model compensated for the effect of melanin and yielded properties in the expected range for healthy breast. Extracted epidermal optical thickness was higher for higher Fitzpatrick types. Compression induced a decrease in total hemoglobin consistent with tissue blanching.Conclusions: The handheld SFDI device and two-layer model show potential for imaging hemodynamic responses that potentially could help predict efficacy of NAC in patients of varying skin tones.

Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction.

This study was to explore the application value of magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in the diagnosis and prognosis of cerebral infarction. Two image reconstruction methods, frequency domain reconstruction network (FDRN) and image domain reconstruction network (IDRN), were introduced based on the CCNN algorithm. In addition, they were integrated to form two new MRI image reconstruction models, namely D-FDRN and D-IDRN. The peak signal to noise ratio (PSNR) value and structural similarity index measure (SSIM) value of the image were compared and analyzed before and after the integration. The MRI images of patients with cerebral infarction in the dataset were undertaken as the data source, the average diffusion coefficient (DCavg) and apparent diffusion coefficient (ADC) values of different parts of the MRI image were measured, respectively. The correlation of the vein abnormality grading (VABG) to the infarct size and the degree of stenosis of the responsible vessel was analyzed in this study. The results showed that the PSNR and SSIM values of the MRI reconstructed image of the D-IDRN algorithm based on the CCNN algorithm in this study were higher than those of other algorithms. There was a positive correlation between the VABG and the infarct size (r = 0.48 and P = 0.002), and there was a positive correlation between the VABG the degree of stenosis of the responsible vessel (r = 0.58 and P < 0.0001). The ADC value of the central area of the infarct on the affected side was significantly greatly lower than that of the normal side (P < 0.01), and the DCavg value of the central area of the infarct was much lower in contrast to the normal side (P < 0.05). It indicated that an image reconstruction algorithm constructed in this study could improve the quality of MRI images. The ADC value and DCavg value changed in the infarct central area could be used as the basis for the diagnosis of cerebral infarction. If the vein was abnormal, the patient suffered from severe vascular stenosis, large infarction area, and poorer prognosis.

Wavefield Reconstruction Inversion of GPR Data for Permittivity and Conductivity Models in the Frequency Domain Based on Modified Total Variation Regularization

The full-waveform inversion (FWI) of ground-penetrating radar (GPR) data yields promise for quantitatively characterizing the parameters of the Earth’s shallow subsurface. However, conventional FWI is highly nonlinear and suffers from cycle skipping once the low-frequency data are missed or the initial model is poor. Furthermore, having limited prior knowledge of the subsurface in GPR measurements increases the ill-posedness of the inverse problem. Wavefield reconstruction inversion (WRI), which mitigates cycle skipping, extends the FWI search space by relaxing the wave equation constraint, reduces the nonlinearity, and is less sensitive to the initial model. In this article, we extend WRI to the 2-D frequency-domain imaging of on-ground GPR data. To improve the inversion stability and mitigate the ill-posedness, we utilize modified total variation (MTV) regularization to constrain the inverted models. With a simple numerical example, we first investigate the effects of the penalty parameter, initial models, and MTV regularization on WRI and further discuss the differences between WRI and FWI. Then, we analyze the sensitivity of the proposed approach to the frequency component and noise in a multiple-targets model. Furthermore, we assess our method with a complex synthetic example containing noise-contaminated data, showing that our proposed approach works efficiently even given noisy GPR data. Therefore, the reasonable combination of WRI and MTV regularization can improve the accuracy and efficiency of imaging for on-ground GPR data. This joint approach for the multiparameter quantitative reconstruction of GPR data ultimately exhibits good applicability and strong robustness and is worthy of promotion.

