Optimization of optode arrangements for diffuse optical tomography: A singular-value analysis

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Cathepsin K-Activated Probe for Fluoro-Photoacoustic Imaging of Early Osteolytic Metastasis

Precise detection of early osteolytic metastases is crucial for their treatment but remains challenging in the clinic because of the limited sensitivity and specificity of traditional imaging techniques. Although fluorescence imaging offers attractive features for the diagnosis of osteolytic metastases, it is hampered by limited penetration depth. To address this issue, a fluoro-photoacoustic dual-modality imaging probe comprising a near-infrared dye caged by a cathepsin K (CTSK)-cleavable peptide sequence on one side and functionalized with osteophilic alendronate through a polyethylene glycol linker on the other side is reported. Through systematic in vitro and in vivo experiments, it is demonstrated that in response to CTSK, the probe generated both near-infrared fluorescent and photoacoustic signals from bone metastatic regions, thus offering a potential strategy for detecting deep-seated early osteolytic metastases.

Transformer Based Cross-Subject Mental Workload Classification Using FNIRS for Real-World Application

Mental state monitoring is a hot topic especially in neurorehabilitation, skill training, etc, for which the functional near-infrared spectroscopy (fNIRS) has been suggested to be used, and fewer detection channels and cross-subject performance are usually required for real-world application. To this goal, we propose a transformer-based method for cross-subject mental workload classification using fewer channels of fNIRS. Firstly, the input fNIRS signals in a window are divided into patches in the temporal order and transformed into embeddings, to which a classification token and learnable position embeddings are added. Then, a transformer encoder is used to learn the long-range dependencies among the embeddings, of which the output classification token is sent to a multilayer perceptron (MLP) head. Mental workload classification results can be represented by the outputs of the MLP head. Finally, comparison experiments were conducted on the open-access fNIRS2MW dataset. The results show that, the proposed method can outperform previous methods in cross-subject classification accuracy, and relatively efficient computation can be obtained.

Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection

The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box-Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R² =  > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively.

Numerical Optimisation of a NIRS Device for Monitoring Tissue Oxygen Saturation

The present work aims to develop a wearable, textile-integrated NIRS-based tissue oxygen saturation (StO2) monitor for alerting mobility-restricted individuals - such as paraplegics - of critical tissue oxygen de-saturation in the regions such as the sacrum and the ischial tuberosity; these regions are proven to be extremely susceptible to the development of pressure injuries (PI).Using a combination of numerical methods including finite element analysis, image reconstruction, stochastic gradient descent with momentum (SGDm) and genetic algorithms, a methodology was developed to define the optimal combination of wavelengths and source-detector geometry needed for measuring the StO2 in tissue up to depths of 3 cm. The sensor design was optimised to account for physiologically relevant adipose tissue thicknesses (ATT) between 1 mm and 5 mm. The approach assumes only a priori knowledge of the optical properties of each of the three tissue layers used in the model (skin, fat, muscle) based on the absorption and scattering coefficients of four chromophores (O2Hb, HHb, H2O and lipid).The results show that the selected wavelengths as well as the source-detector geometries and number of sources and detectors depend on ATT and the degree and volume of the hypoxic regions. As a result of a genetic algorithm used to combine the various optimised designs into a single sensor layout, a group of four wavelengths was chosen, coinciding with the four chromophores and agreeing very well with literature. The optimised number of source points and detector points and their geometry resulted in good reconstruction of the StO2 across a wide range of layer geometries.

Optimal Scanning Protocol for Optical Tomography

Tomography is a two step process in which the sample under test is first scanned by the hardware of the system to acquire data and then the operating software reconstruct images from the gathered information. The main objective of this work is to optimize the scanning process to acquire maximum amount of information in each measurement when the system is scanning the sample. By exploiting our prior information about the sample and using estimation theory, we developed a systematic approach to implement the optimal scanning protocol. Results of this study provide strong evidence that the developed algorithms can speed up data acquisition. Also it is shown that the proposed method can reduce the impact of noise as well as improving the reconstruction error while performing less number of measurements.Clinical relevance- The proposed method can enhance data acquisition time, exposure dosage and cost of operation in medical applications of tomography.

Optimization of data acquisition operation in optical tomography based on estimation theory

The data acquisition process is occasionally the most time consuming and costly operation in tomography. Currently, raster scanning is still the common practice in making sequential measurements in most tomography scanners. Raster scanning is known to be slow and such scanners usually cannot catch up with the speed of changes when imaging dynamically evolving objects. In this research, we studied the possibility of using estimation theory and our prior knowledge about the sample under test to reduce the number of measurements required to achieve a given image quality. This systematic approach for optimization of the data acquisition process also provides a vision toward improving the geometry of the scanner and reducing the effect of noise, including the common state-dependent noise of detectors. The theory is developed in the article and simulations are provided to better display discussed concepts.

