Diffuse optical tomography for mapping cerebral hemodynamics and functional connectivity in delirium

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.

Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment

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.

Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography

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.

Near-Infrared Fluorescence Tomography and Imaging of Ventricular Cerebrospinal Fluid Flow and Extracranial Outflow in Non-Human Primates

The role of the lymphatics in the clearance of cerebrospinal fluid (CSF) from the brain has been implicated in multiple neurodegenerative conditions. In premature infants, intraventricular hemorrhage causes increased CSF production and, if clearance is impeded, hydrocephalus and severe developmental disabilities can result. In this work, we developed and deployed near-infrared fluorescence (NIRF) tomography and imaging to assess CSF ventricular dynamics and extracranial outflow in similarly sized, intact non-human primates (NHP) following microdose of indocyanine green (ICG) administered to the right lateral ventricle. Fluorescence optical tomography measurements were made by delivering ~10 mW of 785 nm light to the scalp by sequential illumination of 8 fiber optics and imaging the 830 nm emission light collected from 22 fibers using a gallium arsenide intensified, charge coupled device. Acquisition times were 16 seconds. Image reconstruction used the diffusion approximation and hard-priors obtained from MRI to enable dynamic mapping of ICG-laden CSF ventricular dynamics and drainage into the subarachnoid space (SAS) of NHPs. Subsequent, planar NIRF imaging of the scalp confirmed extracranial efflux into SAS and abdominal imaging showed ICG clearance through the hepatobiliary system. Necropsy confirmed imaging results and showed that deep cervical lymph nodes were the routes of extracranial CSF egress. The results confirm the ability to use trace doses of ICG to monitor ventricular CSF dynamics and extracranial outflow in NHP. The techniques may also be feasible for similarly-sized infants and children who may suffer impairment of CSF outflow due to intraventricular hemorrhage.

Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications

Significance

To effectively apply functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT) devices, a three-dimensional (3D) model of the position of each optode on a subject's scalp and the positions of that subject's cranial landmarks are critical. Obtaining this information accurately in infants, who rarely stop moving, is an ongoing challenge.

Aim

We propose a smartphone-based registration system that can potentially achieve a full-head 3D scan of a 6-month-old infant instantly.

Approach

The proposed system is remotely controlled by a custom-designed Bluetooth controller. The scanned images can either be manually or automatically aligned to generate a 3D head surface model.

Results

A full-head 3D scan of a 6-month-old infant can be achieved within 2 s via this system. In testing on a realistic but static infant head model, the average Euclidean error of optode position using this device was 1.8 mm.

Conclusions

This low-cost 3D registration system therefore has the potential to permit accurate and near-instant fNIRS/DOT spatial registration.

Optical Breast Imaging: A Review of Physical Principles, Technologies, and Clinical Applications

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.

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning

Significance

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

Aim

We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.

Approach

We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.

Results

The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.

Conclusions

The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight)

Significance

We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature.

Aim

Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research.

Approach

We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules.

Results

The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy.

Conclusions

The advances presented here should serve to make fNIRS more accessible for BCI applications.

Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy

Significance

Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance.

Aim

Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously.

Approach

The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT.

Results

The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation.

Conclusions

The SS-DOT model improves the fNIRS image reconstruction quality.

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology.

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

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.

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

SIGNIFICANCE

The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently.

AIM

This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages.

APPROACH

The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including 16 × 15 , 20 × 19 , and 36 × 35 boundary measurement setups.

RESULTS

The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors.

CONCLUSIONS

The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Algorithms and instrumentation for rapid spatial frequency domain fluorescence diffuse optical imaging

SIGNIFICANCE

Rapid estimation of the depth and margins of fluorescence targets buried below the tissue surface could improve upon current image-guided surgery techniques for tumor resection.

AIM

We describe algorithms and instrumentation that permit rapid estimation of the depth and transverse margins of fluorescence target(s) in turbid media; the work aims to introduce, experimentally demonstrate, and characterize the methodology.

