Temporal mirror-symmetry in functional signals recorded from rat barrel cortex with optical coherence tomography.

Functional optical coherence tomography (fOCT) detects activity-dependent light scattering changes in micro-structures of neural tissue, drawing attention as in vivo volumetric functional imaging technique at a sub-columnar level. There are 2 plausible origins for the light scattering changes: (i) hemodynamic responses such as changes in blood volume and in density of blood cells and (ii) reorientation of dipoles in cellular membrane. However, it has not been clarified which is the major contributor to fOCT signals. Furthermore, previous studies showed both increase and decrease of reflectivity as fOCT signals, making interpretation more difficult. We proposed combination of fOCT with Fourier imaging and adaptive statistics to the rat barrel cortex. Active voxels revealed barrels elongating throughout layers with mini-columns in superficial layers consistent with physiological studies, suggesting that active voxels revealed by fOCT reflect spatial patterns of activated neurons. These voxels included voxels with negative changes in reflectivity and those with positive changes in reflectivity. However, they were temporally mirror-symmetric, suggesting that they share common sources. It is hard to explain that hemodynamic responses elicit positive signals in some voxels and negative signals in the other. On the other hand, considering membrane dipoles, polarities of OCT signals can be positive and negative depending on orientations of scattering particles relative to the incident light. Therefore, the present study suggests that fOCT signals are induced by the reorientation of membrane dipoles.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging.

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.

Intelligent Image De-Blurring for Imaging Flow Cytometry.

By virtue of the combined merits of optical microscopy and flow cytometry, imaging flow cytometry is a powerful tool for rapid, high-content analysis of single cells in large heterogeneous populations. However, its efficiency (defined by the ratio of the number of clearly imaged cells to the total cell population) is not high (typically 50-80%), due to out-of-focus image blurring caused by imperfect fluidic focusing of cells, a common drawback that not only reduces the number of cell images useable for high-content analysis but also increases the probability of false events and missed rare cells. To address this challenge and expand the efficacy of imaging flow cytometry, here, we propose and demonstrate intelligent deblurring of out-of-focus cell images in imaging flow cytometry. Specifically, by using our machine learning algorithms, we show an 11% increase in variance and a 95% increase in first-order gradient summation of cell images taken with an optofluidic time-stretch microscope. Without strict hardware requirements, our intelligent de-blurring method provides a promising solution to the out-of-focus blurring problem of imaging flow cytometers and holds promise for significantly improving their performance. © 2019 International Society for Advancement of Cytometry.

Optical Polarimetric Detection for Dental Hard Tissue Diseases Characterization.

Dental enamel constitutes the outer layer of a crown of teeth and grows nearly parallel. This unique nanostructure makes enamel possess birefringence properties. Currently, there is still no appropriate clinical solution to examine dental hard tissue diseases. Therefore, we developed an optical polarization imaging system for diagnosing dental calculus, caries, and cracked tooth syndrome. By obtaining Stokes signals reflected from samples, Mueller images were constructed and analyzed using Lu-Chipman decomposition. The results showed that diattenuation and linear retardance images can distinguish abnormal tissues. Our result also aligns with previous studies assessed by other methods. Polarimetric imaging is promising for real-time diagnosing.

A Study on Intelligent Optical Bone Densitometry.

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual's bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Mild cognitive impairment estimation based on functional near-infrared spectroscopy.

Patients with mild cognitive impairment (MCI) are at a high risk of developing future dementia. However, early identification and active intervention could potentially reduce its morbidity and the incidence of dementia. Functional near-infrared spectroscopy (fNIRS) has been proposed as a noninvasive modality for detecting oxygenation changes in the time-varying hemodynamics of the prefrontal cortex. This study sought to provide an effective method for detecting patients with MCI using fNIRS and the Wisconsin card sorting test (WCST) to evaluate changes in blood oxygenation. The results revealed that all groups with a lower mini-mental state examination grade had a higher increase in HHb concentration during a modified WCST (MCST). The increase in the change in oxygenated hemoglobin concentration in the stroke group was smaller than that in the normal group due to weak cerebrovascular reactivity.

Dental optical coherence tomography.

This review paper describes the applications of dental optical coherence tomography (OCT) in oral tissue images, caries, periodontal disease and oral cancer. The background of OCT, including basic theory, system setup, light sources, spatial resolution and system limitations, is provided. The comparisons between OCT and other clinical oral diagnostic methods are also discussed.

Intraoperative application of optical coherence tomography for lung tumor.

On-site instant determination of benign or malignant tumors for deciding the types of resection is crucial during pulmonary surgery. We designed a portable spectral-domain optical coherence tomography (SD-OCT) system to do real-time scanning intraoperatively for the distinction of fresh tumor specimens in the lung. A total of 12 ex vivo lung specimens from six patients were enrolled. Three patients were diagnosed with invasive adenocarcinoma (IA), while the others were benign. After OCT-imaged reconstruction, we compared the qualitative morphology of OCT and histology among malignant, benign, and normal tissues. In addition, through analysis of the quantitative data, a discrete difference in optical attenuation coefficients around the junctional surface was shown by our data processing. This study demonstrated a feasible OCT-assisted resection guide by a rapid on-site tumor diagnosis. The results indicate that future deep learning of OCT-captured image systems able to improve diagnostic and therapeutic efficiency is warranted.

Characterization of pathogenic factors for premenstrual dysphoric disorder using machine learning algorithms in rats.