Evaluation of affine fiber kinematics in porcine tricuspid valve leaflets using polarized spatial frequency domain imaging and planar biaxial testing

Collagen fibers are the primary load-bearing microstructural constituent of bodily soft tissues, and, when subjected to external loading, the collagen fibers reorient, uncrimp, and elongate. Specific to the atrioventricular heart valve leaflets, the collagen fiber kinematics form the basis of many constitutive models; however, some researchers claim that modeling the affine fiber kinematics (AFK) are sufficient for accurately predicting the macroscopic tissue deformations, while others state that modeling the non-affine kinematics (i.e., fiber uncrimping together with elastic elongation) is required. Experimental verification of the AFK theory has been previously performed for the mitral valve leaflets in the left-side heart; however, this same evaluation has yet to be performed for the morphologically distinct tricuspid valve (TV) leaflets in the right-side heart. In this work, we, for the first time, evaluated the AFK theory for the TV leaflets using an integrated biaxial testing-polarized spatial frequency domain imaging device to experimentally quantify the load-dependent collagen fiber reorientations for comparison to the AFK theory predictions. We found that the AFK theory generally underpredicted the fiber reorientations by 3.1°, on average, under the applied equibiaxial loading with greater disparity when the tissue was subjected to the applied non-equibiaxial loading. Furthermore, increased AFK errors were observed with increasing collagen fiber reorientations (Pearson coefficient r = -0.36, equibiaxial loading), suggesting the AFK theory is better suited for relatively smaller reorientations. Our findings suggest the AFK theory may require modification for more accurate predictions of the collagen fiber kinematics in the TV leaflets, which will be useful in refining modeling efforts for more accurate TV simulations.

Spatial frequency domain imaging for determining absorption and scattering properties of bruised pears based on profile corrected diffused reflectance

Curved surface of fruit has a great influence on the measurement of absorption (μa) and reduced scattering coefficients (μ’s) by using the spatial frequency domain imaging (SFDI) technique. In this study, a developed SFDI system was used to conduct phase-measuring profilometry (PMP) to obtain the surface height of tested object. Monte Carlo (MC) simulation was conducted on planar and spherical geometric models, and the correction formula for diffuse reflectance (Rd) was then determined by simulation results and validated by tilt standard reflector. The Rd correction effect on estimating μa and μ’s was revealed by five semi-spherical phantoms, which turned out that both the accuracy and uniformity were substantially improved, with the relative error range of 0.78 %-11.84 % and 2.83 %-8.95 %, and standard deviation range of 1.89 × 10−5 mm-1-1.42 × 10-3 mm-1 and 1.76 × 10-4 mm-1-8.17 × 10-3 mm-1 for corrected μa and μ’s respectively. Finally, the proposed Rd correction method was applied to the bruise detection in pears, the comparisons of μa and μ’s maps between normal and bruised pears were conducted. The results revealed that the area around the highest point of normal pear contour was easily to be identified as bruised area before Rd correction; the μa and μ’s maps for normal pear were more uniform, and for bruised pears after the correction, and the injured area was highlighted for bruised pears. The profile correction method proposed in this study, coupled with the SFDI technique, was essential for precise measurement of μa and μ’s of pear tissues, resulting in less misclassification for bruise detection of pears.

A serial optical frequency-domain imaging study of early and late vascular responses to bioresorbable-polymer sirolimus-eluting stents for the treatment of acute myocardial infarction and stable coronary artery disease patients: results of the MECHANISM-ULTIMASTER study

The purpose of this study was to assess early and late vascular healing in response to bioresorbable-polymer sirolimus-eluting stents (BP-SESs) for the treatment of patients with ST-elevation myocardial infarction (STEMI) and stable coronary artery disease (CAD). A total of 106 patients with STEMI and 101 patients with stable-CAD were enrolled. Optical frequency-domain images were acquired at baseline, at 1- or 3-month follow-up, and at 12-month follow-up. In the STEMI and CAD cohorts, the percentage of uncovered struts (%US) was significantly and remarkably decreased during early two points and at 12-month (the STEMI cohort: 1-month: 18.75 ± 0.78%, 3-month: 10.19 ± 0.77%, 12-month: 1.80 ± 0.72%; p < 0.001, the CAD cohort: 1-month: 9.44 ± 0.78%, 3-month: 7.78 ± 0.78%, 12-month: 1.07 ± 0.73%; p < 0.001 respectively). The average peri-strut low-intensity area (PLIA) score in the STEMI cohort was significantly decreased during follow-up period (1.90 ± 1.14, 1.18 ± 1.25, and 1.01 ± 0.72; p ≤ 0.001), whereas the one in the CAD cohort was not significantly changed (0.89 ± 1.24, 0.67 ± 1.07, and 0.64 ± 0.72; p = 0.59). In comparison with both groups, differences of %US and PLIA score at early two points were almost disappeared or close at 12 months. The strut-coverage and healing processes in the early phase after BP-SES implantation were significantly improved in both cohorts, especially markedly in STEMI patients. At 1 year, qualitatively and quantitatively consistent neointimal coverage was achieved in both pathogenetic groups.