APPROACH

Spatial frequency domain fluorescence diffuse optical tomography (SFD-FDOT) technique is adapted for rapid and computationally inexpensive estimation of fluorophore target depth and lateral margins. The algorithm utilizes the variation of diffuse fluorescence intensity with respect to spatial-modulation-frequency to compute target depth. The lateral margins are determined via analytical inversion of the data using depth information obtained from the first step. We characterize method performance using fluorescent contrast targets embedded in tissue-simulating phantoms.

RESULTS

Single and multiple targets with significant lateral size were imaged at varying depths as deep as 1 cm. Phantom data analysis showed good depth-sensitivity, and the reconstructed transverse margins were mostly within ∼30 % error from true margins.

CONCLUSIONS

The study suggests that the rapid SFD-FDOT approach could be useful in resection surgery and, more broadly, as a first step in more rigorous SFD-FDOT reconstructions. The experiments permit evaluation of current limitations.

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach

SIGNIFICANCE

"Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction.

AIM

We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only.

APPROACH

We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions.

RESULTS

The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data.

CONCLUSIONS

The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.

Prefrontal cortex supports speech perception in listeners with cochlear implants

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.

A simple algorithm for diffuse optical tomography without Jacobian inversion

A computationally simpler algorithm to reconstruct the optical property distribution of turbid media using diffuse optical tomographic principles is presented. The proposed algorithm eliminates the requirement of large Jacobian matrix inversion which otherwise is essential for tomographic imaging. The most significant Jacobians are identified based on proper thresholding of the measurement and the intersection of these Jacobians gives the approximate spatial location of the inhomogeneity. The algorithm is tested and optimized using simulations and further validated using tissue-mimicking phantom-based experiments andin-vivosmall-animal experiments.

Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications

SIGNIFICANCE

Deep learning (DL) models are being increasingly developed to map sensor data to the image domain directly. However, DL methodologies are data-driven and require large and diverse data sets to provide robust and accurate image formation performances. For research modalities such as 2D/3D diffuse optical imaging, the lack of large publicly available data sets and the wide variety of instrumentation designs, data types, and applications leads to unique challenges in obtaining well-controlled data sets for training and validation. Meanwhile, great efforts over the last four decades have focused on developing accurate and computationally efficient light propagation models that are flexible enough to simulate a wide variety of experimental conditions.

AIM

Recent developments in Monte Carlo (MC)-based modeling offer the unique advantage of simulating accurately light propagation spatially, temporally, and over an extensive range of optical parameters, including minimally to highly scattering tissue within a computationally efficient platform. Herein, we demonstrate how such MC platforms, namely "Monte Carlo eXtreme" and "Mesh-based Monte Carlo," can be leveraged to generate large and representative data sets for training the DL model efficiently.

APPROACH

We propose data generator pipeline strategies using these platforms and demonstrate their potential in fluorescence optical topography, fluorescence optical tomography, and single-pixel diffuse optical tomography. These applications represent a large variety in instrumentation design, sample properties, and contrast function.

RESULTS

DL models trained using the MC-based in silico datasets, validated further with experimental data not used during training, show accurate and promising results.

CONCLUSION

Overall, these MC-based data generation pipelines are expected to support the development of DL models for rapid, robust, and user-friendly image formation in a wide variety of applications.

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

SIGNIFICANCE

Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions.

AIM

To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information.

APPROACH

A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction.

RESULTS

The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%.

CONCLUSIONS

A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.

Deep learning in macroscopic diffuse optical imaging

SIGNIFICANCE

Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.

AIM

We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).

APPROACH

First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.

RESULTS

The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.

CONCLUSIONS

The heavily validated capability of DL's use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient's bedside.