We established a methodology using machine learning algorithms for determining the pathogenic factors for premenstrual dysphoric disorder (PMDD). PMDD is a disease characterized by emotional and physical symptoms that occurs before menstruation in women of childbearing age. Owing to the diverse manifestations and various pathogenic factors associated with this disease, the diagnosis of PMDD is time-consuming and challenging. In the present study, we aimed to establish a methodology for diagnosing PMDD. Using an unsupervised machine-learning algorithm, we divided pseudopregnant rats into three clusters (C1 to C3), depending on the level of anxiety- and depression-like behaviors. From the results of RNA-seq and subsequent qPCR of the hippocampus in each cluster, we identified 17 key genes for building a PMDD diagnostic model using our original two-step feature selection with supervised machine learning. By inputting the expression levels of these 17 genes into the machine learning classifier, the PMDD symptoms of another group of rats were successfully classified as C1-C3 with an accuracy of 96%, corresponding to the classification by behavior. The present methodology would be applicable for the clinical diagnosis of PMDD using blood samples instead of samples from the hippocampus in the future.

Using Signal Features of Functional Near-Infrared Spectroscopy for Acute Physiological Score Estimation in ECMO Patients.

Extracorporeal membrane oxygenation (ECMO) is a vital emergency procedure providing respiratory and circulatory support to critically ill patients, especially those with compromised cardiopulmonary function. Its use has grown due to technological advances and clinical demand. Prolonged ECMO usage can lead to complications, necessitating the timely assessment of peripheral microcirculation for an accurate physiological evaluation. This study utilizes non-invasive near-infrared spectroscopy (NIRS) to monitor knee-level microcirculation in ECMO patients. After processing oxygenation data, machine learning distinguishes high and low disease severity in the veno-venous (VV-ECMO) and veno-arterial (VA-ECMO) groups, with two clinical parameters enhancing the model performance. Both ECMO modes show promise in the clinical severity diagnosis. The research further explores statistical correlations between the oxygenation data and disease severity in diverse physiological conditions, revealing moderate correlations with the acute physiologic and chronic health evaluation (APACHE II) scores in the VV-ECMO and VA-ECMO groups. NIRS holds the potential for assessing patient condition improvements.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

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

BACKGROUND: Although 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. METHODS: In 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. RESULTS: Our 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. CONCLUSIONS: In 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.

Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet.

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively. Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design. Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues. Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice. Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Degree of polarization uniformity for dental calculus visualization.

During the treatment for periodontitis, the removal of dental calculus is essential. Previously, we have proposed the DAM algorithm for intuitive identification of the site of lesion, enabling the non-contact assessment during the operation. Nonetheless, the delineation of dental calculus was still imperfect. To this end, here we utilized the power of polarization-sensitive optical coherence tomography and evaluated the contrast called degree of polarization uniformity for dental calculus visualization. The result showed that the selected index demonstrated excellent contrast of dental calculus from other normal dental hard tissues. The proposed contrast is promising for accurate dental calculus delineation.

Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task.

Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis.

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.

Cultural Adaptation and Validation of Mullen Scales of Early Learning in Taiwanese children with Autism Spectrum Disorder, Global Developmental Delay, and Typically Developing Children.

BACKGROUND: The Mullen Scales of Early Learning (MSEL) is a standardized comprehensive developmental assessment tool for children aged 0-68 months. However, few Asia-based studies have explored cultural and linguistic adaptations of the MSEL or investigated its psychometric properties in populations with autism spectrum disorder (ASD). AIMS: This study evaluated the reliability and validity of the MSEL-Taiwan version (MSEL-T) for Taiwanese children with ASD, global developmental delay (GDD), and typical development (TD). METHODS AND PROCEDURES: The MSEL items were translated and modified according to the language and culture in Taiwan. In total, 191 children (ASD, 69; GDD, 36; and TD, 86) aged 19-68 months were assessed using the MSEL-T and Peabody Developmental Motor Scales 2 (PDMS-2) at enrollment, followed by the assessments of Vineland Adaptive Behavior Scale-Chinese version (VABS-C) at the age of 36 months or later. OUTCOMES AND RESULTS: All subscales were verified to have good interrater reliability and internal consistency, and subscale scores indicated moderate to high correlations with PDMS-2 and VABS-C scores. Significant differences in MSEL-T scores were observed between same-aged pairs of children with TD and GDD and between pairs of children with TD and ASD. CONCLUSIONS AND IMPLICATIONS: The findings provide evidence of validity and reliability of the MSEL-T. And it is suggested that the culturally and linguistically adapted MSEL-T is a good tool for the clinical assessment of children with and without ASD.

Signal properties of split-spectrum amplitude decorrelation angiography for quantitative optical coherence tomography-based velocimetry.

Split-spectrum amplitude-decorrelation angiography (SSADA) is a noninvasive and three-dimensional angiographic technique with a microscale spatial resolution based on optical coherence tomography. The SSADA signal is known to be correlated with the blood flow velocity and the quantitative velocimetry with SSADA has been expected; however, the signal properties of SSADA are not completely understood due to lack of comprehensive investigations of parameters related to SSADA signals. In this study, phantom experiments were performed to comprehensively investigate the relation of SSADA signals with flow velocities, time separations, particle concentrations, signal-to-noise ratios, beam spot sizes, and viscosities, and revealed that SSADA signals reflect the spatial commonality within a coherence volume between adjacent A-scans.