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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]

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]

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]

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.

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]

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]

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.

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]

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration. [ABSTRACT FROM AUTHOR]

Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in the left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of the left prefrontal cortex.

Touch is an important component of early parent-child interaction and plays a critical role in the socio-emotional development of children. However, there are limited studies on touch processing amongst children in the age range from one to three years. The present study used frequency-domain diffuse optical tomography (DOT) to investigate the processing of affective and non-affective touch over left frontotemporal brain areas contralateral to the stimulated forearm in two-year-old children. Affective touch was administered by a single stroke with a soft brush over the child's right dorsal forearm at 3 cm/s, while non-affective touch was provided by multiple brush strokes at 30 cm/s. We found that in the insula, the total haemoglobin (HbT) response to slow brushing was significantly greater than the response to fast brushing (slow > fast). Additionally, a region in the postcentral gyrus, Rolandic operculum and superior temporal gyrus exhibited greater response to fast brushing than slow brushing (fast > slow). These findings confirm that an adult-like pattern of haemodynamic responses to affective and non-affective touch can be recorded in two-year-old subjects using DOT. To improve the accuracy of modelling light transport in the two-year-old subjects, we used a published age-appropriate atlas and deformed it to match the exterior shape of each subject's head. We estimated the combined scalp and skull, and grey matter (GM) optical properties by fitting simulated data to calibrated and coupling error corrected phase and amplitude measurements. By utilizing a two-compartment cerebrospinal fluid (CSF) model, the accuracy of estimation of GM optical properties and the localization of activation in the insula was improved. The techniques presented in this paper can be used to study neural development of children at different ages and illustrate that the technology is well-tolerated by most two-year-old children and not excessively sensitive to subject movement. The study points the way towards exciting possibilities in functional imaging of deeper functional areas near sulci in small children.

The inverse process of diffuse optical tomography (DOT) is ill-posed, which has a significant effect on the quality of the reconstructed results. In this paper, the polarization characteristics of light are introduced into the inverse solution model of DOT to increase the solution constraints and improve the imaging quality. The spectral element method is used to numerically solve the vector radiative transfer equation, which is used to simulate the propagation of polarized light in a scattering medium. The DOT reverse solution algorithm based on the polarization component is constructed so that the Stokes vector can be used for absorption coefficient and scattering coefficient reconstruction. The simulation and experimental results show that when polarized light is used for incidence, using the intensity component and the multi-polarization component to solve the DOT inverse model can improve the reconstruction accuracy of the medium absorption coefficient distribution and the DOT image quality.

Diffuse optical tomography (DOT) has attracted attentions in the last two decades due to its intrinsic sensitivity in imaging chromophores of tissues such as hemoglobin, water, and lipid. However, DOT has not been clinically accepted yet due to its low spatial resolution caused by strong optical scattering in tissues. Structural guidance provided by an anatomical imaging modality enhances the DOT imaging substantially. Here, we propose a computed tomography (CT) guided multispectral DOT imaging system for breast cancer imaging. To validate its feasibility, we have built a prototype DOT imaging system which consists of a laser at the wavelength of 650 nm and an electron multiplying charge coupled device (EMCCD) camera. We have validated the CT guided DOT reconstruction algorithms with numerical simulations and phantom experiments, in which different imaging setup parameters, such as projection number of measurements and width of measurement patch, have been investigated. Our results indicate that an air-cooling EMCCD camera is good enough for the transmission mode DOT imaging. We have also found that measurements at six angular projections are sufficient for DOT to reconstruct the optical targets with 2 and 4 times absorption contrast when the CT guidance is applied. Finally, we have described our future research plan on integration of a multispectral DOT imaging system into a breast CT scanner.

We previously introduced photo-magnetic imaging (PMI), an imaging technique that illuminates the medium under investigation with near-infrared light and measures the induced temperature increase using magnetic resonance thermometry (MRT). Using a multiphysics solver combining photon migration and heat diffusion, PMI models the spatiotemporal distribution of temperature variation and recovers high resolution optical absorption images using these temperature maps. In this paper, we present a new fast non-iterative reconstruction algorithm for PMI. This new algorithm uses analytic methods during the resolution of the forward problem and the assembly of the sensitivity matrix. We validate our new analytic-based algorithm with the first generation finite element method (FEM) based reconstruction algorithm previously developed by our team. The validation is performed using, first synthetic data and afterwards, real MRT measured temperature maps. Our new method accelerates the reconstruction process 30-fold when compared to a single iteration of the FEM-based algorithm.