A homogenized cerebrospinal fluid model for diffuse optical tomography in the neonatal head

Diffuse optical tomography is a non-invasive and non-irradiating medical imaging technique that is particularly suitable for cerebral monitoring of newborns since it can be used at the bedside of the patient. Here, a new model for optical tomography in the neonatal brain is presented that takes into account the presence of arachnoid trabeculae in the cerebrospinal fluid (CSF). It is known that the classical diffusion approximation (DA) for light propagation is at the limit of validity in the CSF layer due to the low values of the absorption and scattering coefficients. The new model is obtained by the DA of the homogenized radiative transfer equation and is rigorously justified. Numerical results in two and three dimensions attest for the improved sensitivity of the new model to the presence of perturbations in the brain layer.

Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index

Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties' distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l ₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall

SIGNIFICANCE

In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.

AIM

We aim to reduce the chest wall's effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction.

APPROACH

We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.

RESULTS

The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.

CONCLUSIONS

Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.

Wearable, Integrated EEG-fNIRS Technologies: A Review

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.

[Study on the inverse problem of diffuse optical tomography based on improved stacked auto-encoder]

The inverse problem of diffuse optical tomography (DOT) is ill-posed. Traditional method cannot achieve high imaging accuracy and the calculation process is time-consuming, which restricts the clinical application of DOT. Therefore, a method based on stacked auto-encoder (SAE) was proposed and used for the DOT inverse problem. Firstly, a traditional SAE method is used to solved the inverse problem. Then, the output structure of SAE neural network is improved to a single output SAE, which reduce the burden on the neural network. Finally, the improved SAE method is used to compare with traditional SAE method and traditional levenberg-marquardt (LM) iterative method. The result shows that the average time to solve the inverse problem of the method proposed in this paper is only 1.67% of the LM method. The mean square error (MSE) value is 46.21% lower than the traditional iterative method, 61.53% lower than the traditional SAE method, and the image correlation coefficient(ICC) value is 4.03% higher than the traditional iterative method, 18.7% higher than the traditional SAE method and has good noise immunity under 3% noise conditions. The research results in this article prove that the improved SAE method has higher image quality and noise resistance than the traditional SAE method, and at the same time has a faster calculation speed than the traditional iterative method, which is conducive to the application of neural networks in DOT inverse problem calculation.

Synergistic tomographic image reconstruction: part 2

This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography

Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional  ∼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.

Investigating developmental changes in scalp-to-cortex correspondence using diffuse optical tomography sensitivity in infancy

Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.

Brain-wide functional diffuse optical tomography of resting state networks

Objective.Diffuse optical tomography (DOT) has the potential in reconstructing resting state networks (RSNs) in human brains with high spatio-temporal resolutions and multiple contrasts. While several RSNs have been reported and successfully reconstructed using DOT, its full potential in recovering a collective set of distributed brain-wide networks with the number of RSNs close to those reported using functional magnetic resonance imaging (fMRI) has not been demonstrated.Approach.The present study developed a novel brain-wide DOT (BW-DOT) framework that integrates a cap-based whole-head optode placement system with multiple computational approaches, i.e. finite-element modeling, inverse source reconstruction, data-driven pattern recognition, and statistical correlation tomography, to reconstruct RSNs in dual contrasts of oxygenated (HbO) and deoxygenated hemoglobins (HbR).Main results.Our results from the proposed framework revealed a comprehensive set of RSNs and their subnetworks, which collectively cover almost the entire neocortical surface of the human brain, both at the group level and individual participants. The spatial patterns of these DOT RSNs suggest statistically significant similarities to fMRI RSN templates. Our results also reported the networks involving the medial prefrontal cortex and precuneus that had been missed in previous DOT studies. Furthermore, RSNs obtained from HbO and HbR suggest similarity in terms of both the number of RSN types reconstructed and their corresponding spatial patterns, while HbR RSNs show statistically more similarity to fMRI RSN templates and HbO RSNs indicate more bilateral patterns over two hemispheres. In addition, the BW-DOT framework allowed consistent reconstructions of RSNs across individuals and across recording sessions, indicating its high robustness and reproducibility, respectively.Significance.Our present results suggest the feasibility of using the BW-DOT, as a neuroimaging tool, in simultaneously mapping multiple RSNs and its potential values in studying RSNs, particularly in patient populations under diverse conditions and needs, due to its advantages in accessibility over fMRI.

