Image reconstruction in optical tomography in the presence of coupling errors

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Imaging affective and non-affective touch processing in two-year-old children

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

Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.

Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography

In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

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.

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.

Movement artefact removal from NIRS signal using multi-channel IMU data

Background

The non-invasive nature of near-infrared spectroscopy (NIRS) makes it a widely accepted method for blood oxygenation measurement in various parts of the human body. One of the main challenges in this method lies in the successful removal of movement artefacts in the detected signal. In this respect, multi-channel inertia measurement unit (IMU) containing accelerometer, gyroscope and magnetometer can be used for better modelling of movement artefact than using accelerometer only, which as a result, movement artefact can be more accurately removed.

Methods

A wearable two-channel continuous wave NIRS system, incorporating an IMU sensor which contain accelerometer, gyroscope and magnetometer in it, was developed to record NIRS signal along with the simultaneous recording of movement artefacts related signal using the IMU. Four healthy subjects volunteered in the recording of the NIRS signals. During the recording from the first two subject, movement artefacts were simulated in one of the NIRS channels by tapping the photodiode sensor nearby. The corresponding IMU data for the simulated movement artefacts were used to estimate the artefacts in the corrupted signal by autoregressive with exogenous input method and subtracted from the corrupted signal to remove the artefacts in the NIRS signal. Signal-to-noise ratio (SNR) improvement was used to evaluate the performance of the movement artefacts removal process. The performance of the movement artefacts estimation and removal were compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading. For the remaining two subjects the NIRS signal was recorded by natural movement artefacts impact and the results of artefacts removal was compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading.

Results

The quantitative and qualitative results revealed that the SNR improvement increases with the number of IMU channels used in the artefacts estimation, and there were approximately 5–11 dB increase in SNR when nine channel IMU data were used rather than using only three channel accelerometer data only. The artefact removal from natural movements also demonstrated that the combination of gyroscope and magnetometer sensors with accelerometer provided better estimation and removal of the movement artefacts, which was revealed by the minimal change of the HbO₂ and Hb level before, during and after movement artefacts occurred in the NIRS signal.

Conclusion

The movement artefacts in NIRS can be more accurately estimated and removed by using accelerometer, gyroscope and magnetograph signals from an integrated IMU sensor than using accelerometer signal only.

Discrimination of Complex Activation Patterns in Near Infrared Optical Tomography with Artificial Neural Networks

Near-infrared optical tomography (NIROT) has great promise for many clinical problems. Here we focus on the study of brain function. During NIROT image reconstruction of brain activity, an inverse problem has to be solved that is sensitive to small superficial perturbations on the head such as e.g. birthmarks on the skin and hair. To consider these perturbations, standard physical modeling is unpractical, since it requires the implementation of detailed information that is generally unavailable. The aim here was to test whether artificial neural networks (ANN) are able to handle such perturbations and thus detect brain activity correctly. For simplicity, we created a virtual test model, where we simulated a pattern of activated and resting brain regions, which was covered by skin features like hair or melanin. We compared the performance of this ANN approach with that of an inverse problem based on a Monte Carlo (MC) model for light propagation. We conclude that ANNs tolerate substantially higher levels of skin perturbations than MC models and consequently are more suitable for detecting brain activity.

Fast multislice fluorescence molecular tomography using sparsity-inducing regularization

Fluorescence molecular tomography (FMT) is a rapidly growing imaging method that facilitates the recovery of small fluorescent targets within biological tissue. The major challenge facing the FMT reconstruction method is the ill-posed nature of the inverse problem. In order to overcome this problem, the acquisition of large FMT datasets and the utilization of a fast FMT reconstruction algorithm with sparsity regularization have been suggested recently. Therefore, the use of a joint L1/total-variation (TV) regularization as a means of solving the ill-posed FMT inverse problem is proposed. A comparative quantified analysis of regularization methods based on L1-norm and TV are performed using simulated datasets, and the results show that the fast composite splitting algorithm regularization method can ensure the accuracy and robustness of the FMT reconstruction. The feasibility of the proposed method is evaluated in an in vivo scenario for the subcutaneous implantation of a fluorescent-dye-filled capillary tube in a mouse, and also using hybrid FMT and x-ray computed tomography data. The results show that the proposed regularization overcomes the difficulties created by the ill-posed inverse problem.

