Optical Image Reconstruction Research Articles

Improvement of absorption and scattering discrimination by selection of sensitive points on temporal profile in diffuse optical tomography

We present a new method allowing the reconstruction of 3D time-domain diffuse optical tomography images, based on the time-dependent diffusion equation and an iterative algorithm (ART) using specific points on the temporal profiles. The first advantage of our method versus the full time-resolved scheme consists in considerably reducing the inverse problem resolution time. Secondly, in the presence of scattering heterogeneities, our method provides images of better quality comparatively to classical methods using full-time data or the first moments of the profiles.

A fast multi-resolution approach to tomographic PIV

Tomographic particle image velocimetry (Tomo-PIV) is a recently developed three-component, three-dimensional anemometric non-intrusive measurement technique, based on an optical tomographic reconstruction applied to simultaneously recorded images of the distribution of light intensity scattered by seeding particles immersed into the flow. Nowadays, the reconstruction process is carried out mainly by iterative algebraic reconstruction techniques, well suited to handle the problem of limited number of views, but computationally intensive and memory demanding. The adoption of the multiplicative algebraic reconstruction technique (MART) has become more and more accepted. In the present work, a novel multi-resolution approach is proposed, relying on the adoption of a coarser grid in the first step of the reconstruction to obtain a fast estimation of a reliable and accurate first guess. A performance assessment, carried out on three-dimensional computer-generated distributions of particles, shows a substantial acceleration of the reconstruction process for all the tested seeding densities with respect to the standard method based on 5 MART iterations; a relevant reduction in the memory storage is also achieved. Furthermore, a slight accuracy improvement is noticed. A modified version, improved by a multiplicative line of sight estimation of the first guess on the compressed configuration, is also tested, exhibiting a further remarkable decrease in both memory storage and computational effort, mostly at the lowest tested seeding densities, while retaining the same performances in terms of accuracy.

Optical property reconstruction for interstitial diffuse optical tomography

Optical property reconstruction for interstitial diffuse optical tomography

A study of capillary discharge lamps in Ar–Hg and Xe–Hg mixtures

Low-pressure capillary discharge lamps in Ar–Hg and Xe–Hg mixtures are studied. The discharge size is 0.5 mm (500 µm) in radius. According to the literature, such types of plasma sources are classified as microplasmas. The studies include spectrally resolved optical measurements, tomographic reconstructions and numerical simulations using the collisional–radiative model for an Ar–Hg plasma. We discuss the problems of theoretical modelling and experimental diagnostics of microplasma sources. It is shown that the conventional collisional–radiative model, based on the assumption that transportation of atoms in the highly excited states can be neglected, has limitations in modelling a capillary discharge in an Ar–Hg mixture. It is found that diffusion of highly excited mercury atoms to the wall influences the emission properties of the capillary discharge. We have concluded that applications of the emission tomography method to microplasmas require a special analysis in each particular case.

Hyperspectral image reconstruction for diffuse optical tomography

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.

Optical coherence tomography by using frequency measurements in wavelength domain

Optical coherence tomography (OCT) reconstruction by using frequency measurements in the wavelength domain is presented in this paper. The method directly recovers the axial scan by formulating the frequency domain OCT (FD-OCT) into an algebraic reconstruction problem. In this way, the need for interpolation is removed. Then by solving the problem with ℓ1 optimization, the computational load is significantly reduced. It is demonstrated by experiment and simulation that the proposed method can achieve high resolution and longer imaging depth compared to the FD-OCT method.