Noninvasive multimodal fluorescence and magnetic resonance imaging of whole-organ intervertebral discs

Low back pain (LBP) is a commonly experienced symptom posing a tremendous healthcare burden to individuals and society at large. The LBP pathology is strongly linked to degeneration of the intervertebral disc (IVD), calling for development of early-stage diagnostic tools for visualizing biomolecular changes in IVD. Multimodal measurements of fluorescence molecular tomography (FMT) and magnetic resonance imaging (MRI) were performed on IVD whole organ culture model using an in-house built FMT system and a high-field MRI scanner. The resulted multimodal images were systematically validated through epifluorescence imaging of the IVD sections at a microscopic level. Multiple image contrasts were exploited, including fluorescence distribution, anatomical map associated with T1-weighted MRI contrast, and water content related with T2 relaxation time. The developed multimodality imaging approach may thus serve as a new assessment tool for early diagnosis of IVD degeneration and longitudinal monitoring of IVD organ culture status using fluorescence markers.

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength

Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging imaging modality that shows great potential for preclinical research and clinical practice. As a hybrid technique, PAI uniquely combines the advantages of optical excitation and of acoustic detection. Optical excitation provides a rich contrast mechanism from either endogenous or exogenous chromophores, allowing PAI to perform biochemical, functional, and molecular imaging. Acoustic detection benefits from the low scattering of ultrasound in biological tissue, enabling PAI to generate high-resolution images in both the optical ballistic and diffusive regimes. Accordingly, this hybrid imaging modality features high sensitivity to optical absorption and wide scalability of spatial resolution with the desired imaging depth. Over the past two decades, the photoacoustic technique has led to a variety of exciting discoveries and applications from laboratory research to clinical patient care. In biological research, PAI has become an irreplaceable tool, providing functional optical contrast with high spatiotemporal resolution. Translational PAI also attracted growing interest in clinical applications including tumor margin examination, internal organ imaging, breast cancer screening, and sentinel lymph node mapping, among others.

In vivo voltage-sensitive dye imaging of mouse cortical activity with mesoscopic optical tomography

Significance: Cellular layering is a hallmark of the mammalian neocortex with layer and cell type-specific connections within the cortical mantle and subcortical connections. A key challenge in studying circuit function within the neocortex is to understand the spatial and temporal patterns of information flow between different columns and layers. Aim: We aimed to investigate the three-dimensional (3D) layer- and area-specific interactions in mouse cortex in vivo. Approach: We applied a new promising neuroimaging method-fluorescence laminar optical tomography in combination with voltage-sensitive dye imaging (VSDi). VSDi is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but it is traditionally used for two-dimensional (2D) imaging. Our mesoscopic technique allows visualization of neuronal activity in a 3D manner with high temporal resolution. Results: We first demonstrated the depth-resolved capability of 3D mesoscopic imaging technology in Thy1-ChR2-YFP transgenic mice. Next, we recorded the long-range functional projections between sensory cortex (S1) and motor cortex (M1) in mice, in vivo, following single whisker deflection. Conclusions: The results show that mesoscopic imaging technique has the potential to investigate the layer-specific neural connectivity in the mouse cortex in vivo. Combination of mesoscopic imaging technique with optogenetic control strategy is a promising platform for determining depth-resolved interactions between cortical circuit elements.

Multiple information extracted from photoacoustic radio-frequency signal and the application on tissue classification

Photoacoustic imaging is a hybrid biomedical imaging technique, combining rich optical contrasts and good acoustic resolution in deep tissues. As a noninvasive and nonionized imaging method, photoacoustic imaging has shown great potentials in biomedicine in the past decade. In this review, we give a brief introduction of the physical principle and three major implementations of photoacoustic imaging. Then, we present pictures of some recent progress about the extraction of new imaging parameters from photoacoustic radio-frequency signals. These parameters are highly associated with the tissue microstructure characteristics, including characteristic size, number density, and elasticity. This information could give us insight into various properties of tissue in-depth and be applied to tissue classification for basic research and clinical settings.

Performance assessment of high-density diffuse optical topography regarding source-detector array topology

Recent advances in optical neuroimaging systems as a functional interface enhance our understanding of neuronal activity in the brain. High density diffuse optical topography (HD-DOT) uses multi-distance overlapped channels to improve the spatial resolution of images comparable to functional magnetic resonance imaging (fMRI). The topology of the source and detector (SD) array directly impacts the quality of the hemodynamic reconstruction in HD-DOT imaging modality. In this work, the effect of different SD configurations on the quality of cerebral hemodynamic recovery is investigated by presenting a simulation setup based on the analytical approach. Given that the SD arrangement determines the elements of the Jacobian matrix, we conclude that the more individual components in this matrix, the better the retrieval quality. The results demonstrate that the multi-distance multi-directional (MDMD) arrangement produces more unique elements in the Jacobian array. Consequently, the inverse problem can accurately retrieve the brain activity of diffuse optical topography data.

