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The inverse problem associated with Diffuse optical tomography image reconstruction is known to be highly nonlinear, under-determined, and ill-posed. The Levenberg-Marquardt technique is employed in solving it and is known to produce low-resolution reconstructed images. To stabilize the inversion of the large matrix, a heuristically chosen regularization parameter is used. A novel methodology is developed using Gauss-Seidel, Modified Richardson, and Kaczmarz recursive methods to solve the inverse problem and to obtain spatially varying regularization parameters, which are optimally obtained for every node automatically, which is otherwise not possible. The proposed methods are thoroughly compared with the existing traditional methods in both 2-D and 3-D imaging domains using numerically simulated noisy data and also real-life phantom data. Of all the proposed methods, the Gauss-Seidel-based method provides a quantitatively accurate estimation of spatially varying regularization using the model-resolution-matrix-based method and hence improves the quality of the reconstructed images with better resolution characteristics. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening syndrome consisting of physiological, pathological, and biochemical abnormalities induced by infection which continues to be a major public health burden. It remains one of the most common reasons for intensive care unit (ICU) admission. Delirium precipitated by sepsis in the intensive care setting is one of its most common neuropsychiatric complications that leads to prolonged hospitalization, increased mortality, and an increased risk of incident dementia. Understanding the pathophysiology and neurobiological mechanisms of sepsis-associated delirium is difficult; neuroimaging biomarkers are lacking due to difficulties with imaging critically ill patients. Optical imaging techniques, including near-infrared spectroscopy and diffuse optical tomography are potentially promising approaches for investigating this pathophysiology due to their portability and high spatiotemporal resolution. In this review, we examine the emergence of optical neuroimaging techniques for the study of sepsis-associated delirium in the ICU and how they can further advance our knowledge and lead to the development of improved preventative, predictive, and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Diffuse optical tomography (DOT) provides three‐dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high‐density (HD) grids of sources and detectors with closest spacing less than 15 mm, or (ii) source modulated light of order ∼100 MHz.However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated.Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal‐to‐noise, and localization depth of field for each of ∼85 000 point spread functions in each model.We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9 mm closest spacing. Further, for a given array density, 300 MHz source modulation provided the deepest reliable imaging compared to other frequencies.Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection. [ABSTRACT FROM AUTHOR]

Significance: Optical mammography as a promising tool for cancer diagnosis has largely fallen behind expectations. Modern machine learning (ML) methods offer ways to improve cancer detection in diffuse optical transmission data. Aim: We aim to quantitatively evaluate the classification of cancer-positive versus cancer-negative patients using ML methods on raw transmission time series data from bilateral breast scans during subjects' rest. Approach: We use a support vector machine (SVM) with hyperparameter optimization and cross-validation to systematically explore a range of data preprocessing and feature-generation strategies. We also apply an automated ML (AutoML) framework to validate our findings. We use receiver operating characteristics and the corresponding area under the curve (AUC) to quantify classification performance. Results: For the sample group available (N = 63, 18 cancer patients), we demonstrate an AUC score of up to 93.3% for SVM classification and up to 95.0% for the AutoML classifier. Conclusions: ML offers a viable strategy for clinically relevant breast cancer diagnosis using diffuse-optical transmission measurements. The diagnostic performance of ML on raw data can outperform traditional statistical biomarkers derived from reconstructed image time series. To achieve clinically relevant performance, our ML approach requires simultaneous bilateral scanning of the breasts with spatially dense channel coverage. [ABSTRACT FROM AUTHOR]

Shear Wave Elastography (SWE) and Ultrasound-guided Diffuse Optical Tomography (US-guided DOT) demonstrate promise in distinguishing between benign and malignant breast lesions. This study aims to assess the feasibility and correlation of SWE and US-guided DOT in evaluating the biological characteristics of breast cancer. A cohort of 235 breast cancer patients with 238 lesions, scheduled for surgery within one to three days, underwent B-mode ultrasound (US), US-guided DOT, and SWE. Parameters such as Total Hemoglobin Concentration (THC), Maximal Elasticity (E max), Mean Elasticity (E mean), Standard Deviation of Elasticity (E sd), and Area Ratio were measured. Correlation with post-surgical pathology reports was examined to explore associations between THC, SWE Parameters, and pathology characteristics. Lesions in patient groups with ER-, PR-, HER2 + , high Ki67, LVI+ , and ALN+ exhibited higher THC, E max , and E sd compared to groups with ER+ , PR+ , HER2-, low Ki67, LVI-, and ALN-. The increase was seen in all grades of IDC-I to -III. THC significantly correlated with S max (r = 0.340, P < 0.001), E max (r = 0.339, P < 0.001), E mean (r = 0.201, P = 0.003), and E sd (r = 0.313, P < 0.001). US-guided DOT and SWE prove valuable for the quantitative assessment of breast cancer's biological characteristics, with THC positively correlated with E max , E mean , and E sd. [ABSTRACT FROM AUTHOR]