Quantitative evaluation of frequency domain measurements in high density diffuse optical tomography

SIGNIFICANCE

High density diffuse optical tomography (HD-DOT) as applied in functional near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a single modulation frequency, frequency domain (FD) HD-DOT has recently demonstrated better localization of focal activation as compared to CW data. We show that combining CW and FD measurements and multiple modulation frequencies increases imaging performance in fNIRS.

AIM

We evaluate the benefits of multiple modulation frequencies, combining different frequencies as well as CW data in fNIRS HD-DOT.

APPROACH

A layered model was used, with activation occurring within a cortex layer. CW and FD measurements were simulated at 78, 141, and 203 MHz with and without noise. The localization error, full width half maximum, and effective resolution were evaluated.

RESULTS

Across the average of the three metrics, at 141 MHz, FD performed 8.4% better than CW, and the combination of CW and FD was 21.7% better than CW. FD measurements at 203 MHz performed 5% better than 78 MHz. Moreover, the three combined modulation frequencies of FD and CW performed up to 3.92% better than 141 MHz alone.

CONCLUSIONS

We show that combining CW and FD measurements offers better performance than FD alone, with higher modulation frequencies increasing accuracy. Combining CW and FD measurements at multiple modulation frequencies yields the best overall performance.

Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy. Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.

A Comparison of Two Probes to Determine Rectum Optical Properties

Tissue optical properties are crucial for determining the light dose delivered to the tumor. Two probes are compared: the two-catheter probe is based on transmittance measurement between one point source and one isotropic detector inside parallel catheters spaced at 0.5 cm along a 1-inch diameter transparent cylinder; and a 1-inch trans-rectal diffuse optical tomography (DOT) probe designed for prostate measurements, using a multiple fiber-array with source-detector separations of 1.4-10 mm. The two-catheter probe uses an empirical model for primary and scatter light fluence rates in the cylindrical cavity condition for anal PDT to determine optical properties along the source catheter using dual motors to move the source and detector along the catheters. The DOT probe uses finite element method (FEM) to obtain distribution of optical properties in 3D. Validations for the two probes were performed in liquid and solid phantoms. For each method, validation was performed in tissue-mimicking liquid phantoms for a range of known optical properties (μa between 0.05 and 0.9 cm-1 and μs' between 5.5 and 16.5 cm-1). To cross-check the two methods, solid phantoms were created of known optical properties at the University of Pennsylvania and sent for measurement to Princess Margaret Cancer Centre (PMH) to mimic realistic patient simulating conditions. Measurements were taken and optical properties were then recovered without knowing the expected values to cross-validate each probe. The results show modest agreement between the measured μa and μs'values, but high degree of agreement between the measured μeff performed independently using the two methods.

Mismatch between Tissue Partial Oxygen Pressure and Near-Infrared Spectroscopy Neuromonitoring of Tissue Respiration in Acute Brain Trauma: The Rationale for Implementing a Multimodal Monitoring Strategy

The brain tissue partial oxygen pressure (PbtO₂) and near-infrared spectroscopy (NIRS) neuromonitoring are frequently compared in the management of acute moderate and severe traumatic brain injury patients; however, the relationship between their respective output parameters flows from the complex pathogenesis of tissue respiration after brain trauma. NIRS neuromonitoring overcomes certain limitations related to the heterogeneity of the pathology across the brain that cannot be adequately addressed by local-sample invasive neuromonitoring (e.g., PbtO₂ neuromonitoring, microdialysis), and it allows clinicians to assess parameters that cannot otherwise be scanned. The anatomical co-registration of an NIRS signal with axial imaging (e.g., computerized tomography scan) enhances the optical signal, which can be changed by the anatomy of the lesions and the significance of the radiological assessment. These arguments led us to conclude that rather than aiming to substitute PbtO₂ with tissue saturation, multiple types of NIRS should be included via multimodal systemic- and neuro-monitoring, whose values then are incorporated into biosignatures linked to patient status and prognosis. Discussion on the abnormalities in tissue respiration due to brain trauma and how they affect the PbtO₂ and NIRS neuromonitoring is given.

