Choice of data types in time resolved fluorescence enhanced diffuse optical tomography

In this paper we examine possible data types for time resolved fluorescence enhanced diffuse optical tomography (FDOT). FDOT is a particular case of diffuse optical tomography, where our goal is to analyze fluorophores deeply embedded in a turbid medium. We focus on the relative robustness of the different sets of data types to noise. We use an analytical model to generate the expected temporal point spread function (TPSF) and generate the data types from this. Varying levels of noise are applied to the TPSF before generating the data types. We show that local data types are more robust to noise than global data types, and should provide enhanced information to the inverse problem. We go on to show that with a simple reconstruction algorithm, depth and lifetime (the parameters of interest) of the fluorophore are better reconstructed using the local data types. Further we show that the relationship between depth and lifetime is better preserved for the local data types, suggesting they are in some way not only more robust, but also self-regularizing. We conclude that while the local data types may be more expensive to generate in the general case, they do offer clear advantages over the standard global data types.     

Image analysis methods for diffuse optical tomography

Three major analytical tools in imaging science are summarized and demonstrated relative to optical imaging in vivo. Standard resolution testing is optimal when infinite contrast is used and hardware evaluation is the goal. However, deep tissue imaging of absorption or fluorescent contrast agents in vivo often presents a different problem, which requires contrast-detail analysis. This analysis shows that the minimum detectable sizes are in the range of 1/10 the outer diameter, whereas minimum detectable contrast values are in the range of 10 to 20% relative to the continuous background values. This is estimated for objects being in the center of the domain being imaged, and as the heterogeneous region becomes closer to the surface, the lower limit on size and contrast can become arbitrarily low and more dictated by hardware specifications. Finally, if human observer detection of abnormalities in the images is the goal, as is standard in most radiological practice, receiver operating characteristic (ROC) curve and location receiver operating characteristic curve (LROC) are used. Each of these three major areas of image interpretation and analysis are reviewed in the context of medical imaging as well as how they are used to quantify the performance of diffuse optical imaging of tissue.     

Target detection and quantification using a hybrid hand-held diffuse optical tomography and photoacoustic tomography system

We present a photoacoustic tomography-guided diffuse optical tomography approach using a hand-held probe for detection and characterization of deeply-seated targets embedded in a turbid medium. Diffuse optical tomography guided by coregistered ultrasound, MRI, and x ray has demonstrated a great clinical potential to overcome lesion location uncertainty and to improve light quantification accuracy. However, due to the different contrast mechanisms, some lesions may not be detectable by a nonoptical modality but yet have high optical contrast. Photoacoustic tomography utilizes a short-pulsed laser beam to diffusively penetrate into tissue. Upon absorption of the light by the target, photoacoustic waves are generated and used to reconstruct, at ultrasound resolution, the optical absorption distribution that reveals optical contrast. However, the robustness of optical property quantification of targets by photoacoustic tomography is complicated because of the wide range of ultrasound transducer sensitivity, the orientation and shape of the targets relative to the ultrasound array, and the uniformity of the laser beam. We show in this paper that the relative optical absorption map provided by photoacoustic tomography can potentially guide the diffuse optical tomography to accurately reconstruct target absorption maps.     

Quantitative photoacoustic imaging: correcting for heterogeneous light fluence distributions using diffuse optical tomography

The specificity of molecular and functional photoacoustic (PA) images depends on the accuracy of the photoacoustic absorption spectroscopy. The PA signal is proportional to the product of the optical absorption coefficient and local light fluence; quantitative PA measurements of the optical absorption coefficient therefore require an accurate estimation of optical fluence. Light-modeling aided by diffuse optical tomography (DOT) can be used to map the required fluence and to reduce errors in traditional PA spectroscopic analysis. As a proof-of-concept, we designed a tissue-mimicking phantom to demonstrate how fluence-related artifacts in PA images can lead to misrepresentations of tissue properties. To correct for these inaccuracies, the internal fluence in the tissue phantom was estimated by using DOT to reconstruct spatial distributions of the absorption and reduced scattering coefficients of multiple targets within the phantom. The derived fluence map, which only consisted of low spatial frequency components, was used to correct PA images of the phantom. Once calibrated to a known absorber, this method reduced errors in estimated absorption coefficients from 33% to 6%. These results experimentally demonstrate that combining DOT with PA imaging can significantly reduce fluence-related errors in PA images, while producing quantitatively accurate, high-resolution images of the optical absorption coefficient.     

Quantitative diffuse optical tomography for small animals using an ultrafast gated image intensifier

The quantitative accuracy of fluorescence and bioluminescence imaging of small animals can be improved by knowledge of the in situ optical properties of each animal. Obtaining in situ optical property maps is challenging, however, due to short propagation distances, requirements for high dynamic range, and the need for dense spatial, temporal, and spectral sampling. Using an ultrafast gated image intensifier and a pulsed laser source, we have developed a small animal diffuse optical tomography system with multiple synthetic modulation frequencies up to >1 GHz. We show that amplitude and phase measurements with useful contrast-to-noise ratios can be obtained for modulation frequencies over the range of approximately 250 to 1250 MHz. Experiments with tissue simulating phantoms demonstrate the feasibility of reconstructing the absorption and scattering optical properties in a small animal imaging system.     