System configuration optimization for mesoscopic fluorescence molecular tomography

Tissue engineering applications demand 3D, non-invasive, and longitudinal assessment of bioprinted constructs. Current emphasis is on developing tissue constructs mimicking in vivo conditions; however, these are increasingly challenging to image as they are typically a few millimeters thick and turbid, limiting the usefulness of classical fluorescence microscopic techniques. For such applications, we developed a Mesoscopic Fluorescence Molecular Tomography methodology that collects high information content data to enable high-resolution tomographic reconstruction of fluorescence biomarkers at millimeters depths. This imaging approach is based on an inverse problem; hence, its imaging performances are dependent on critical technical considerations including optode sampling, forward model design and inverse solver parameters. Herein, we investigate the impact of the optical system configuration parameters, including detector layout, number of detectors, combination of detector and source numbers, and scanning mode with uncoupled or coupled source and detector array, on the 3D imaging performances. Our results establish that an MFMT system with a 2D detection chain implemented in a de-scanned mode provides the optimal imaging reconstruction performances.

High-density functional diffuse optical tomography based on frequency-domain measurements improves image quality and spatial resolution

Measurements of dynamic near-infrared (NIR) light attenuation across the human head together with model-based image reconstruction algorithms allow the recovery of three-dimensional spatial brain activation maps. Previous studies using high-density diffuse optical tomography (HD-DOT) systems have reported improved image quality over sparse arrays. These HD-DOT systems incorporated multidistance overlapping continuous wave measurements that only recover differential intensity attenuation. We investigate the potential improvement in reconstructed image quality due to the additional incorporation of phase shift measurements, which reflect the time-of-flight of the measured NIR light, within the tomographic reconstruction from high-density measurements. To evaluate image reconstruction with and without the additional phase information, we simulated point spread functions across a whole-scalp field of view in 24 subject-specific anatomical models using an experimentally derived noise model. The addition of phase information improves the image quality by reducing localization error by up to 59% and effective resolution by up to 21% as compared to using the intensity attenuation measurements alone. Furthermore, we demonstrate that the phase data enable images to be resolved at deeper brain regions where intensity data fail, which is further supported by utilizing experimental data from a single subject measurement during a retinotopic experiment.

High-density diffuse optical tomography for imaging human brain function

This review describes the unique opportunities and challenges for noninvasive optical mapping of human brain function. Diffuse optical methods offer safe, portable, and radiation free alternatives to traditional technologies like positron emission tomography or functional magnetic resonance imaging (fMRI). Recent developments in high-density diffuse optical tomography (HD-DOT) have demonstrated capabilities for mapping human cortical brain function over an extended field of view with image quality approaching that of fMRI. In this review, we cover fundamental principles of the diffusion of near infrared light in biological tissue. We discuss the challenges involved in the HD-DOT system design and implementation that must be overcome to acquire the signal-to-noise necessary to measure and locate brain function at the depth of the cortex. We discuss strategies for validation of the sensitivity, specificity, and reliability of HD-DOT acquired maps of cortical brain function. We then provide a brief overview of some clinical applications of HD-DOT. Though diffuse optical measurements of neurophysiology have existed for several decades, tremendous opportunity remains to advance optical imaging of brain function to address a crucial niche in basic and clinical neuroscience: that of bedside and minimally constrained high fidelity imaging of brain function.

Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation

OBJECTIVE

Fluorescence molecular tomography (FMT) can provide valuable molecular information by mapping the bio-distribution of fluorescent reporter molecules in the intact organism. Various prototype FMT systems have been introduced during the past decade. However, none of them has evolved as a standard tool for routine biomedical research. The goal of this paper is to develop a software package that can automate the complete FMT reconstruction procedure.

METHODS

We present smart toolkit for fluorescence tomography (STIFT), a comprehensive platform comprising three major protocols: 1) virtual FMT, i.e., forward modeling and reconstruction of simulated data; 2) control of actual FMT data acquisition; and 3) reconstruction of experimental FMT data.

RESULTS

Both simulation and phantom experiments have shown robust reconstruction results for homogeneous and heterogeneous tissue-mimicking phantoms containing fluorescent inclusions.

CONCLUSION

STIFT can be used for optimization of FMT experiments, in particular for optimizing illumination patterns.

SIGNIFICANCE

This paper facilitates FMT experiments by bridging the gaps between simulation, actual experiments, and data reconstruction.