Design and validation of a mechanically flexible and ultra-lightweight high-density diffuse optical tomography system for functional neuroimaging of newborns

Significance: Neonates are a highly vulnerable population. The risk of brain injury is greater during the first days and weeks after birth than at any other time of life. Functional neuroimaging that can be performed longitudinally and at the cot-side has the potential to improve our understanding of the evolution of multiple forms of neurological injury over the perinatal period. However, existing technologies make it very difficult to perform repeated and/or long-duration functional neuroimaging experiments at the cot-side. Aim: We aimed to create a modular, high-density diffuse optical tomography (HD-DOT) technology specifically for neonatal applications that is ultra-lightweight, low profile and provides high mechanical flexibility. We then sought to validate this technology using an anatomically accurate dynamic phantom. Approach: An advanced 10-layer rigid-flexible printed circuit board technology was adopted as the basis for the DOT modules, which allows for a compact module design that also provides the flexibility needed to conform to the curved infant scalp. Two module layouts were implemented: dual-hexagon and triple-hexagon. Using in-built board-to-board connectors, the system can be configured to provide a vast range of possible layouts. Using epoxy resin, thermochromic dyes, and MRI-derived 3D-printed moulds, we constructed an electrically switchable, anatomically accurate dynamic phantom. This phantom was used to quantify the imaging performance of our flexible, modular HD-DOT system. Results: Using one particular module configuration designed to cover the infant sensorimotor system, the device provided 36 source and 48 detector positions, and over 700 viable DOT channels per wavelength, ranging from 10 to ∼ 45    mm over an area of approximately 60    cm 2 . The total weight of this system is only 70 g. The signal changes from the dynamic phantom, while slow, closely simulated real hemodynamic response functions. Using difference images obtained from the phantom, the measured 3D localization error provided by the system at the depth of the cortex was in the of range 3 to 6 mm, and the lateral image resolution at the depth of the neonatal cortex is estimated to be as good as 10 to 12 mm. Conclusions: The HD-DOT system described is ultra-low weight, low profile, can conform to the infant scalp, and provides excellent imaging performance. It is expected that this device will make functional neuroimaging of the neonatal brain at the cot-side significantly more practical and effective.

Mapping fluorescence resonance energy transfer parameters of a bifunctional agent using time-domain fluorescence diffuse optical tomography

We present a method to map fluorescence resonance energy transfer (FRET) parameters of a bifunctional photodynamic therapy agent, (2-[1-hexyloxyethyl]-2-devinyl pyropheophorbide-a)-cyanine dye (HPPH-CD) conjugate, which consists of a photosensitizer (HPPH) and a fluorescent agent CD. We utilized time-domain fluorescence diffuse optical tomography, the normalized Born ratio model in the Fourier-domain, and an iterative algorithm to map depth-resolved spatial heterogeneities of FRET parameters. Our results exhibited depth-resolved changes of fluorophore's lifetime and the distance maps due to FRET between HPPH and CD. Our model suggests a potential approach of using FRET parameters to monitor efficacies of multifunctional photodynamic therapy agents in deep tissue.

Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Regional analysis of cerebral hemodynamic changes during the head-up tilt test in Parkinson's disease patients with orthostatic intolerance

Significance: Cerebral oxygenation changes in the superior, middle, and medial gyri were used to elucidate spatial impairments of autonomic hemodynamic recovery during the head-up tilt table test (HUTT) in Parkinson's disease (PD) patients with orthostatic intolerance (OI) symptoms. Aim: To analyze dynamic oxygenation changes during the HUTT and classify PD patients with OI symptoms using clinical and oxygenation features. Approach: Thirty-nine PD patients with OI symptoms [10: orthostatic hypotension (PD-OH); 29: normal HUTT results (PD-NOR)] and seven healthy controls (HCs) were recruited. Prefrontal oxyhemoglobin (HbO) changes during the HUTT were reconstructed with diffuse optical tomography and segmented using the automated anatomical labeling system. Decision trees were used for classification. Results: HCs and PD-NOR patients with positive rates of HbO change (PD-POS) showed the greatest HbO recovery in the superior frontal gyrus (SFG) during tilt. PD-OH and PD-NOR patients with negative rates of HbO change (PD-NEG) showed asymmetric reoxygenation. The classification accuracy was 89.4% for PD-POS versus PD-NEG, 71% for PD-NOR versus PD-OH, and 55.8% for PD-POS versus PD-NEG versus PD-OH. The oxygenation features were more discriminative than the clinical features. Conclusions: PD-OH showed decreased right SFG function, which may be associated with impaired compensatory autonomic responses to orthostatic stress.

Tomographic Task-Related Functional Near-Infrared Spectroscopy in Acute Sport-Related Concussion: An Observational Case Study

Making decisions regarding return-to-play after sport-related concussion (SRC) based on resolution of symptoms alone can expose contact-sport athletes to further injury before their recovery is complete. Task-related functional near-infrared spectroscopy (fNIRS) could be used to scan for abnormalities in the brain activation patterns of SRC athletes and help clinicians to manage their return-to-play. This study aims to show a proof of concept of mapping brain activation, using tomographic task-related fNIRS, as part of the clinical assessment of acute SRC patients. A high-density frequency-domain optical device was used to scan 2 SRC patients, within 72 h from injury, during the execution of 3 neurocognitive tests used in clinical practice. The optical data were resolved into a tomographic reconstruction of the brain functional activation pattern, using diffuse optical tomography. Moreover, brain activity was inferred using single-subject statistical analyses. The advantages and limitations of the introduction of this optical technique into the clinical assessment of acute SRC patients are discussed.

Real-Time Dual-Wavelength Time-Resolved Diffuse Optical Tomography System for Functional Brain Imaging Based on Probe-Hosted Silicon Photomultipliers

Near-infrared diffuse optical tomography is a non-invasive photonics-based imaging technology suited to functional brain imaging applications. Recent developments have proved that it is possible to build a compact time-domain diffuse optical tomography system based on silicon photomultipliers (SiPM) detectors. The system presented in this paper was equipped with the same eight SiPM probe-hosted detectors, but was upgraded with six injection fibers to shine the sample at several points. Moreover, an automatic switch was included enabling a complete measurement to be performed in less than one second. Further, the system was provided with a dual-wavelength (670 nm and 820 nm) light source to quantify the oxy- and deoxy-hemoglobin concentration evolution in the tissue. This novel system was challenged against a solid phantom experiment, and two in-vivo tests, namely arm occlusion and motor cortex brain activation. The results show that the tomographic system makes it possible to follow the evolution of brain activation over time with a 1s-resolution.

Cerebral hemodynamic monitoring of Parkinson's disease patients with orthostatic intolerance during head-up tilt test