Abstract Background The purpose of this study is to evaluate whether the changes in optically derived parameters acquired with a diffuse optical tomography breast imager system (DOTBIS) in the contralateral non-tumor-bearing breast in patients administered neoadjuvant chemotherapy (NAC) for breast cancer are associated with pathologic complete response (pCR). Methods In this retrospective evaluation of 105 patients with stage II–III breast cancer, oxy-hemoglobin (ctO 2 Hb) from the contralateral non-tumor-bearing breast was collected and analyzed at different time points during NAC. The earliest monitoring imaging time point was after 2–3 weeks receiving taxane. Longitudinal data were analyzed using linear mixed-effects modeling to evaluate the contralateral breast ctO 2 Hb changes across chemotherapy when corrected for pCR status, age, and BMI. Results Patients who achieved pCR to NAC had an overall decrease of 3.88 μM for ctO 2 Hb (95% CI, 1.39 to 6.37 μM), p = .004, after 2–3 weeks. On the other hand, non-pCR subjects had a non-significant mean reduction of 0.14 μM (95% CI, − 1.30 to 1.58 μM), p > .05. Mixed-effect model results indicated a statistically significant negative relationship of ctO 2 Hb levels with BMI and age. Conclusions This study demonstrates that the contralateral normal breast tissue assessed by DOTBIS is modifiable after NAC, with changes associated with pCR after only 2–3 weeks of chemotherapy.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction. [ABSTRACT FROM AUTHOR]

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.

Diffuse optical tomography (DOT), as a functional near-infrared spectroscopy (fNIRS) technique, can estimate three-dimensional (3D) images of the functional hemodynamic response in brain volume from measured optical signals. In this study, we applied DOT algorithms to the fNIRS data recorded from the surface of macaque monkeys' skulls when the animals performed food retrieval tasks using either the left- or right-hand under head-free conditions. The hemodynamic response images, reconstructed by DOT with a high sampling rate and fine voxel size, demonstrated significant activations at the upper limb regions of the primary motor area in the central sulcus and premotor, and parietal areas contralateral to the hands used in the tasks. The results were also reliable in terms of consistency across different recording dates. Time-series analyses of each brain area revealed preceding activity of premotor area to primary motor area consistent with previous physiological studies. Therefore, the fNIRS–DOT protocol demonstrated in this study provides reliable 3D functional brain images over a period of days under head-free conditions for region-of-interest–based time-series analysis. [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.

Diffuse optical tomography, an imaging modality that utilizes near-infrared light, is a new way to assess soft tissue. It provides a non-invasive screening of soft tissue, such as the breast in females and prostate in males, to inspect the existence of cancer. This new imaging method is considered cost-effective and preferred because the implementation is simply through the application of a laser or light-emitting diode as a light source. Near-infrared technology does not only offer cancer screening modality, but also acts as a cancer treatment method, called near-infrared photoimmunotherapy. Despite plentiful studies in the area of near-infrared technology for cancer imaging and cancer cell suppression, there is no consolidated review that provides an overview of near-infrared application in cancer cell imaging and therapy. The objective of this study is to review near-infrared-based medical imaging and novel approaches to eradicate cancer cells. Additionally, we have discussed prospective instrumentation to establish cancer therapeutics apparatuses based on near-infrared technology. This review is expected to guide researchers implementing near-infrared for a medical imaging modality and cancer suppression in vitro, in vivo, and in clinical settings.