Sensitivity Uniformity Ratio as a New Index to Optimize the Scanning Geometry for Fluorescent Molecular Tomography

Background:

Molecular fluorescence imaging is widely used as a noninvasive method to study the cellular and molecular mechanisms. In the optical imaging system, the sensitivity is the change of the intensity received by the detector for the changed optical characteristics (fluorescence) in each sample point. Sensitivity could be considered as a function of imaging geometry. A favor imaging system has a uniform and high-sensitivity coefficient for each point of the sample. In this study, a new parameter was proposed which the optimal angle between the source and detector could be determined based on this parameter.

Methods:

For evaluation of the new method, a two-dimensional mesh with a radius of 20 mm and 5133 nodes was built. In each reconstruction, 0.5-mm fluorescence heterogeneity with a contrast-to-purpose ratio of fluorescence yield of 10 was randomly added at different points of the sample. The source and the detector were simulated in different geometric conditions. The calculations were performed using the NIRFAST and MATLAB software. The relationship between mean squared error (MSE) and sensitivity uniformity ratio (SUR) was evaluated using the correlation coefficient. Finally, based on the new index, an optimal geometrical strategy was introduced.

Results:

There was a negative correlation coefficient (R = −0.78) with the inverse relationship between the SUR and MSE indices. The reconstructed images showed that the better image quality achieved using the optimal geometry for all scanning depths. For the conventional geometry, there is an artifact in the opposite side of the inhomogeneity at the shallow depths, which has been eliminated in the reconstructed images achieved using the optimal geometry.

Conclusion:

The SUR is a powerful computational tool which could be used to determine the optimal angles between the source and detector for each scanning depth.

Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution

Imaging through scattering media is still a formidable challenge with widespread applications ranging from biomedical imaging to remote sensing. Recent research progresses provide several feasible solutions, which are hampered by limited complexity of targets, invasiveness of data collection process and lack of robustness for reconstruction. In this paper, we show that the complex to-be-observed targets can be non-invasively reconstructed with fine details. Training targets, which can be directly reconstructed by speckle correlation and phase retrieval, are utilized as the input of the proposed speckle pattern estimation model, in which speckle modeling and constrained least square optimization are applied to estimate the distribution of the speckle pattern. Reconstructions for to-be-observed targets are realized by deconvoluting the estimated speckle pattern from the acquired integrated intensity matrices (IIMs). The qualities of reconstructed results are ensured by the stable statistical property and memory effect of laser speckle patterns. Experimental results show that the proposed method can reconstruct complex targets in high quality and the reconstruction performance is robust even much less data are acquired.

Optical property uncertainty estimates for spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging modality that has attracted considerable interest in recent years. Typically, diffuse reflectance measurements of spatially modulated light are used to quantify the optical absorption and reduced scattering coefficients of tissue, and with these, chromophore concentrations are extracted. However, uncertainties in estimated absorption and reduced scattering coefficients are rarely reported, and we know of no method capable of providing these when look-up table (LUT) algorithms are used to recover the optical properties. We present a method to generate optical property uncertainty estimates from knowledge of diffuse reflectance measurement errors. By employing the Cramér-Rao bound, we can quickly and efficiently explore theoretical SFDI performance as a function of spatial frequencies and sample optical properties, allowing us to optimize spatial frequency selection for a given application. In practice, we can also obtain useful uncertainty estimates for optical properties recovered with a two-frequency LUT algorithm, as we demonstrate with tissue-simulating phantom and in vivo experiments. Finally, we illustrate how absorption coefficient uncertainties can be propagated forward to yield uncertainties for chromophore concentrations, which could significantly impact the interpretation of experimental results.

Imaging stem cell distribution, growth, migration, and differentiation in 3-D scaffolds for bone tissue engineering using mesoscopic fluorescence tomography

Regenerative medicine has emerged as an important discipline that aims to repair injury or replace damaged tissues or organs by introducing living cells or functioning tissues. Successful regenerative medicine strategies will likely depend upon a simultaneous optimization strategy for the design of biomaterials, cell-seeding methods, cell-biomaterial interactions, and molecular signaling within the engineered tissues. It remains a challenge to image three-dimensional (3-D) structures and functions of the cell-seeded scaffold in mesoscopic scale (>2 ∼ 3 mm). In this study, we utilized angled fluorescence laminar optical tomography (aFLOT), which allows depth-resolved molecular characterization of engineered tissues in 3-D to investigate cell viability, migration, and bone mineralization within bone tissue engineering scaffolds in situ.

Imperial College near infrared spectroscopy neuroimaging analysis framework

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.

Null functions in three-dimensional imaging of alpha and beta particles

Null functions of an imaging system are functions in the object space that give exactly zero data. Hence, they represent the intrinsic limitations of the imaging system. Null functions exist in all digital imaging systems, because these systems map continuous objects to discrete data. However, the emergence of detectors that measure continuous data, e.g. particle-processing (PP) detectors, has the potential to eliminate null functions. PP detectors process signals produced by each particle and estimate particle attributes, which include two position coordinates and three components of momentum, as continuous variables. We consider Charged-Particle Emission Tomography (CPET), which relies on data collected by a PP detector to reconstruct the 3D distribution of a radioisotope that emits alpha or beta particles, and show empirically that the null functions are significantly reduced for alpha particles if ≥3 attributes are measured or for beta particles with five attributes measured.