Speckle reduction in optical coherence tomography images using tissue viscoelasticity

We present a technique to reduce speckle in optical coherence tomography images of soft tissues. An average is formed over a set of B-scans that have been decorrelated by viscoelastic creep strain. The necessary correction for the deformation-induced spatial distortions between B-scans is achieved through geometrical co-registration using an affine transformation. Speckle reduction by up to a factor of 1.65 is shown in images of tissue-mimicking soft fibrin phantoms and excised human lymph node tissue with no observable loss of spatial resolution.     

A soft deformable tissue-equivalent phantom for diffuse optical tomography

A recipe is presented for the manufacture of highly compressible phantoms for diffuse optical tomography. The recipe is based on polyvinyl alcohol (PVA) slime, a viscoelastic fluid which readily deforms under moderate pressure. Scattering particles and absorbing compounds can be added to provide a uniform material with stable and reproducible optical properties. A linear relationship between the concentration of scattering particles (either titanium dioxide or microspheres) and the transport scatter coefficient is demonstrated. Phantoms of an arbitrary size and shape may be produced by containing the slime within a thin latex shell, and a stability over a period of at least 3 months has been established. The deformable phantoms may be used to test and calibrate optical tomography systems designed for use on patients with irregular or variable geometries.     

Gauss-Newton method for image reconstruction in diffuse optical tomography

We present a regularized Gauss-Newton method for solving the inverse problem of parameter reconstruction from boundary data in frequency-domain diffuse optical tomography. To avoid the explicit formation and inversion of the Hessian which is often prohibitively expensive in terms of memory resources and runtime for large-scale problems, we propose to solve the normal equation at each Newton step by means of an iterative Krylov method, which accesses the Hessian only in the form of matrix-vector products. This allows us to represent the Hessian implicitly by the Jacobian and regularization term. Further we introduce transformation strategies for data and parameter space to improve the reconstruction performance. We present simultaneous reconstructions of absorption and scattering distributions using this method for a simulated test case and experimental phantom data.     

Artifact reduction method in ultrasound-guided diffuse optical tomography using exogenous contrast agents

In diffuse optical tomography (DOT), a typical perturbation approach requires two sets of measurements obtained at the lesion breast (lesion or target site) and a contra-lateral location of the normal breast (reference site) for image reconstruction. For patients who have a small amount of breast tissue, the chest-wall underneath the breast tissue at both sites affects the imaging results. In this group of patients, the perturbation, which is the difference between measurements obtained at the lesion and reference sites, may include the information of background mismatch which can generate artifacts or affect the reconstructed quantitative absorption coefficient of the lesion. Also, for patients who have a single breast due to prior surgery, the contra-lateral reference is not available. To improve the DOT performance or overcome its limitation, we introduced a new method based on an exogenous contrast agent and demonstrate its performance using animal models. Co-registered ultrasound was used to guide the lesion localization. The results have shown that artifacts caused by background mismatch can be reduced significantly by using this new method.     

Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe

We quantify the variability in diffuse optical tomography (DOT) sensitivity over the cortical surface in eight young adult subjects. We use the 10/5 electroencephalography system as a basis for our whole-head optical high-density probe design. The contrast-to-noise ratio (CNR) is calculated along with the percentage of the cortex that is above a CNR = 0 dB threshold. We also quantify the effect of including vasculature on the forward model and list our assumptions that allow us to estimate light penetration depth in the head. We show that using the 10/5 system for the optical probe design allows for the measurement of 37% of the cortical surface on average, with a mean CNR in the visible region of 5.5 dB. Certain anatomical regions, such as the lateral occipital cortex, had a very high percentage above the CNR threshold, while other regions such as the cingulate cortex were not measurable. Vasculature blocked optical sensitivity over 1% of the cortex. Cortical coverage was positively correlated with intracranial volume and relative cerebrospinal fluid volume, and negatively correlated with relative scalp volume and skull volume. These contributions allow experimenters to understand how anatomical variation in a subject population may impact DOT or functional near-infrared spectroscopy measurements.     

Computer-aided interpretation approach for optical tomographic images

A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging, and inspection of optical tomographic images), were used to produce ground truth benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities, while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.     