Three critical problems, including high-cost instrumentations, time-consuming image recovery, and low image quality, limit clinical applications of diffuse optical tomography (DOT). Image reconstruction based on deep learning can enhance the image quality of DOT, especially in the case of low number of measurements where image reconstruction becomes a more ill-posed and underdetermined problem. Here, we present a sparse-view image reconstruction based on deep learning to recover the absorption coefficient of a phantom. The presented neural network enhances image quality and the results show that deep learning can enhance the contrast to noise-ratio effectively (more than 80%). The presented method provides recovered images with more accurate localization capabilities (an increase in localization metric by more than 30% compared with the classic method). Moreover, the time of image reconstruction is reduced by three orders of magnitude. This method can be applied for single-source DOT that significantly reduces the cost of required instruments for brain imaging and cervical or thyroid cancer screening. [ABSTRACT FROM AUTHOR]

In this paper, we consider inverse shape problems coming from diffuse optical tomography and the Helmholtz equation. In both problems, our goal is to reconstruct small volume interior regions from measured data on the exterior surface of an object. In order to achieve this, we will derive an asymptotic expansion of the reciprocity gap functional associated with each problem. The reciprocity gap functional takes in the measured Cauchy data on the exterior surface of the object. In diffuse optical tomography, we prove that a MUSIC-type algorithm that does not require evaluating the Green's function can be used to recover the unknown subregions. This gives an analytically rigorous and computationally simple method for recovering the small volume regions. For the problem coming from inverse scattering, we recover the subregions of interest via a direct sampling method. The direct sampling method presented here allows us to accurately recover the small volume region from one pair of Cauchy data, requiring less data than many direct sampling methods. We also prove that the direct sampling method is stable with respect to noisy data. Numerical examples will be presented for both cases in two dimensions where the measurement surface is the unit circle. [ABSTRACT FROM AUTHOR]

Touch is an essential form of mother-child interaction, instigating better social bonding and emotional stability. We used diffuse optical tomography to explore the relationship between total haemoglobin (HbT) responses to affective touch in the child's brain at two years of age and maternal self-reported prenatal depressive symptoms (EPDS). Affective touch was implemented via slow brushing of the child's right forearm at 3 cm/s and non-affective touch via fast brushing at 30 cm/s and HbT responses were recorded on the left hemisphere. We discovered a cluster in the postcentral gyrus exhibiting a negative correlation (Pearson's r = −0.84, p = 0.015 corrected for multiple comparisons) between child HbT response to affective touch and EPDS at gestational week 34. Based on region of interest (ROI) analysis, we found negative correlations between child responses to affective touch and maternal prenatal EPDS at gestational week 14 in the precentral gyrus, Rolandic operculum and secondary somatosensory cortex. The responses to non-affective touch did not correlate with EPDS in these regions. The number of mother-child dyads was 16. However, by utilising high-density optode arrangements, individualised anatomical models, and video and accelerometry to monitor movement, we were able to minimize methodological sources of variability in the data. The results show that maternal depressive symptoms during pregnancy may be associated with reduced child responses to affective touch in the temporoparietal cortex. Responses to affective touch may be considered as potential biomarkers for psychosocial development in children. Early identification of and intervention in maternal depression may be important already during early pregnancy. • Self-reported depression scores (EPDS) were collected from mothers during pregnancy. • Hemodynamic responses to affective touch in two-year-old child brain were recorded. • Child affective touch responses correlated negatively with prenatal EPDS. • Correlation was found in the Postcentral gyrus and Rolandic operculum. • Non-affective touch responses in same areas showed no significant correlation. [ABSTRACT FROM AUTHOR]