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors.

Adaptable near-infrared spectroscopy fiber array for improved coupling to different breast sizes during clinical MRI

RATIONALE AND OBJECTIVES

Near-infrared spectroscopy (NIRS) of breast can provide functional information on the vascular and structural compartments of tissues in regions identified during simultaneous magnetic resonance imaging (MRI). NIRS can be acquired during dynamic contrast-enhanced MRI (DCE-MRI) to accomplish image-guided spectroscopy of the enhancing regions, potentially increasing the diagnostic specificity of the examination and reducing the number of biopsies performed as a result of inconclusive MRI breast imaging studies.

MATERIALS AND METHODS

We combine synergistic attributes of concurrent DCE-MRI and NIRS with a new design of the clinical NIRS breast interface that couples to a standard MR breast coil and allows imaging of variable breast sizes. Spectral information from healthy volunteers and cancer patients is recovered, providing molecular information in regions defined by the segmented MR image volume.

RESULTS

The new coupling system significantly improves examination utility by allowing improved coupling of the NIR fibers to breasts of all cup sizes and lesion locations. This improvement is demonstrated over a range of breast sizes (cup size A through D) and normal tissue heterogeneity using a group of eight healthy volunteers and two cancer patients. Lesions located in the axillary region and medial-posterior breast are now accessible to NIRS optodes. Reconstructed images were found to have biologically plausible hemoglobin content, oxygen saturation, and water and lipid fractions.

CONCLUSIONS

In summary, a new NIRS/MRI breast interface was developed to accommodate the variation in breast sizes and lesion locations that can be expected in clinical practice. DCE-MRI-guided NIRS quantifies total hemoglobin, oxygenation, and scattering in MR-enhancing regions, increasing the diagnostic information acquired from MR examinations.

Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Diffuse optical tomography is highly sensitive to measurement and modeling errors. Errors in the source and detector coupling and positions can cause significant artifacts in the reconstructed images. Recently the approximation error theory has been proposed to handle modeling errors. In this article, we investigate the feasibility of the approximation error approach to compensate for modeling errors due to inaccurately known optode locations and coupling coefficients. The approach is evaluated with simulations. The results show that the approximation error method can be used to recover from artifacts in reconstructed images due to optode coupling and position errors.

Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Diffuse optical tomography is highly sensitive to measurement and modeling errors. Errors in the source and detector coupling and positions can cause significant artifacts in the reconstructed images. Recently the approximation error theory has been proposed to handle modeling errors. In this article, we investigate the feasibility of the approximation error approach to compensate for modeling errors due to inaccurately known optode locations and coupling coefficients. The approach is evaluated with simulations. The results show that the approximation error method can be used to recover from artifacts in reconstructed images due to optode coupling and position errors.

Optical imaging in breast cancer diagnosis: the next evolution

Breast cancer is one of the most common cancers among the population of the Western world. Diagnostic methods include mammography, ultrasound, and magnetic resonance; meanwhile, nuclear medicine techniques have a secondary role, being useful in regional assessment and therapy followup. Optical imaging is a very promising imaging technique that uses near-infrared light to assess optical properties of tissues and is expected to play an important role in breast cancer detection. Optical breast imaging can be performed by intrinsic breast tissue contrast alone (hemoglobin, water, and lipid content) or with the use of exogenous fluorescent probes that target specific molecules for breast cancer. Major advantages of optical imaging are that it does not use any radioactive components, very high sensitivity, relatively inexpensive, easily accessible, and the potential to be combined in a multimodal approach with other technologies such as mammography, ultrasound, MRI, and positron emission tomography. Moreover, optical imaging agents could, potentially, be used as “theranostics,” combining the process of diagnosis and therapy.