GPU-Accelerated Finite Element Method for Modelling Light Transport in Diffuse Optical Tomography

We introduce a GPU-accelerated finite element forward solver for the computation of light transport in scattering media. The forward model is the computationally most expensive component of iterative methods for image reconstruction in diffuse optical tomography, and performance optimisation of the forward solver is therefore crucial for improving the efficiency of the solution of the inverse problem. The GPU forward solver uses a CUDA implementation that evaluates on the graphics hardware the sparse linear system arising in the finite element formulation of the diffusion equation. We present solutions for both time-domain and frequency-domain problems. A comparison with a CPU-based implementation shows significant performance gains of the graphics accelerated solution, with improvements of approximately a factor of 10 for double-precision computations, and factors beyond 20 for single-precision computations. The gains are also shown to be dependent on the mesh complexity, where the largest gains are achieved for high mesh resolutions.

Accelerated gradient based diffuse optical tomographic image reconstruction

Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data.

3-Dimensional Optical Coherence Tomography Assessment of Jailed Side Branches by Bioresorbable Vascular Scaffolds: A Proposal for Classification

The purpose of this study is to assess jailing of side branches (SB) by the everolimus-eluting, bioresorbable vascular scaffold (BVS) with 3-dimensional (3D) optical coherence tomography (OCT) reconstruction. Because BVS struts at the SB orifice are suspected of being bioresorbed and/or forming a neointimal bridge, OCT has been used to evaluate the struts in detail at that particular site. Our understanding of the 3D relationship of the strut and the SB orifice is limited by the use of 2-dimensional OCT images. Fourier-domain OCT enables reliable 3D reconstruction of coronary vessels. The ABSORB Cohort B (A Clinical Evaluation of the Bioabsorbable Everolimus Eluting Coronary Stent System in the Treatment of Patients With de Novo Native Coronary Artery Lesions) trial is a multicenter single-arm trial to assess the safety and performance of the BVS. Fourier-domain OCT pullbacks (C7-XR system, LightLab Imaging Inc., Westford, Massachusetts) are obtained at pullback speed of 20 mm/s and 3D renderings are performed. The orifices of the SB are assessed visually. The area of SB orifice and the number of strut-free compartments delineated by the BVS struts are evaluated. Fifty-one OCT pullbacks were acquired: 33 pullbacks were imaged with Fourier-domain OCT and 27 treated segments had 46 side branches. Three-dimensional assessment was feasible in 87% (40 of 46) of pullbacks. The mean area of the SB orifice was 1.16 +/- 1.02 mm(2). The mean number of strut-free compartments was 2.0 +/- 1.1. The classification of the overhanging struts is proposed. This study demonstrates that 3D OCT reconstruction is feasible to evaluate the orifices of SB jailed with BVS. (ABSORB Clinical Investigation, Cohort B; NCT00856856).

Effective optode configuration for the image reconstruction in diffuse optical tomography

Abstract The influence of the configuration of the optodes on the images of diffuse optical tomography (DOT) was investigated using 3D numerical simulations. 3D distributions of the absorption coefficients in a spherical object were reconstructed from the numerically simulated measurement data for various configurations of the optodes. When the optodes were placed in a plane containing a target which strongly absorbs light, the target could be reconstructed with good localization. For good reconstruction of the target, it was found to be very important that the optode configuration was optimized in order to detect light propagating through the target effectively. The simulations also showed that the optode configuration affects the quality of the reconstructed images and that some prior information about the measured object improved the DOT images. Finally the simulation results were verified by a phantom experiment.

Corrections to linear methods for diffuse optical tomography using approximation error modelling.

Linear reconstruction methods in diffuse optical tomography have been found to produce reasonable good images in cases in which the variation in optical properties within the medium is relatively small and a reference measurement with known background optical properties is available. In this paper we examine the correction of errors when using a first order Born approximation with an infinite space Green’s function model as the basis for linear reconstruction in diffuse optical tomography, when real data is generated on a finite domain with possibly unknown background optical properties. We consider the relationship between conventional reference measurement correction and approximation error modelling in reconstruction. It is shown that, using the approximation error modelling, linear reconstruction method can be used to produce good quality images also in situations in which the background optical properties are not known and a reference is not available.

Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography

We analyze a method to extract additional depth resolved spectroscopic information from frequency domain optical coherence tomography (FDOCT) data. The reconstruction of depth resolved spectra is obtained by a Fourier transform of the individual peaks in the complex FDOCT depth profiles. We demonstrate a validation of this concept with theoretical simulations and with accurate experimental studies on a multilayer sample with four different characteristic absorbers. The spatially resolved spectroscopic patterns of all individual sample layers are calculated from the depth resolved reconstructed spectra. With an additional pattern recognition algorithm, these reconstructed patterns are compared automatically to the spectral characteristics of the expected substances. This provides an allocation of the reconstructed spectra to the substances with high reliability. Thus, we present an automated substance identification directly from conventional FDOCT data, which increases significantly the information content of the image.

Combined domain-decomposition and matrix-decomposition scheme for large-scale diffuse optical tomography

Image reconstruction in diffuse optical tomography (DOT) is, in general, posed as a model-based, nonlinear optimization problem, which requires repeated use of the three-dimensional (3D) forward and inverse solvers. To cope with the computation and storage problem for some applications, such as breast tumor diagnosis, it is preferable to develop a subdomain-based parallel computation scheme. In this study, we propose a two-level image reconstruction scheme for 3D DOT, which combines the Schwarz-type domain-decomposition (DD)-based forward calculation and the matrix-decomposition (MD)-based inversion. In the forward calculation, the solution to the diffusion equation is initially obtained using a whole-domain finite difference method at a coarse grid, and then updated with a parallel DD scheme at a fine grid. The inversion procedure starts with the wavelet-decomposition-based reconstruction at a coarse grid, and then follows with a Levenberg-Marquardt least-squares solution at a fine grid, where an MD strategy is adopted for the relevant linear inversion. It is demonstrated that the combination of the DD-based forward solver and MD-based inversion allows for coarse-grain parallel implementation of both the forward and inverse issues and effectively reduces computation and storage loads for the large-scale problem. Also, both numerical simulations and phantom experiments show that MD-based linear inversion is superior to the row-fashioned algebraic reconstruction technique.

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.

Abstract LB-381: A multi-modal imaging technique to identify metabolites in hypoxic regions of a human breast cancer model

Abstract Tumor hypoxia triggers signaling cascades that significantly impact on biological outcomes, resulting in resistance to radio- and chemotherapy. Therefore, understanding the hypoxic response of tumors is critical. In this study, we investigated links between hypoxia and different metabolites, such as lactate/lipid and total choline (tCho), in a human breast cancer model by combining in vivo magnetic resonance spectroscopic imaging (MRSI) with ex vivo optical imaging. Human MDA-MB-231-HRE-Tdtomato breast cancer cells, which were genetically engineered to express red fluorescent Tdtomato protein under the transcriptional control of hypoxia response elements (HREs) under hypoxic conditions, were orthotopically grown in female athymic nude mice. Both 3-dimensional (3D) water-unsuppressed chemical shift imaging (CSI) to determine tumor shape, and water-suppressed 3D CSI to detect metabolites, was performed. Each tumor was removed and sectioned to obtain serial slices throughout the tumor. Before sectioning, the tumor was embedded in a gelatine block containing straight fiducial marker lines for 3D reconstruction of serial optical images. Bright-field and Tdtomato fluorescence images of the same field of view were acquired from each tumor slice on a microscope to visualize hypoxia. We performed 3D reconstruction and registration of MRSI and optical images of 4 tumors. All metabolites were 3D-reconstructed from MRSI data with a 0.4 ppm spectral window size. Bright-field images from each serial section were aligned to each other by rigid body transformation based on the locations of fiducial markers. The tumor's edges were then detected and interpolated to reconstruct the 3D shape of the tumor. Accordingly, the 3D hypoxic region in the tumor was also reconstructed given the inherent co-registration of bright-field and fluorescence imaging. Then, the 3D tumor shape and the corresponding 3D hypoxic region were registered to 3D MRSI images of water-unsuppressed signal along with 3D metabolite images. Regions where hypoxia overlaps with a given metabolite were measured. tCho, glutamate/glutamine, lipid/lactate CH3, lipid CH2 and myo-inositol were detected by MRSI. The overlapping region between those metabolites and hypoxia was 35.1%, 12.7%, 9.8%, 16.7%, and 23.7% of the hypoxic region, respectively. A large proportion of high tCho-containing and myo-inositol-containing regions colocalized with the Tdtomato fluorescence in hypoxic regions, indicating that hypoxia can up-regulate tCho and myo-inositol levels in this breast tumor model. Hypoxia-driven up-regulation of choline kinase and tCho was previously shown by us in a prostate tumor model. Combining 3D MRSI and optical imaging proved useful to delineate the effects of tumor hypoxia on MRS-detectable metabolites, some of which may, in the future, serve as surrogate markers for hypoxia. This work was supported by NIH R01 CA134695. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-381.