Significance: Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes. Aim: We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT. Approach: Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers. Results: Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (p = 0.042). In addition, the change in normalized tumor StO2 upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (p = 0.038). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI. Conclusions: These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Delirium is associated with mortality and new onset dementia, yet the underlying pathophysiology remains poorly understood. Development of imaging biomarkers has been difficult given the challenging nature of imaging delirious patients. Diffuse optical tomography (DOT) offers a promising approach for investigating delirium given its portability and three‐dimensional capabilities. METHODS: Twenty‐five delirious and matched non‐delirious patients (n = 50) were examined using DOT, comparing cerebral oxygenation and functional connectivity in the prefrontal cortex during and after an episode of delirium. RESULTS: Total hemoglobin values were significantly decreased in the delirium group, even after delirium resolution. Functional connectivity between the dorsolateral prefrontal cortex and dorsomedial prefrontal cortex was strengthened post‐resolution compared to during an episode; however, this relationship was still significantly weaker compared to controls. DISCUSSION: These findings highlight DOT's potential as an imaging biomarker to measure impaired cerebral oxygenation and functional dysconnectivity during and after delirium. Future studies should focus on the role of cerebral oxygenation in delirium pathogenesis and exploring the etiological link between delirium and dementias. Highlights: We developed a portable diffuse optical tomography (DOT) system for bedside three‐dimensional functional neuroimaging to study delirium in the hospital.We implemented a novel DOT task‐focused seed‐based correlation analysis.DOT revealed decreased cerebral oxygenation and functional connectivity strength in the delirium group, even after resolution of delirium. [ABSTRACT FROM AUTHOR]

Ultrasound (US)‐guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real‐time or near real‐time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real‐time diagnosis. Here, we propose a real‐time classification scheme that combines US breast imaging reporting and data system (BI‐RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI‐RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening syndrome consisting of physiological, pathological, and biochemical abnormalities induced by infection which continues to be a major public health burden. It remains one of the most common reasons for intensive care unit (ICU) admission. Delirium precipitated by sepsis in the intensive care setting is one of its most common neuropsychiatric complications that leads to prolonged hospitalization, increased mortality, and an increased risk of incident dementia. Understanding the pathophysiology and neurobiological mechanisms of sepsis-associated delirium is difficult; neuroimaging biomarkers are lacking due to difficulties with imaging critically ill patients. Optical imaging techniques, including near-infrared spectroscopy and diffuse optical tomography are potentially promising approaches for investigating this pathophysiology due to their portability and high spatiotemporal resolution. In this review, we examine the emergence of optical neuroimaging techniques for the study of sepsis-associated delirium in the ICU and how they can further advance our knowledge and lead to the development of improved preventative, predictive, and therapeutic strategies.

Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations. Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use. Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants. Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies. Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans. [ABSTRACT FROM AUTHOR]

Functional neuroimaging studies of neuropsychiatric disorders and cognitive impairment are commonly conducted in the clinic setting but less so in the acutely medically ill while hospitalized. This is largely due to technical and logistical limitations, given the lack of portable devices with high spatial and temporal resolutions. This exploratory study reports on the development and implementation of a novel diffuse optical tomography (DOT) system that can be employed for bedside three-dimensional functional neuroimaging. To test this portable DOT system, our protocol included a task-based sequence involving the Months Backwards Test with imaging centered on the bilateral prefrontal cortex. Fifteen subjects were recruited from intensive care units and the general wards of a single tertiary academic hospital and included in our final analysis. Volumetric hemoglobin analyses of the dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) were reliably captured in all our subjects. The peak value was calculated to be 3.36 µM and 0.74 µM for oxygenated-hemoglobin (HbO) and total-hemoglobin (HbT) (p < 0.042, [HbT]), respectively. The standard error was calculated to be 4.58 uM and 3.68 uM for (HbO) and (HbT). We additionally developed a seed-based correlation analysis to demonstrate the capability of DOT in studying functional connectivity. The right DLPFC was found to be moderately associated with the left DLPFC in all our subjects (r = 0.656). The DMPFC was observed to be associated with the left DLPFC but less so (r = 0.273) at the group level. Overall, the contribution of left-to-right DLPFC connectivity was significantly higher than left DLPFC to DMPFC in our group (p = 0.012). Future studies should investigate the potential of such a DOT system in the research of neuropsychiatric and neurocognitive disorders within the hospital to study different types of mechanisms, pathophysiology, and interventions that occur acutely and can advance our knowledge of these disorders. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.