Automatic and robust calibration of optical detector arrays for biomedical diffuse optical spectroscopy

The design and testing of a new, fully automated, calibration approach is described. The process was used to calibrate an image-guided diffuse optical spectroscopy system with 16 photomultiplier tubes (PMTs), but can be extended to any large array of optical detectors and associated imaging geometry. The design goals were accomplished by developing a routine for robust automated calibration of the multi-detector array within 45 minutes. Our process was able to characterize individual detectors to a median norm of the residuals of 0.03 V for amplitude and 4.4 degrees in phase and achieved less than 5% variation between all the detectors at the 95% confidence interval for equivalent measurements. Repeatability of the calibrated data from the imaging system was found to be within 0.05 V for amplitude and 0.2 degrees for phase, and was used to evaluate tissue-simulating phantoms in two separate imaging geometries. Spectroscopic imaging of total hemoglobin concentration was recovered to within 5% of the true value in both cases. Future work will focus on streamlining the technology for use in a clinical setting with expectations of achieving accurate quantification of suspicious lesions in the breast.

A hematoma detector-a practical application of instrumental motion as signal in near infra-red imaging

In this paper we discuss results based on using instrumental motion as a signal rather than treating it as noise in Near Infra-Red (NIR) imaging. As a practical application to demonstrate this approach we show the design of a novel NIR hematoma detection device. The proposed device is based on a simplified single source configuration with a dual separation detector array and uses motion as a signal for detecting changes in blood volume in the dural regions of the head. The rapid triage of hematomas in the emergency room will lead to improved use of more sophisticated/expensive imaging facilities such as CT/MRI units. We present simulation results demonstrating the viability of such a device and initial phantom results from a proof of principle device. The results demonstrate excellent localization of inclusions as well as good quantitative comparisons.

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

Reduction of image artifacts induced by change in the optode coupling in time-resolved diffuse optical tomography

Tomographic images of the optical properties can be reconstructed using inversion algorithms for diffuse optical tomography (DOT); however, changes in the optode coupling that occurs while obtaining an object's measurements may often lead to the presence of artifacts in the reconstructed images. To reduce the number of artifacts induced by optode coupling, previous studies have introduced (unknown) coupling coefficients in reconstruction algorithms, which were found to be effective for continuous wave- and frequency-domain DOT. This study aims to investigate the effects of optode calibration on the reconstructed images of time-domain DOT. Here, coupling coefficients are incorporated into the time-domain DOT algorithm based on a modified generalized pulse spectrum technique. The images of the absorption coefficient are reconstructed in various numerical simulations, phantom experiments, and in vivo experiments of time-domain DOT. As a result, the artifacts resulting from changes in optode coupling are reduced in the reconstructed images of the absorption coefficient, thereby demonstrating that introduction of coupling coefficients is effective in time-domain DOT. Moreover, numerical simulations, phantom experiments, and in vivo studies have validated this algorithm.

Localization of an absorber in a turbid semi-infinite medium by spatially resolved continuous-wave diffuse reflectance measurements

A method to locate an absorber embedded in a semi-infinite turbid medium by spatially-resolved continuous-wave (SRCW) diffuse reflectance measurements is introduced. The depth of the absorber is assessed by single wavelength SRCW diffuse reflectance measurements by two detectors in a radial row. The ratio of perturbations introduced by the defect at two detectors is used to be matched with the ratio-versus-depth curve, which are generated by approximate formulas of continuous wave diffuse reflectance. The error due to approximation and the error in depth assessment are studied for different cases revealing favorable source-detector placements with respect to planar position of the defect. The effect of lateral displacement of the source with respect to defect is studied. A strategy to overcome errors introduced by erroneous prediction of background medium optical properties is suggested. Theoretical results indicate that the depth of the absorber can be obtained with 0.1 mm precision independent of its absorption coefficient and its size for the values chosen in the study. The approach is tested experimentally and it is observed that theoretical results fit with experimental data.