Significance: Monitoring of cerebral perfusion rather than blood pressure changes during a head-up tilt test (HUTT) is proposed to understand the pathophysiological effect of orthostatic intolerance (OI), including orthostatic hypotension (OH), in Parkinson's disease (PD) patients. Aim: We aim to characterize and distinguish the cerebral perfusion response to a HUTT for healthy controls (HCs) and PD patients with OI symptoms. Approach: Thirty-nine PD patients with OI symptoms [10 PD patients with OH (PD-OH) and 29 PD patients with normal HUTT results (PD-NOR)], along with seven HCs participated. A 108-channel diffuse optical tomography (DOT) system was used to reconstruct prefrontal oxyhemoglobin (HbO), deoxyhemoglobin (Hb), and total hemoglobin (HbT) changes during dynamic tilt (from supine to 70-deg tilt) and static tilt (remained tilted at 70 deg). Results: HCs showed rapid recovery of cerebral perfusion in the early stages of static tilt. PD-OH patients showed decreasing HbO and HbT during dynamic tilt, continuing into the static tilt period. The rate of HbO change from dynamic tilt to static tilt is the distinguishing feature between HCs and PD-OH patients. Accordingly, PD-NOR patients were subgrouped based on positive-rate and negative-rate of HbO change. PD patients with a negative rate of HbO change were more likely to report severe OI symptoms in the COMPASS questionnaire. Conclusions: Our findings showcase the usability of DOT for sensitive detection and quantification of autonomic dysfunction in PD patients with OI symptoms, even those with normal HUTT results.

Small-animal 360-deg fluorescence diffuse optical tomography using structural prior information from ultrasound imaging

We demonstrate dual modality of free-space fluorescence diffuse optical tomography (FDOT) and handheld ultrasound (US) imaging to reveal both functional and structural information in small animals. FDOT is a noninvasive method for examining the fluorophore inside an object from the light distribution of the surface. In FDOT, a 660-nm continuous wave diode laser was used as an excitation source and an electron-multiplying charge-coupled device (EMCCD) was used for fluorescence data acquisition. Both the laser and EMCCD were mounted on a 360-deg rotation gantry for the transmission optical data collection. The structural information is obtained from a 6- to 17-MHz handheld US linear transducer by single-side access and conducts in the reconstruction as soft priors. The rotation ranges from 0 deg to 360 deg; different rotation degrees, object positions, and parameters were determined for comparison. Both phantom and tissue phantom results demonstrate that fluorophore distribution can be recovered accurately and quantitatively using this imaging system. Finally, an animal study confirms that the system can extract a dual-modality image, validating its feasibility for further in vivo experiments. In all experiments, the error and standard deviation decrease as the rotation degree is increased and the error was reduced to 10% when the rotation degree was increased over 135 deg.

Transformational change in the field of diffuse optics: From going bananas to going nuts

The concept of region of sensitivity is central to the field of diffuse optics and is closely related to the Jacobian matrix used to solve the inverse problem in imaging. It is well-known that, in diffuse reflectance, the region of sensitivity associated with a given source-detector pair is shaped as a banana, and features maximal sensitivity to the portions of the sample that are closest to the source and the detector. We have recently introduced a dual-slope method based on a special arrangement of two sources and two detectors, which results in deeper and more localized regions of sensitivity, resembling the shapes of different kinds of nuts. Here, we report the regions of sensitivity associated with a variety of source-detector arrangements for dual-slope measurements of intensity and phase with frequency-domain spectroscopy (modulation frequency: 140 MHz) in a medium with absorption and reduced scattering coefficients of 0.1 cm^-1 and 12 cm^-1, respectively. The main result is that the depth of maximum sensitivity, considering only cases that use source-detector separations of 25 and 35 mm, progressively increases as we consider single-distance intensity (2.0 mm), dual-slope intensity (4.6 mm), single-distance phase (7.5 mm), and dual-slope phase (10.9 mm). These results indicate the importance of dual-slope measurements, and even more so of phase measurements, when it is desirable to selectively probe deeper portions of a sample with diffuse optics. This is certainly the case in non-invasive optical studies of brain, muscle, and breast tissue, which are located underneath superficial tissue at variable depths.

Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements

Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.