Computed Tomography (CT) is an essential imaging tool for medical inspection, diagnosis and prevention. While X-rays CT is a consolidated technology, there is nowadays a strong drive for innovation in this field. Between the emerging topics, Diffuse Optical Tomography (DOT) is an instance of Diffuse Optical Imaging which uses non-ionizing light in the near-infrared (NIR) band as investigating signal. Non-trivial challenges accompany DOT reconstruction, which is a severely ill-conditioned inverse problem due to the highly scattering nature of the propagation of light in biological tissues. Correspondingly, the solution of this problem is far from being trivial. In this review paper, we first recall the theoretical basis of NIR light propagation, the relevant mathematical models with their derivation in the perspective of a hierarchy of modeling approaches and the analytical results on the uniqueness issue and stability estimates. Then we describe the state-of-the-art in analytic theory and in computational and algorithmic methods. We present a survey of the few contributions regarding DOT reconstruction aided by machine learning approaches and we conclude providing perspectives in the mathematical treatment of this highly challenging problem. [ABSTRACT FROM AUTHOR]

Delirium persists as the most common neuropsychiatric syndrome among medically ill hospitalized patients, yet its neural mechanisms remain poorly understood. The development of neuroimaging biomarkers has been difficult primarily due to the complexities of imaging patients experiencing delirium. Optical imaging techniques, including near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), offer promising avenues for investigating delirium's pathophysiology. These modalities uniquely stand out for delirium exploration due to their blend of spatiotemporal resolution, bedside applicability, cost-effectiveness, and potential for real-time monitoring. In this review, we examine the emergence of optical imaging modalities and their pioneering utility in delirium research. With further investment and research efforts, they will become instrumental in our understanding of delirium's pathophysiology and the development of preventive, predictive, and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Optical imaging involves the propagation of light through tissue. Current optical breast imaging technologies, including diffuse optical spectroscopy, diffuse optical tomography, and photoacoustic imaging, capitalize on the selective absorption of light in the near-infrared spectrum by deoxygenated and oxygenated hemoglobin. They provide information on the morphological and functional characteristics of different tissues based on their varied interactions with light, including physiologic information on lesion vascular content and anatomic information on tissue vascularity. Fluorescent contrast agents, such as indocyanine green, are used to visualize specific tissues, molecules, or proteins depending on how and where the agent accumulates. In this review, we describe the physical principles, spectrum of technologies, and clinical applications of the most common optical systems currently being used or developed for breast imaging. Most notably, US co-registered photoacoustic imaging and US-guided diffuse optical tomography have demonstrated efficacy in differentiating benign from malignant breast masses, thereby improving the specificity of diagnostic imaging. Diffuse optical tomography and diffuse optical spectroscopy have shown promise in assessing treatment response to preoperative systemic therapy, and photoacoustic imaging and diffuse optical tomography may help predict tumor phenotype. Lastly, fluorescent imaging using indocyanine green dye performs comparably to radioisotope mapping of sentinel lymph nodes and appears to improve the outcomes of autologous tissue flap breast reconstruction. [ABSTRACT FROM AUTHOR]

This work presents a generalized angle-dependent ray transfer function that can accurately map the angular and spatial distribution of light intensities on the tissue surface onto a camera image plane in a non-contact camera-based imaging system. The method developed here goes beyond existing ray transfer models that apply to angle-averaged tomographic data alone. The angle-dependent ray transfer operator was constructed using backward ray tracing based on radiation surface theory. The proposed method was validated using numerical phantoms and experimental data from an actual non-contact imaging system. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.