High-dynamic-range fluorescence laminar optical tomography (HDR-FLOT)

Three-dimensional fluorescence laminar optical tomography (FLOT) can achieve resolutions of 100-200 µm and penetration depths of 2-3 mm. FLOT has been used in tissue engineering, neuroscience, as well as oncology. The limited dynamic range of the charge-coupled device-based system makes it difficult to image fluorescent samples with a large concentration difference, limits its penetration depth, and diminishes the quantitative accuracy of 3D reconstruction data. Here, incorporating the high-dynamic-range (HDR) method widely used in digital cameras, we present HDR-FLOT, increasing penetration depth and improving the ability to image fluorescent samples with a large concentration difference. The method was tested using an agar phantom and a B6 mouse for brain imaging in vivo.

Structured illumination diffuse optical tomography for noninvasive functional neuroimaging in mice

Optical intrinsic signal (OIS) imaging has been a powerful tool for capturing functional brain hemodynamics in rodents. Recent wide field-of-view implementations of OIS have provided efficient maps of functional connectivity from spontaneous brain activity in mice. However, OIS requires scalp retraction and is limited to superficial cortical tissues. Diffuse optical tomography (DOT) techniques provide noninvasive imaging, but previous DOT systems for rodent neuroimaging have been limited either by sparse spatial sampling or by slow speed. Here, we develop a DOT system with asymmetric source-detector sampling that combines the high-density spatial sampling (0.4 mm) detection of a scientific complementary metal-oxide-semiconductor camera with the rapid (2 Hz) imaging of a few ([Formula: see text]) structured illumination (SI) patterns. Analysis techniques are developed to take advantage of the system's flexibility and optimize trade-offs among spatial sampling, imaging speed, and signal-to-noise ratio. An effective source-detector separation for the SI patterns was developed and compared with light intensity for a quantitative assessment of data quality. The light fall-off versus effective distance was also used for in situ empirical optimization of our light model. We demonstrated the feasibility of this technique by noninvasively mapping the functional response in the somatosensory cortex of the mouse following electrical stimulation of the forepaw.

Depth-resolved imaging of colon tumor using optical coherence tomography and fluorescence laminar optical tomography

Early detection of neoplastic changes remains a critical challenge in clinical cancer diagnosis and treatment. Many cancers arise from epithelial layers such as those of the gastrointestinal (GI) tract. Current standard endoscopic technology is difficult to detect the subsurface lesions. In this research, we investigated the feasibility of a novel multi-modal optical imaging approach including high-resolution optical coherence tomography (OCT) and high-sensitivity fluorescence laminar optical tomography (FLOT) for structural and molecular imaging. The C57BL/6J-Apc^Min/J mice were imaged using OCT and FLOT, and the correlated histopathological diagnosis was obtained. Quantitative structural (scattering coefficient) and molecular (relative enzyme activity) parameters were obtained from OCT and FLOT images for multi-parametric analysis. This multi-modal imaging method has demonstrated the feasibility for more accurate diagnosis with 88.23% (82.35%) for sensitivity (specificity) compared to either modality alone. This study suggested that combining OCT and FLOT is promising for subsurface cancer detection, diagnosis, and characterization.

Sampling scheme optimization for diffuse optical tomography based on data and image space rankings

We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source–detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.

Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy

The paper presents a functional near-infrared spectroscopy (fNIRS)-based bundled-optode method for detection of the changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. fNIRS with 32 optodes is utilized to measure five healthy male subjects' brain-hemodynamic responses to arithmetic tasks. Specifically, the coordinates of 256 voxels in the three-dimensional (3D) volume are computed according to the known probe geometry. The mean path length factor in the Beer-Lambert equation is estimated as a function of the emitter-detector distance, which is utilized for computation of the absorption coefficient. The mean values of HbO and HbR obtained from the absorption coefficient are then applied for construction of a 3D fNIRS image. Our results show that the proposed method, as compared with the conventional approach, can detect brain activity with higher spatial resolution. This method can be extended for 3D fNIRS imaging in real-time applications.