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability. [ABSTRACT FROM AUTHOR]

Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12%±40% and 23%±40% , increased the spatial similarity by 17%±17% and 9%±15% , increased the anomaly contrast accuracy by 9%±9% (μa), and reduced the crosstalk by 5%±18% and 7%±11% , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT. [ABSTRACT FROM AUTHOR]

Background: In diffuse optical tomography, determining the optimal angle between the source and detector is an effective method to reduce the number of projections while maintaining the quality of the reconstructed images. In this study, a new parameter is introduced to evaluate the source-detector geometries. Methods: A two-dimensional mesh with the radius of 20 mm and 7987 nodes were built. In each reconstruction, 0.5 mm heterogeneity with the absorption coefficient of 0.06 mm−1 and the dispersion coefficient of 0.6 mm−1 was added in different parts of the sample randomly. The relationship between the mean square error (MSE), sensitivity Laplacian ratio (SLR), and sensitivity standard deviation ratio (SSR) was evaluated based on their correlation coefficients. The quality of the images achieved using the optimized projections were compared with that of the full projections for the same depths.Results: MSE decreases by increasing the SLR magnitudes which indicate that the parameter could be used to evaluate the scanning geometries. There was a negative correlation coefficient (R = −0.76) with the inverse relationship between the SLR and MSE indices. SSR does not have a significant relationship with the quality of the reconstructed images. For each scanning depth, the comparison of the images obtained using the full and optimized-selective projections did not show any considerable difference despite the decrease of the projection numbers in scanning geometry with the optimized-selective projections. Conclusion: The unnecessary projections could be eliminated by placing the detectors at the specific angles, which were determined using the SLR. Thus, a proper compromise between the quality of the reconstructed images and reconstruction time might establish.

Multi-modality imaging leverages the competitive advantage of different imaging systems to improve the overall resolution and quantitative accuracy. Our new technique, Photo-Magnetic Imaging (PMI) is one of these true multi-modality imaging approaches, which can provide quantitative optical absorption map at MRI spatial resolution. PMI uses laser light to illuminate tissue and elevate its temperature while utilizing MR thermometry to measure the laser-induced temperature variation with high spatial resolution. The high-resolution temperature maps are later converted to tissue absorption maps by a finite element based inverse solver that is based on modeling of photon migration and heat diffusion in tissue. Previously, we have demonstrated the feasibility of PMI with phantom studies. Recently, we have managed to reduce the laser power under ANSI limit for maximum skin exposure therefore, we have well positioned PMI for in vivo imaging. Currently we are expanding our system by adding multi-wavelength imaging capability. This will allow us not only to resolve spatial distribution of tissue chromophores but also exogenous contrast agents. Although we test PMIs feasibility with animal studies, our future goal is to use PMI for breast cancer imaging due to its high translational potential.

Diffüz optik tomografi (DOT), kanser tanısı veya kanser takibi için meme dokusunun doku kromoforlarının uzaysal dağılımını görüntülemek için 750-950 nm dalga boyu aralığını kullanır. DOT, doku optik özelliklerinin geri çatım teknikleri ile tomografik olarak gösterilmesini sağlar. Görüntülerdeki gürültüleri en aza indirmek ve daha gerçekçi tomografik görüntüler elde etmek için çeşitli geri çatım teknikleri geliştirilmiştir. Dört farklı geri çatım tekniklerini karşılaştırmak için DOT sistemi ile doku fantomu kullanılarak veriler elde edildi. Doku fantomundaki inklüzyonun konumu belirleme açısından Cebirsel geri çatım tekniği (ART), eşzamanlı cebirsel geri çatım tekniği (SIRT), tekil nokta ayrışması (TSVD) ve kesikli eşlenik gradyent (TCG) teknikleri karşılaştırılmıştır. Alt uzay tekniklerinden TSVD ve TCG ile oluşturulan görüntülerdeki inklüzyonun konumu, cebirsel yöntemlerden ART ve SIRT' den daha iyi konumlandırıldığı gösterilmiştir. Ayrıca, TSVD ve TCG tarafından oluşturulan görüntüler, ART ve SIRT ile oluşturulan görüntülere göre daha az gürültü olarak elde edilmiştir.

Abstract We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.

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., (© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Diffuse optical tomography (DOT) is a novel functional imaging technique that has the potential clinical application for breast cancer screening. Currently, an underexploited benefit of DOT is its ability to track and characterize dynamic events related to physiological progression, which can provide additional imaging contrast. In this work, we propose a novel 3-wavelength, 32-source–detector continuous-wave dynamic DOT system that allows relatively fast and high-sensitivity imaging by combining phase lock and photon counting technologies and obtains a balance between cost and effectiveness through series parallel measurement mode. To assess the effectiveness of the proposed system prototype, we systematically investigated the key specifications of the system, including stability, channel cross talk, and dynamic range. Furthermore, the static and dynamic phantom experiments validated the capability of the system to detect the target with low absorption contrast as well as track the changes in the target’s absorption coefficient. These preliminary results indicate that the system has great application prospects in imaging of the low-contrast lesion as well as capturing the dynamic changes of hemodynamics in tumor tissue.