Bail-out use of Wiggle wire for stuck wire in dissection lumen under optical frequency domain imaging.

Bail-out use of Wiggle wire for stuck wire in dissection lumen under optical frequency domain imaging.

Profile-based intensity and frequency corrections for single-snapshot spatial frequency domain imaging.

We have proposed the profile-based intensity and frequency corrections for single-snapshot spatial frequency domain (SFD) imaging to mitigate surface profile effects on the measured intensity and spatial frequency in extracting the optical properties. In the scheme, the spatially modulated frequency of the projected sinusoidal pattern is adaptively adjusted according to the sample surface profile, reducing distortions of the modulation amplitude in the single-snapshot demodulation and errors in the optical property extraction. The profile effects on both the measured intensities of light incident onto and reflected from the sample are then compensated using Minnaert's correction to obtain the true diffuse reflectance of the sample. We have validated the method by phantom experiments using a highly sensitive SFD imaging system based on the single-pixel photon-counting detection and assessed error reductions in extracting the absorption and reduced scattering coefficients by an average of 40% and 10%, respectively. Further, an in vivo topography experiment of the opisthenar vessels has demonstrated its clinical feasibility.

Prediction of the debulking effect of rotational atherectomy using optical frequency domain imaging.

Whether predicting the rotational atherectomy (RA) effect based on the position of optical frequency domain imaging (OFDI) is accurate remains uncertain. The aim of this study was to evaluate the predictive accuracy of OFDI in identifying RA location and area. Twenty-five patients who underwent RA with OFDI were included. On pre-RA OFDI images, a circle with the dimension of a Rota burr was drawn at the center of the OFDI catheter. The area where the circle overlapped with the vessel wall was defined as the predicted ablation area (P-area), and the actual ablated area (A-area) was measured. The predictive accuracy of OFDI was evaluated as follows: overlapped ablation area (O-area: overlapping P- and A-areas) divided by P-area = %Correct-area, and A-area - O-area divided by A-area = %Error-area. Cross-sections were separated into four categories based on the median values of %Correct- and %Error-area. Among 334 cross-sections, RA effects were confirmed in the predicted location in 87% of them. The median %Correct- and %Error-areas were 43.1% and 64.2%, respectively. Floppy wire, narrow lumen area, OFDI catheter close to the intima, and large arc of calcium were independently associated with good prediction (high %Correct-/low %Error-areas). Non-left anterior descending lesions, OFDI catheter far from the wire, and OFDI catheter and wire far from the intima were associated with irrelevant ablation (low %Correct-/ high %Error-areas). The accuracy of the OFDI-based predictions for RA effects was acceptable with regard to location, but not high with regard to area. Wire types, target vessels, and OFDI catheter and wire positions are important determinants for accurately predicting RA effect using pre-procedural OFDI.

High-speed quantitative optical imaging of absolute metabolism in the rat cortex.

.Significance: Quantitative measures of blood flow and metabolism are essential for improved assessment of brain health and response to ischemic injury.Aim: We demonstrate a multimodal technique for measuring the cerebral metabolic rate of oxygen () in the rodent brain on an absolute scale ().Approach: We use laser speckle imaging at 809 nm and spatial frequency domain imaging at 655, 730, and 850 nm to obtain spatiotemporal maps of cerebral blood flow, tissue absorption (), and tissue scattering (). Knowledge of these three values enables calculation of a characteristic blood flow speed, which in turn is input to a mathematical model with a “zero-flow” boundary condition to calculate absolute . We apply this method to a rat model of cardiac arrest (CA) and cardiopulmonary resuscitation. With this model, the zero-flow condition occurs during entry into CA.Results: The values calculated with our method are in good agreement with those measured with magnetic resonance and positron emission tomography by other groups.Conclusions: Our technique provides a quantitative metric of absolute cerebral metabolism that can potentially be used for comparison between animals and longitudinal monitoring of a single animal over multiple days. Though this report focuses on metabolism in a model of ischemia and reperfusion, this technique can potentially be applied to far broader types of acute brain injury and whole-body pathological occurrences.