Assessment of cerebrovascular development and intraventricular hemorrhages in preterm infants with optical measures of the brain arterial pulse wave

Preterm infants (born at 24-34 weeks of gestational age) suffer from a high incidence of neurological complications. Cerebrovascular lesions (intraventricular hemorrhages, IVH, and ischemic injury) due to the immaturity of the vascular system and its inability to adapt to the extra-uterine environment are the major causes of adverse neurological outcomes. We investigated the feasibility of assessing cerebrovascular status in preterm infants using a novel non-invasive optical procedure, pulse-DOT, usable within the incubator. Pulse-DOT, validated in adults, provides estimates of cerebral arterial status based on optical measurements of the pulse wave. These measurements are taken with a high-density optode montage and provide accurate spatial and temporal information. We found that two pulse parameters (pulse relaxation function, PReFx, and pulse rise time, PRT) in the investigated frontotemporal region, correlated with infant's age at recording, indexing cerebrovascular development. Moreover, PRT differentiated infants with and without concurrent IVH (sensitivity = 100%, specificity = 70%). These values are at least as high as those of the resistivity index obtained with transcranial Doppler of the middle cerebral artery, the current clinical method of choice for investigating arterial elasticity in preterm infants. This makes pulse-DOT a promising tool for investigating cerebrovascular risk factors and related pathologies in preterm infants.

An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis

The ultrasound-guided diffuse optical tomography is a noninvasive imaging technique for breast cancer diagnosis and treatment monitoring. The technique uses a handheld probe capable of providing measurements of multiple wavelengths in a few seconds. These measurements are used to estimate optical absorptions of lesions and calculate the total hemoglobin concentration. Any measurement errors caused by low signal to noise ratio data and/or movements during data acquisition would reduce the accuracy of reconstructed total hemoglobin concentration. In this article, we introduce an automated preprocessing method that combines data collected from multiple sets of lesion measurements of 4 optical wavelengths to detect and correct outliers in the perturbation. Two new measures of correlation between each pair of wavelength measurements and a wavelength consistency index of all reconstructed absorption maps are introduced. For phantom and patients' data without evidence of measurement errors, the correlation coefficient between each pair of wavelength measurements was above 0.6. However, for patients with measurement errors, the correlation coefficient was much lower. After applying the correction method to 18 patients' data with measurement errors, the correlation has improved and the wavelength consistency index is in the same range as the cases without wavelength-dependent measurement errors. The results show an improvement in classification of malignant and benign lesions.

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging

Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.

High-performance, robustly verified Monte Carlo simulation with FullMonte

We introduce the FullMonte tetrahedral 3-D Monte Carlo (MC) software package for simulation, visualization, and analysis of light propagation in heterogeneous turbid media including tissue. It provides the highest computational performance and richest set of input, output, and analysis facilities of any open-source tetrahedral-mesh MC light simulator. It also provides a robust framework for statistical verification. A scripting interface makes set-up of simulation runs simple, including parameter sweeps, while simultaneously providing customization options. Data formats shared with class-leading visualization tools, VTK and Paraview, facilitate interactive generation of publication-quality fluence and irradiance maps. The simulator can read and write file formats supported by other similar simulators, such as TIM-OS, MMC, COMSOL (finite-element simulations), and MCML to support comparison. Where simulator features permit, FullMonte can take a single test case, run it in multiple software packages, and load the results together for comparison. Example meshes, optical properties, set-up scripts, and output files are provided for user convenience. We demonstrate its use in several test cases, including photodynamic therapy of the brain, bioluminescence imaging (BLI) in a mouse phantom, and a comparison against MCML for layered geometries. Application domains that can benefit from use of FullMonte include photodynamic, photothermal, and photobiomodulation therapies, BLI, diffuse optical tomography, MC software development, and biophotonics education. Since MC results may be used for preclinical or even clinical experiments, a robust and rigorous verification process is essential. Being a stochastic numerical method, MC simulation has unique challenges associated with verification of output results since observed differences may be due simply to output variance or actual differences in expected output. We describe and have implemented a rigorous and statistically justified framework for comparing between simulators of the same class and for performing regression testing.