Functional neuroimaging studies of neuropsychiatric disorders and cognitive impairment are commonly conducted in the clinic setting but less so in the acutely medically ill while hospitalized. This is largely due to technical and logistical limitations, given the lack of portable devices with high spatial and temporal resolutions. This exploratory study reports on the development and implementation of a novel diffuse optical tomography (DOT) system that can be employed for bedside three-dimensional functional neuroimaging. To test this portable DOT system, our protocol included a task-based sequence involving the Months Backwards Test with imaging centered on the bilateral prefrontal cortex. Fifteen subjects were recruited from intensive care units and the general wards of a single tertiary academic hospital and included in our final analysis. Volumetric hemoglobin analyses of the dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) were reliably captured in all our subjects. The peak value was calculated to be 3.36 µM and 0.74 µM for oxygenated-hemoglobin (HbO) and total-hemoglobin (HbT) (p < 0.042, [HbT]), respectively. The standard error was calculated to be 4.58 uM and 3.68 uM for (HbO) and (HbT). We additionally developed a seed-based correlation analysis to demonstrate the capability of DOT in studying functional connectivity. The right DLPFC was found to be moderately associated with the left DLPFC in all our subjects (r = 0.656). The DMPFC was observed to be associated with the left DLPFC but less so (r = 0.273) at the group level. Overall, the contribution of left-to-right DLPFC connectivity was significantly higher than left DLPFC to DMPFC in our group (p = 0.012). Future studies should investigate the potential of such a DOT system in the research of neuropsychiatric and neurocognitive disorders within the hospital to study different types of mechanisms, pathophysiology, and interventions that occur acutely and can advance our knowledge of these disorders.

Abstract Transcranial magnetic stimulation (TMS) has been established as an important and effective treatment for various psychiatric disorders. However, its effectiveness has likely been limited due to the dearth of neuronavigational tools for targeting purposes, unclear ideal stimulation parameters, and a lack of knowledge regarding the physiological response of the brain to TMS in each psychiatric condition. Modern optical imaging modalities, such as functional near-infrared spectroscopy and diffuse optical tomography, are promising tools for the study of TMS optimization and functional targeting in psychiatric disorders. They possess a unique combination of high spatial and temporal resolutions, portability, real-time capability, and relatively low costs. In this mini-review, we discuss the advent of optical imaging techniques and their innovative use in several psychiatric conditions including depression, panic disorder, phobias, and eating disorders. With further investment and research in the development of these optical imaging approaches, their potential will be paramount for the advancement of TMS treatment protocols in psychiatry.

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification. [ABSTRACT FROM AUTHOR]

We address the inverse problem in Optical Tomography of stably determining the optical properties of an anisotropic medium $ \Omega\subset\mathbb{R}^n $, with $ n\geq 3 $, under the so-called diffusion approximation. Assuming that the scattering coefficient $ \mu_s $ is known, we prove Hölder stability of the derivatives of any order of the absorption coefficient $ \mu_a $ at the boundary $ \partial\Omega $ in terms of the measurements, in the time-harmonic case, where the anisotropic medium $ \Omega $ is interrogated with an input field that is modulated with a fixed harmonic frequency $ \omega = \frac{k}{c} $, where $ c $ is the speed of light and $ k $ is the wave number. The stability estimates are established under suitable conditions that include a range of variability for $ k $ and they rely on the construction of singular solutions of the underlying forward elliptic system, which extend results obtained in J. Differential Equations 84 (2): 252-272 for the single elliptic equation and those obtained in Applicable Analysis DOI:10.1080/00036811.2020.1758314, where a Lipschitz type stability estimate of $ \mu_a $ on $ \partial\Omega $ was established in terms of the measurements. [ABSTRACT FROM AUTHOR]

Graph total variation (GTV) is a powerful regularization tool for diffuse optical tomography (DOT) reconstruction since it combines the powerful representation ability of graph and the edge‐preserving ability of total variation (TV) regularization. However, as everyone knows, the classical TV regularization trend underestimates the large edge values. In this paper, we propose a convex–nonconvex graph total variation (CNC‐GTV) regularization for DOT reconstruction. In particular, we construct a nonconvex regularization by subtracting the generalized Huber function from the GTV regularization. We show that the global convexity of the objective function can be guaranteed by adjusting the nonconvex control parameters. Moreover, we present an alternating direction multiplier method (ADMM) to solve the proposed DOT reconstruction model. Numerical experiments show that the proposed model outperforms existing models in visual and numerical results. [ABSTRACT FROM AUTHOR]

Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called "XGBoost" to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth. [ABSTRACT FROM AUTHOR]