EIT-MESHER (https://github.com/EIT-team/Mesher) is C++ software, based on the CGAL library, which generates high quality Finite Element Model tetrahedral meshes from binary masks of 3D volume segmentations. Originally developed for biomedical applications in Electrical Impedance Tomography (EIT) to address the need for custom, non-linear refinement in certain areas (e.g. around electrodes), EIT-MESHER can also be used in other fields where custom FEM refinement is required, such as Diffuse Optical Tomography (DOT). Funding statement: This work was supported by an ARCHER eCSE grant (eCSE13-11). James Avery was supported by the NIHR Imperial BRC.

It is well acknowledged that the functional near-infrared spectroscopic (fNIRS) imaging of brain functions can be quantitatively enhanced by diffuse optical tomography (DOT). Unlike the widely-used conventional optical topography (optical mapping), an fNIRS-DOT instrument needs to possess both high sensitivity and large dynamic-range so that measurements at multiple source-detector separations are reliably and synchronously collected for improved depth-resolution, quantitation, and superficial signal reduction, the performances particularly crucial to the occipital cortex probing. For the goal, we implemented a novel 3-wavelength, 240-channel (20 sources and 12 detectors) continuous-wave fNIRS-DOT instrument. The system features the square-wave modulation lock-in photon-counting scheme that combines the multi-channel parallelization of the lock-in detection, the ultra-high sensitivity, and unprecedented dynamic-range of the photon-counting technique, as well as the reduced complexity of the hardware architecture. We have systematically investigated the key specifications of the instrument such as the detection linearity, stability, channel crosstalks and so on, under the practically-viable conditions of the incident power and signal-to-noise ratio, and accordingly proposed wavelength-switched multiple-field illumination paradigms for both the non-overlapping and overlapping DOT measurements, respectively. Finally, DOT experiments using the semi-three-dimensional image reconstruction has been conducted on a phantom with a practically low-contrasted and deep-sit absorption target, to evaluate the overall imaging performance of the system and to demonstrate the superiority of the model-based image reconstruction over the topographical mapping.

The diffuse optical tomography (DOT) technique which uses the traditional linear iterative algorithm has the problems of slow calculation speed and low reconstruction imaging accuracy in the inverse problem reconstruction, which limits its clinical application and development. This paper proposes an inverse-problem solving technology based on a stacked auto-encoder (SAE) network to improve the reconstruction accuracy of anomaly position, size and absorption coefficient in tissues. The reconstruction accuracy of anomaly position, size and absorption coefficient obtained by the traditional algebraic reconstruction technique (ART) method and the SAE based method are experimentally compared. The experimental results show that the SAE based method achieves the prediction accuracy of anomaly position of 96.25%, thus improving the accuracy and shortening the reconstruction time compared with the traditional ART method. Accordingly, the proposed method provides a better solution to the problem of the inaccurate reconstruction of the position and size of the rapid DOT based positioning of anomalies.

In this work, a first-of-its-kind fully integrated tri-modality system that combines fluorescence, diffuse optical and x-ray tomography (FT/DOT/XCT) into the same setting is presented. The purpose of this system is to perform quantitative fluorescence tomography using multi-modality imaging approach. XCT anatomical information is used as structural priori while optical background heterogeneity information obtained by DOT measurements is used as functional priori. The performance of the hybrid system is evaluated using multi-modality phantoms. In particular, we show that a 2.4 mm diameter fluorescence inclusion located in a heterogeneous medium can be localized accurately with the functional a priori information, although the fluorophore concentration is recovered with 70% error. On the other hand, the fluorophore concentration can be accurately recovered within 8% error only when both DOT optical background functional and XCT structural a priori information are utilized to guide and constrain the FT reconstruction algorithm. (C) 2010 Optical Society of America