In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation

Functional mapping of brain activity is important in elucidating how neural networks operate in the living brain. The whisker sensory system of rodents is an excellent model to study peripherally evoked neural activity in the central nervous system. Each facial whisker is represented by discrete modules of neurons all along the pathway leading to the neocortex. These modules are called "barrels" in layer 4 of the primary somatosensory cortex. Their location (approximately 300-500 μm below cortical surface) allows for convenient imaging of whisker-evoked neural activity in vivo. Fluorescence laminar optical tomography (FLOT) provides depth-resolved fluorescence molecular information with an imaging depth of a few millimeters. Angled illumination and detection configurations can improve both resolution and penetration depth. We applied angled FLOT (aFLOT) to record 3D neural activities evoked in the whisker system of mice by deflection of a single whisker in vivo. A 100 μm capillary and a pair of microelectrodes were inserted to the mouse brain to test the capability of the imaging system. The results show that it is possible to obtain 3D functional maps of the sensory periphery in the brain. This approach can be broadly applicable to functional imaging of other brain structures.

Emerging concepts in functional and molecular photoacoustic imaging

Providing the specific imaging contrast of optical absorption and excellent spatial scalability across the optical and ultrasonic dimensions, photoacoustic imaging has been rapidly emerging and expanding in the past two decades. In this review, I focus on a few latest advances in this enabling technology that hold the potential to transform in vivo functional and molecular imaging at multiple length scales. Specifically, multi-parametric photoacoustic microscopy enables simultaneous high-resolution mapping of hemoglobin concentration, oxygen saturation and blood flow-opening up the possibility of quantifying the metabolic rate of oxygen at the microscopic level. The pump-probe approach harnesses a variety of photoinduced transient optical absorption as novel contrast mechanisms for high-specificity molecular imaging at depth and as nonlinear excitation strategies for high-resolution volumetric microscopy beyond the conventional limit. Novel magneto-optical and photochromic probes lead to contrast-enhanced molecular photoacoustic imaging through differential detection.

Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography

Conventional reconstruction of diffuse optical tomography (DOT) is based on the Tikhonov regularization and the white Gaussian noise assumption. Consequently, the reconstructed DOT images usually have a low spatial resolution. In this work, we have derived a novel quantification method for noise variance based on the linear Rytov approximation of the photon diffusion equation. Specifically, we have implemented this quantification of noise variance to normalize the measurement signals from all source-detector channels along with sparsity regularization to provide high-quality DOT images. Multiple experiments from computer simulations and laboratory phantoms were performed to validate and support the newly developed algorithm. The reconstructed images demonstrate that quantification and normalization of noise variance with sparsity regularization (QNNVSR) is an effective reconstruction approach to greatly enhance the spatial resolution and the shape fidelity for DOT images. Since noise variance can be estimated by our derived expression with relatively limited resources available, this approach is practically useful for many DOT applications.

Fluorescence molecular tomography in the second near-infrared window

Fluorescence molecular tomography (FMT), an in vivo noninvasive imaging technology, can provide localization and quantification information for deep fluorophores. Light at wavelengths in the near-infrared (NIR-I) window from 650 nm to 950 nm has conventionally been chosen for FMT. In this study, we introduced longer NIR wavelengths within the 1100 nm to 1400 nm range, known as the "second NIR spectral window" (NIR-II). A singular-value analysis method was used to demonstrate the utility and advantages of using the NIR-II for FMT, and experiments showed an improvement in the spatial resolution in phantom studies.

Detection of cervical cancer based on photoacoustic imaging-the in-vitro results

In current clinical practice, the diagnosis of cervical cancer (CC) is mainly through the cervical screening followed by a necessary biopsy, but this method is labor consuming and expensive, and can only detect superficial lesions around the external cervical orifice. In contrast, photoacoustic imaging (PAI) is sensitive to the abnormal angiogenesis deep in the biological tissue, and may be capable for the intact scanning both around the external orifice and in cervical canal. In this paper, we for the first time put forward the photoacoustic diagnosis of CC. A total of 30 in-vitro experiments were carried out in this study, and the obtained depth maximum amplitude projection (DMAP) images were analyzed to evaluate the extent of the angiogenesis for different clinical stages of CC. Stronger absorption from the cervical lesions is observed relative to that of normal tissue. Paired t-test indicates that the difference in mean optical absorption (MOA) between normal tissue and cervical lesion has statistical significance with a confidential coefficient of 0.05. Statistical results also show that the MOAs of the cervical lesions are closely related to the severity of CC. These results imply that PAI may have great utility in the clinical diagnosis of CC.

In vivo pharmacokinetic features and biodistribution of star and rod shaped gold nanoparticles by multispectral optoacoustic tomography

Multispectral optoacoustic tomography (MSOT) provides a real-time monitoring method to evaluate gold nanoparticles' pharmacokinetics and biodistribution.

Comprehensive studies of pharmacokinetics and biodistribution of indocyanine green and liposomal indocyanine green by multispectral optoacoustic tomography

A real-time and continuous study of the pharmacokinetics and biodistribution of ICG and Lipo-ICG are performed by multispectral optoacoustic tomography.