Diffuse optical tomography (DOT) is promising to investigate functional and structural information in biological tissue by involving the forward problem and inverse solution. The work within this study develops an image reconstruction algorithm based on frequency-domain DOT using multi-frequency. In the forward problem, numerical simulation performs a diffusion equation to compute the light radiance distribution inside the model geometry with the assumed light intensity and optical properties. While in inverse solution, the absorption and reduced scattering coefficients are reconstructed utilizing light boundary data. To test the algorithm, several simulation cases, such as different contrast ratios between background and inclusion, inclusion size, as well as single and multi-frequency using one to three frequencies each and simultaneously are implemented. For verifying the image reconstruction results of multi-frequency, an objective numerical assessment, namely contrast and size detail is applied with a threshold value to specify the visible inclusion inside the geometry. Reconstructed images indicate that the effectivity of two frequencies in multi-frequency with 100 and 150 MHz has similar results as in single-frequency with 200 MHz. Therefore, the more number of frequencies may not enhance the contrast and image quality.

Delirium persists as the most common neuropsychiatric syndrome among medically ill hospitalized patients, yet its neural mechanisms remain poorly understood. The development of neuroimaging biomarkers has been difficult primarily due to the complexities of imaging patients experiencing delirium. Optical imaging techniques, including near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), offer promising avenues for investigating delirium’s pathophysiology. These modalities uniquely stand out for delirium exploration due to their blend of spatiotemporal resolution, bedside applicability, cost-effectiveness, and potential for real-time monitoring. In this review, we examine the emergence of optical imaging modalities and their pioneering utility in delirium research. With further investment and research efforts, they will become instrumental in our understanding of delirium’s pathophysiology and the development of preventive, predictive, and therapeutic strategies.

The forward and inverse problem are the important parts of the Diffuse Optical Tomography system. As well as the correct determination of the optode number and good placement of the source-detector matches, the regularization method to be used relative to the selected geometry is also important. The higher the number of source-detector, the higher cost and computation time. These parameters can be reduced with a correct placement. The aim of this study is to show that the success to be obtained from a vast amount of source detector matches can also be obtained from few number of source detector matches with the correct regularization method and optode geometry. In this paper, a low cost and effective optode geometry was studied. Firstly a custom optode geometry was designed. In addition to this geometry, four different source-detector placements that are frequently used in other studies were selected to test the performance of proposed optode geometry. Then forward model weight matrix was generated for all geometries. Ordinary Least Squares, Least Absolute Shrinkage and Selection Operator Regression, Ridge Regression and Elastic-Net regularization methods were used to solve inverse problem. The quality of the reconstructed images of optode geometries were examined by comparing synthetic data with and without noise. The results show that the proposed geometry and the correct regularization method has better estimation accuracy and lower computation time more robust to noise than the other cost-effective geometries. [ABSTRACT FROM AUTHOR]

Near infrared diffuse optical tomography (DOT) is a potential tool for diagnosing cancer by image reconstruction of tissue optical properties. A variety of image reconstruction methods for DOT have been attempted, in general, based on the diffusion equation (DE). However, the image quality is still insufficient to clinical use, which is mainly attributed to the fact that the DE is invalid in some regions, such as low-scattering regions, and the inverse problem is inherently ill-posed. In contrast, the radiative transfer equation (RTE) accurately describes light propagation in biological tissue and also the DOT by deep learning is recently thought to be an alternative approach to the inverse problem. Distribution of time of flight (DTOF) of photons estimated by the time-domain RTE lends itself to deep learning along a temporal sequence. In this study, we propose a new DOT image reconstruction algorithm based on a long-short-term memory and the time-domain RTE. In simulation studies, using this algorithm, we succeeded in detection of an absorbing inclusion with a diameter of 5 mm, an absorber mimicking cancer, which was embedded in a two-dimensional square model (4 cm × 4 cm) with an optically homogeneous background. Multiple absorbers and a bigger absorber embedded in this model were also detected. We also demonstrate that, if simulation data by beam injection from multiple directions are employed as a training set, the accuracy of detection is improved especially for multiple absorbers. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) has been established as an important and effective treatment for various psychiatric disorders. However, its effectiveness has likely been limited due to the dearth of neuronavigational tools for targeting purposes, unclear ideal stimulation parameters, and a lack of knowledge regarding the physiological response of the brain to TMS in each psychiatric condition. Modern optical imaging modalities, such as functional near-infrared spectroscopy and diffuse optical tomography, are promising tools for the study of TMS optimization and functional targeting in psychiatric disorders. They possess a unique combination of high spatial and temporal resolutions, portability, real-time capability, and relatively low costs. In this mini-review, we discuss the advent of optical imaging techniques and their innovative use in several psychiatric conditions including depression, panic disorder, phobias, and eating disorders. With further investment and research in the development of these optical imaging approaches, their potential will be paramount for the advancement of TMS treatment protocols in psychiatry. [ABSTRACT FROM AUTHOR]