Experimental measurement of time-dependent photon scatter for diffuse optical tomography

Time-resolved measurement of early arriving photons through diffusive media has been shown to effectively reduce the high degree of light scatter in biological tissue. However, the experimentally achievable reduction in photon scatter and the impact of time-gated detection on instrument noise performance is not well understood. We measure time-dependent photon density sensitivity functions (PDSFs) between a pulsed laser source and a photomultiplier tube operating in time-correlated single-photon-counting mode. Our data show that with our system, measurement of early arriving photons reduces the full width half maximum of PDSFs on average by about 40 to 60% versus quasicontinuous wave photons over a range of experimental conditions similar to those encountered in small animal tomography, corresponding to a 64 to 84% reduction in PDSF volume. Factoring in noise considerations, the optimal operating point of our instrument is determined to be about the 10% point on the rising edge of the transmitted intensity curve. Time-dependent Monte Carlo simulations and the time-resolved diffusion approximation are used to model photon propagation and are evaluated for agreement with experimental data.     

Texture analysis of optical coherence tomography images: feasibility for tissue classification

Optical coherence tomography (OCT) acquires cross-sectional images of tissue by measuring back-reflected light. Images from in vivo OCT systems typically have a resolution of 10 to 15 mm, and are thus best suited for visualizing structures in the range of tens to hundreds of microns, such as tissue layers or glands. Many normal and abnormal tissues lack visible structures in this size range, so it may appear that OCT is unsuitable for identification of these tissues. However, examination of structure-poor OCT images reveals that they frequently display a characteristic texture that is due to speckle. We evaluated the application of statistical and spectral texture analysis techniques for differentiating tissue types based on the structural and speckle content in OCT images. Excellent correct classification rates were obtained when images had slight visual differences (mouse skin and fat, correct classification rates of 98.5 and 97.3%, respectively), and reasonable rates were obtained with nearly identical-appearing images (normal versus abnormal mouse lung, correct classification rates of 64.0 and 88.6%, respectively). This study shows that texture analysis of OCT images may be capable of differentiating tissue types without reliance on visible structures.     

Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging

Diffuse optical imaging is an effective technique for noninvasive functional brain imaging. However, the measurements respond to systemic hemodynamic fluctuations caused by the cardiac cycle, respiration, and blood pressure, which may obscure or overwhelm the desired stimulus-evoked response. Previous work on this problem employed temporal filtering, estimation of systemic effects from background pixels, or modeling of interference signals with predefined basis functions, with some success. However, weak signals are still lost in the interference, and other complementary methods are desirable. We use the spatial behavior of measured baseline signals to identify the interference subspaces. We then project signals components in this subspace out of the stimulation data. In doing so, we assume that systemic interference components will be more global spatially, with higher energy, than the stimulus-evoked signals of interest. Thus, the eigenvectors corresponding to the largest eigenvalues of an appropriate correlation matrix form the basis for an interference subspace. By projecting the data onto the orthogonal nullspace of these eigenvectors, we can obtain more localized response, as reflected in improved contrast-to-noise ratio and correlation coefficient maps.     

Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy

Diffuse optical spectroscopy (DOS) provides a powerful tool for fast and noninvasive disease diagnosis. The ability to leverage DOS to accurately quantify tissue optical parameters hinges on the model used to estimate light-tissue interaction. We describe the accuracy of a lookup table (LUT)-based inverse model for measuring optical properties under different conditions relevant to biological tissue. The LUT is a matrix of reflectance values acquired experimentally from calibration standards of varying scattering and absorption properties. Because it is based on experimental values, the LUT inherently accounts for system response and probe geometry. We tested our approach in tissue phantoms containing multiple absorbers, different sizes of scatterers, and varying oxygen saturation of hemoglobin. The LUT-based model was able to extract scattering and absorption properties under most conditions with errors of less than 5 percent. We demonstrate the validity of the lookup table over a range of source-detector separations from 0.25 to 1.48 mm. Finally, we describe the rapid fabrication of a lookup table using only six calibration standards. This optimized LUT was able to extract scattering and absorption properties with average RMS errors of 2.5 and 4 percent, respectively.     

Design and implementation of a multifrequency near-infrared diffuse optical tomography system

The design and implementation of a multifrequency and multispectral diffuse optical tomography system is described. Four wavelengths are utilized: 665, 785, 808, and 830 nm. The system is based on a network analyzer, which provides rf modulation signals for the laser diodes, as well as measures the amplitude and the phase of the detected signals. Six different modulation frequencies ranging from 110 to 280 MHz are used. The details of instrumentation, calibration, data acquisition, and performance of the system are given. A finite element algorithm is used to solve the diffusion equation, and an inverse solver based on this forward solver is implemented to calculate the absorption and scattering maps from the acquired data. Data acquisition for one wavelength is completed in less than 2.5 min for a single modulation frequency. The measurement repeatability is 0.5% in ac intensity and 0.2 deg in phase. The performance of the system is evaluated with phantom studies. A multifrequency reconstruction algorithm is used, in which a single absorption and scattering image pair is obtained using the whole dataset obtained at different modulation frequencies. It is shown that the multifrequency reconstruction approach provides superior image quality compared to the single frequency counterpart.