Optimal analysis method for dynamic contrast-enhanced diffuse optical tomography

Diffuse Optical Tomography (DOT) is an optical imaging modality that has various clinical applications. However, the spatial resolution and quantitative accuracy of DOT is poor due to strong photon scatting in biological tissue. Structural a priori information from another high spatial resolution imaging modality such as Magnetic Resonance Imaging (MRI) has been demonstrated to significantly improve DOT accuracy. In addition, a contrast agent can be used to obtain differential absorption images of the lesion by using dynamic contrast enhanced DOT (DCE-DOT). This produces a relative absorption map that consists of subtracting a reconstructed baseline image from reconstructed images in which optical contrast is included. In this study, we investigated and compared different reconstruction methods and analysis approaches for regular endogenous DOT and DCE-DOT with and without MR anatomical a priori information for arbitrarily-shaped objects. Our phantom and animal studies have shown that superior image quality and higher accuracy can be achieved using DCE-DOT together with MR structural a priori information. Hence, implementation of a combined MRI-DOT system to image ICG enhancement can potentially be a promising tool for breast cancer imaging.

Remote positioning optical breast magnetic resonance coil for slice-selection during image-guided near-infrared spectroscopy of breast cancer

The design and testing of a pneumatic optical positioning interface produced with the goal of improving fiber positioning in magnetic resonance (MR)-guided diffuse spectral imaging of breast cancer is presented. The system was created for vertical positioning of optical fibers inside the MR bore during a patient exam to target suspicious lesions with MR scans for reference and collect multiple planes of optical data. The interface includes new fiber plates for mechanical and optical coupling to the breast, and was tested in phantoms and human imaging. Reconstructions with data taken in the new interface show acceptable linearity over different absorber concentrations (residual norm = 0.067), and exhibit good contrast recovery at different imaging planes, which is consistent with previous work. An example of human breast imaging through the new interface is shown and a discussion of how it compares to other patient interfaces for breast imaging is presented. Design goals of increasing the available degrees of freedom for fiber positioning while maintaining good patient-fiber contact and comfort were accomplished. This interface allows improved volumetric imaging with interactive and accurate slice selection to quantify targeted suspicious lesions.

Optical mammography: Diffuse optical imaging of breast cancer

Existing imaging modalities for breast cancer screening, diagnosis and therapy monitoring, namely X-ray mammography and magnetic resonance imaging, have been proven to have limitations. Diffuse optical imaging is a set of non-invasive imaging modalities that use near-infrared light, which can be an alternative, if not replacement, to those existing modalities. This review covers the background knowledge, recent clinical outcome, and future outlook of this newly emerging medical imaging modality.

Diffuse Optics for Tissue Monitoring and Tomography

This review describes the diffusion model for light transport in tissues and the medical applications of diffuse light. Diffuse optics is particularly useful for measurement of tissue hemodynamics, wherein quantitative assessment of oxy- and deoxy-hemoglobin concentrations and blood flow are desired. The theoretical basis for near-infrared or diffuse optical spectroscopy (NIRS or DOS, respectively) is developed, and the basic elements of diffuse optical tomography (DOT) are outlined. We also discuss diffuse correlation spectroscopy (DCS), a technique whereby temporal correlation functions of diffusing light are transported through tissue and are used to measure blood flow. Essential instrumentation is described, and representative brain and breast functional imaging and monitoring results illustrate the workings of these new tissue diagnostics.

Direct-current-based image reconstruction versus direct-current included or excluded frequency-domain reconstruction in diffuse optical tomography

We study the level of image artifacts in optical tomography associated with measurement uncertainty under three reconstruction configurations, namely, by using only direct-current (DC), DC-excluded frequency-domain, and DC-included frequency-domain data. Analytic and synthetic studies demonstrate that, at the same level of measurement uncertainty typical to optical tomography, the ratio of the standard deviation of mu(a) over mu(a) reconstructed by DC only is at least 1.4 times lower than that by frequency-domain methods. The ratio of standard deviations of D (or mu(s)') over D (or mu(s)') reconstructed by DC only are slightly lower than those by frequency-domain methods. Frequency-domain reconstruction including DC generally outperforms that excluding DC, but as the amount of measurements increases, the difference between the two diminishes. Under the condition of a priori structural information, the performances of three reconstruction configurations are seemingly equivalent.