Diffuse optical tomography (DOT) has been studied for use in the detection of breast cancer, cerebral oxygenation, and cognitive brain signals. As optical imaging studies have increased significantly, acquiring imaging data in real time has become increasingly important. We have developed frequency-division multiplexing (FDM) DOT systems to analyze their performance with respect to acquisition time and imaging quality, in comparison with the conventional time-division multiplexing (TDM) DOT. A large tomographic area of a cylindrical phantom 60 mm in diameter could be successfully reconstructed using both TDM DOT and FDM DOT systems. In our experiment with 6 source-detector (S-D) pairs, the TDM DOT and FDM DOT systems required 6.18 and 1 s, respectively, to obtain a single tomographic data set. While the absorption coefficient of the reconstruction image was underestimated in the case of the FDM DOT, we experimentally confirmed that the abnormal region can be clearly distinguished from the background phantom using both methods.

Image reconstruction in diffuse optical tomography (DOT) is a challenging task because its inverse problem is nonlinear, ill-posed, and ill-conditioned. Anatomical guidance from imaging modalities with high spatial resolution can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we proposed a kernel method to introduce anatomical guidance into the DOT image reconstruction. In this kernel method, the optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT) images. Compared with Laplacian approaches that include structural priors, the proposed method does not require image segmentation. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. 5% Gaussian noise was added to both the numerical DOT measurements and the simulated CT images. The proposed method was also validated by an agar phantom experiment with the anatomical guidance from a cone beam CT scan. The effects of voxel size and number of nearest neighbors in the kernel method on the reconstructed DOT images were studied. The results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method comparing to the case without guidance. Furthermore, we demonstrated that the kernel method was able to utilize clinical breast CT images as anatomical guidance without segmentation. In addition, we found that the proposed kernel method was robust to the false positive guidance in the anatomical image.

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis. We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy. We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data. Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile. [ABSTRACT FROM AUTHOR]

Diffuse optical tomography (DOT) is promising to investigate functional and structural information in biological tissue by involving the forward problem and inverse solution. The work within this study develops an image reconstruction algorithm based on frequency-domain DOT using multi-frequency. In the forward problem, numerical simulation performs a diffusion equation to compute the light radiance distribution inside the model geometry with the assumed light intensity and optical properties. While in inverse solution, the absorption and reduced scattering coefficients are reconstructed utilizing light boundary data. To test the algorithm, several simulation cases, such as different contrast ratios between background and inclusion, inclusion size, as well as single and multi-frequency using one to three frequencies each and simultaneously are implemented. For verifying the image reconstruction results of multi-frequency, an objective numerical assessment, namely contrast and size detail is applied with a threshold value to specify the visible inclusion inside the geometry. Reconstructed images indicate that the effectivity of two frequencies in multi-frequency with 100 and 150 MHz has similar results as in single-frequency with 200 MHz. Therefore, the more number of frequencies may not enhance the contrast and image quality. [ABSTRACT FROM AUTHOR]

We study the inverse problem in Optical Tomography of determining the optical properties of a medium Ω ⊂ R n , with n ≥ 3 , under the so-called diffusion approximation. We consider the time-harmonic case where Ω is probed with an input field that is modulated with a fixed harmonic frequency ω = k / c , where c is the speed of light and k is the wave number. We prove a result of Lipschitz stability of the absorption coefficient μ a at the boundary ∂ Ω in terms of the measurements in the case when the scattering coefficient μ s is assumed to be known and k belongs to certain intervals depending on some a-priori bounds on μ a , μ s . [ABSTRACT FROM AUTHOR]