Photoacoustic microscopy and computed tomography: from bench to bedside

Photoacoustic imaging (PAI) of biological tissue has seen immense growth in the past decade, providing unprecedented spatial resolution and functional information at depths in the optical diffusive regime. PAI uniquely combines the advantages of optical excitation and those of acoustic detection. The hybrid imaging modality features high sensitivity to optical absorption and wide scalability of spatial resolution with the desired imaging depth. Here we first summarize the fundamental principles underpinning the technology, then highlight its practical implementation, and finally discuss recent advances toward clinical translation.

Incoherence-based optimal selection of independent measurements in diffuse optical tomography

An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The proposed method was compared with the recently established data-resolution matrix-based approach for optimal choice of independent measurements and shown, using simulated and experimental gelatin phantom data sets, to be superior as it does not require an optimal regularization parameter for providing the same information.

Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges

Functional near-infrared spectroscopy (fNIRS), acquired simultaneously with electroencephalography (EEG), allows the investigation of hemodynamic brain responses to epileptic activity. Because the presumed epileptogenic focus is patient-specific, an appropriate source/detector (SD) montage has to be reconfigured for each patient. The combination of EEG and fNIRS, however, entails several constraints on montages, and finding an optimal arrangement of optodes on the cap is an important issue. We present a method for computing an optimal SD montage on an EEG/fNIRS cap that focuses on one or several specific brain regions; the montage maximizes the spatial sensitivity. We formulate this optimization problem as a linear integer programming problem. The method was evaluated on two EEG/fNIRS caps. We simulated absorbers at different locations on a head model and generated realistic optical density maps on the scalp. We found that the maps of optimal SD montages had spatial resolution properties comparable to those of regular SD arrangements for the whole head with significantly fewer sensors than regular SD arrangements. In addition, we observed that optimal montages yielded improved spatial density of fNIRS measurements over the targeted regions together with an increase in signal-to-noise ratio.

On the use of the Cramér-Rao lower bound for diffuse optical imaging system design

We evaluated the potential of the Cramér-Rao lower bound (CRLB) to serve as a design metric for diffuse optical imaging systems. The CRLB defines the best achievable precision of any estimator for a given data model; it is often used in the statistical signal processing community for feasibility studies and system design. Computing the CRLB requires inverting the Fisher information matrix (FIM), however, which is usually ill-conditioned (and often underdetermined) in the case of diffuse optical tomography (DOT). We regularized the FIM by assuming that the inhomogeneity to be imaged was a point target and assessed the ability of point-target CRLBs to predict system performance in a typical DOT setting in silico. Our reconstructions, obtained with a common iterative algebraic technique, revealed that these bounds are not good predictors of imaging performance across different system configurations, even in a relative sense. This study demonstrates that agreement between the trends predicted by the CRLBs and imaging performance obtained with reconstruction algorithms that rely on a different regularization approach cannot be assumed a priori. Moreover, it underscores the importance of taking into account the intended regularization method when attempting to optimize source-detector configurations.

Methodology to optimize detector geometry in fluorescence tomography of tissue using the minimized curvature of the summed diffuse sensitivity projections

The dependence of the sensitivity function in fluorescence tomography on the geometry of the excitation source and detection locations can severely influence an imaging system's ability to recover fluorescent distributions. Here a methodology for choosing imaging configuration based on the uniformity of the sensitivity function is presented. The uniformity of detection sensitivity is correlated with reconstruction accuracy in silico, and reconstructions in a murine head model show that a detector configuration optimized using Nelder-Mead minimization improves recovery over uniformly sampled tomography.

Virtual source method for diffuse optical imaging

The Green's function for diffusive wave propagation can be obtained by utilizing the representation theorems of the convolution type and the correlation type. In this work, the Green's function is retrieved by making use of the Robin boundary condition and the representation theorems for diffusive media. The diffusive Green's function between two detectors for photon flux is calculated by combining detector readings due to point light sources and utilizing virtual light sources at the detector positions in optical tomography. Two dimensional simulations for a circular region with eight sources and eight detectors located on the boundary are performed using a finite element method to demonstrate the feasibility of virtual sources. The most important potential application would be the replacement of noisy measurements with synthetic measurements that are provided by the virtual sources. This becomes an important issue in small animal and human studies. In addition, the same method may also be used to reduce the imaging time.

Multimodal fluorescence-mediated tomography and SPECT/CT for small-animal imaging

Spatial and temporal coregistration of nuclear and optical images can enable the fusion of the information from these complementary molecular imaging modalities. A critical challenge is in integrating the optical and nuclear imaging hardware. Flexible fiber-based fluorescence-mediated tomography (FMT) systems provide a viable solution. The various bore sizes of small-animal nuclear imaging systems can potentially accommodate the FMT fiber imaging arrays. In addition, FMT imaging facilitates coregistration of the nuclear and optical contrasts in time. Herein, we combine a fiber-based FMT system with a preclinical SPECT/CT platform. Feasibility of in vivo imaging is demonstrated by tracking a monomolecular multimodal imaging agent (MOMIA) during transport from the forepaw to the axillary lymph node region of a rat.