Approximation errors and model reduction in three-dimensional diffuse optical tomography

Model reduction is often required in diffuse optical tomography (DOT), typically because of limited available computation time or computer memory. In practice, this means that one is bound to use coarse mesh and truncated computation domain in the model for the forward problem. We apply the (Bayesian) approximation error model for the compensation of modeling errors caused by domain truncation and a coarse computation mesh in DOT. The approach is tested with a three-dimensional example using experimental data. The results show that when the approximation error model is employed, it is possible to use mesh densities and computation domains that would be unacceptable with a conventional measurement error model.

Combined optical imaging and mammography of the healthy breast: optical contrast derived from breast structure and compression

In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 microm and 71%, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1+/-6.1 microm for fibroglandular tissue, 15.4+/-5.0 microm for adipose, and 22.2+/-7.3 microm for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p < 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis.

Optical imaging of the breast

This review provides a summary of the current state of optical breast imaging and describes its potential future clinical applications in breast cancer imaging. Optical breast imaging is a novel imaging technique that uses near-infrared light to assess the optical properties of breast tissue. In optical breast imaging, two techniques can be distinguished, i.e. optical imaging without contrast agent, which only makes use of intrinsic tissue contrast, and optical imaging with a contrast agent, which uses exogenous fluorescent probes. In this review the basic concepts of optical breast imaging are described, clinical studies on optical imaging without contrast agent are summarized, an outline of preclinical animal studies on optical breast imaging with contrast agents is provided, and, finally, potential applications of optical breast imaging in clinical practice are addressed. Based on the present literature, diagnostic performance of optical breast imaging without contrast agent is expected to be insufficient for clinical application. Development of contrast agents that target specific molecular changes associated with breast cancer formation is the opportunity for clinical success of optical breast imaging.

Methodology development for three-dimensional MR-guided near infrared spectroscopy of breast tumors

Combined Magnetic Resonance (MR) and Near Infrared Spectroscopy (NIRS) has been proposed as a unique method to quantify hemodynamics, water content, and cellular size and packing density of breast tumors, as these tissue constituents can be quantified with increased resolution and overlaid on the structural features identified by the MR. However, the choices in how to reconstruct and visualize this information can have a dramatic impact on the feasibility of implementing this modality in the clinic. This is especially true in 3 dimensions, as there is often limited optical sampling of the breast tissue, and methods need to accurately reflect the tissue composition. In this paper, the implementation and display of fully 3D MR image-guided NIRS is outlined and demonstrated using in vivo data from three healthy women and a volunteer undergoing neoadjuvant chemotherapy. Additionally, a display feature presented here scales the transparency of the optical images to the sensitivity of the measurements, providing a logical way to incorporate partial volume sets of optical images onto the MR volume. These concepts are demonstrated with 3D data sets using Volview software online.

Implementation of a computationally efficient least-squares algorithm for highly under-determined three-dimensional diffuse optical tomography problems

Three-dimensional (3D) diffuse optical tomography is known to be a nonlinear, ill-posed and sometimes under-determined problem, where regularization is added to the minimization to allow convergence to a unique solution. In this work, a generalized least-squares (GLS) minimization method was implemented, which employs weight matrices for both data-model misfit and optical properties to include their variances and covariances, using a computationally efficient scheme. This allows inversion of a matrix that is of a dimension dictated by the number of measurements, instead of by the number of imaging parameters. This increases the computation speed up to four times per iteration in most of the under-determined 3D imaging problems. An analytic derivation, using the Sherman-Morrison-Woodbury identity, is shown for this efficient alternative form and it is proven to be equivalent, not only analytically, but also numerically. Equivalent alternative forms for other minimization methods, like Levenberg-Marquardt (LM) and Tikhonov, are also derived. Three-dimensional reconstruction results indicate that the poor recovery of quantitatively accurate values in 3D optical images can also be a characteristic of the reconstruction algorithm, along with the target size. Interestingly, usage of GLS reconstruction methods reduces error in the periphery of the image, as expected, and improves by 20% the ability to quantify local interior regions in terms of the recovered optical contrast, as compared to LM methods. Characterization of detector photo-multiplier tubes noise has enabled the use of the GLS method for reconstructing experimental data and showed a promise for better quantification of target in 3D optical imaging. Use of these new alternative forms becomes effective when the ratio of the number of imaging property parameters exceeds the number of measurements by a factor greater than 2.