METHODS

The fiber-based, video-rate FMT imaging system is composed of 12 sources (785- and 830-nm laser diodes) and 13 detectors. To maintain high temporal sampling, the system simultaneously acquires ratio-metric data at each detector. A 3-dimensional finite element model derived from CT projections provides anatomically based light propagation modeling. Injection of a MOMIA intradermally into the forepaw of rats provided spatially and temporally coregistered nuclear and optical contrasts. FMT data were acquired concurrently with SPECT and CT data. The incorporation of SPECT data as a priori information in the reconstruction of FMT data integrated both optical and nuclear contrasts.

RESULTS

Accurate depth localization of phantoms with different thicknesses was accomplished with an average center-of-mass error of 4.1 ± 2.1 mm between FMT and SPECT measurements. During in vivo tests, fluorescence and radioactivity from the MOMIA were colocalized in spatially coincident regions with an average center-of-mass error of 2.68 ± 1.0 mm between FMT and SPECT for axillary lymph node localization. Intravital imaging with surgical exposure of the lymph node validated the localization of the optical contrast.

CONCLUSION

The feasibility of integrating a fiber-based, video-rate FMT system with a commercial preclinical SPECT/CT platform was established. These coregistered FMT and SPECT/CT results with MOMIAs may facilitate the development of the next generation of preclinical and clinical multimodal optical-nuclear platforms for a broad array of imaging applications and help elucidate the underlying biologic processes relevant to cancer diagnosis and therapy monitoring.

Parametric estimation of 3D tubular structures for diffuse optical tomography

We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction.

Data-resolution based optimization of the data-collection strategy for near infrared diffuse optical tomography

PURPOSE

To optimize the data-collection strategy for diffuse optical tomography and to obtain a set of independent measurements among the total measurements using the model based data-resolution matrix characteristics.

METHODS

The data-resolution matrix is computed based on the sensitivity matrix and the regularization scheme used in the reconstruction procedure by matching the predicted data with the actual one. The diagonal values of data-resolution matrix show the importance of a particular measurement and the magnitude of off-diagonal entries shows the dependence among measurements. Based on the closeness of diagonal value magnitude to off-diagonal entries, the independent measurements choice is made. The reconstruction results obtained using all measurements were compared to the ones obtained using only independent measurements in both numerical and experimental phantom cases. The traditional singular value analysis was also performed to compare the results obtained using the proposed method.

RESULTS

The results indicate that choosing only independent measurements based on data-resolution matrix characteristics for the image reconstruction does not compromise the reconstructed image quality significantly, in turn reduces the data-collection time associated with the procedure. When the same number of measurements (equivalent to independent ones) are chosen at random, the reconstruction results were having poor quality with major boundary artifacts. The number of independent measurements obtained using data-resolution matrix analysis is much higher compared to that obtained using the singular value analysis.

CONCLUSIONS

The data-resolution matrix analysis is able to provide the high level of optimization needed for effective data-collection in diffuse optical imaging. The analysis itself is independent of noise characteristics in the data, resulting in an universal framework to characterize and optimize a given data-collection strategy.

Singular value decomposition based regularization prior to spectral mixing improves crosstalk in dynamic imaging using spectral diffuse optical tomography

The spectrally constrained diffuse optical tomography (DOT) method relies on incorporating spectral prior information directly into the image reconstruction algorithm, thereby correlating the underlying optical properties across multiple wavelengths. Although this method has been shown to provide a solution that is stable, the use of conventional Tikhonov-type regularization techniques can lead to additional crosstalk between parameters, particularly in linear, single-step dynamic imaging applications. This is due mainly to the suboptimal regularization of the spectral Jacobian matrix, which smoothes not only the image-data space, but also the spectral mapping space. In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters. Using simulated data, images of changes in oxygenated and deoxygenated hemoglobin concentrations are reconstructed via the SVD-based approach and compared with images reconstructed by using non-spectral and conventional spectral methods. In a 2D, two wavelength example, it is shown that the proposed approach provides a 98% reduction in crosstalk between recovered parameters as compared with conventional spectral reconstruction algorithms, and 60% as compared with non-spectrally constrained algorithms. Using a subject specific multilayered model of the human head, a noiseless dynamic simulation of cortical activation is performed to further demonstrate such improvement in crosstalk. However, with the addition of realistic noise in the data, both non-spectral and proposed algorithms perform similarly, indicating that the use of spectrally constrained reconstruction algorithms in dynamic DOT may be limited by the contrast of the signal as well as the noise characteristics of the system.