Fast image reconstruction in fluoresence optical tomography using data compression

We present a method for fast reconstruction in fluorescence optical tomography with very large data sets. In recent reports, CCD cameras at multiple positions have been used to collect optical measurements, producing more than 10(7) data samples. This makes storage of the full system Jacobian infeasible, and so data are usually subsampled. The method reported here allows use of the full data set, via image compression methods, and explicit construction of the (small) Jacobian, meaning optimal inversion methods can be applied, and thus leading to very fast reconstruction.

Accelerating frequency-domain diffuse optical tomographic image reconstruction using graphics processing units

Diffuse optical tomographic image reconstruction uses advanced numerical models that are computationally costly to be implemented in the real time. The graphics processing units (GPUs) offer desktop massive parallelization that can accelerate these computations. An open-source GPU-accelerated linear algebra library package is used to compute the most intensive matrix-matrix calculations and matrix decompositions that are used in solving the system of linear equations. These open-source functions were integrated into the existing frequency-domain diffuse optical image reconstruction algorithms to evaluate the acceleration capability of the GPUs (NVIDIA Tesla C 1060) with increasing reconstruction problem sizes. These studies indicate that single precision computations are sufficient for diffuse optical tomographic image reconstruction. The acceleration per iteration can be up to 40, using GPUs compared to traditional CPUs in case of three-dimensional reconstruction, where the reconstruction problem is more underdetermined, making the GPUs more attractive in the clinical settings. The current limitation of these GPUs in the available onboard memory (4 GB) that restricts the reconstruction of a large set of optical parameters, more than 13,377.

High-resolution optical functional mapping of the human somatosensory cortex

Non-invasive optical imaging of brain function has been promoted in a number of fields in which functional magnetic resonance imaging (fMRI) is limited due to constraints induced by the scanning environment. Beyond physiological and psychological research, bedside monitoring and neurorehabilitation may be relevant clinical applications that are yet little explored. A major obstacle to advocate the tool in clinical research is insufficient spatial resolution. Based on a multi-distance high-density optical imaging setup, we here demonstrate a dramatic increase in sensitivity of the method. We show that optical imaging allows for the differentiation between activations of single finger representations in the primary somatosensory cortex (SI). Methodologically our findings confirm results in a pioneering study by Zeff et al. (2007) and extend them to the homuncular organization of SI. After performing a motor task, eight subjects underwent vibrotactile stimulation of the little finger and the thumb. We used a high-density diffuse-optical sensing array in conjunction with optical tomographic reconstruction. Optical imaging disclosed three discrete activation foci one for motor and two discrete foci for vibrotactile stimulation of the first and fifth finger, respectively. The results were co-registered to the individual anatomical brain anatomy (MRI) which confirmed the localization in the expected cortical gyri in four subjects. This advance in spatial resolution opens new perspectives to apply optical imaging in the research on plasticity notably in patients undergoing neurorehabilitation.

Full 3D displacement field measurement by Optical Scanning Tomography and PIV Tomography

This work is about the development of new techniques for the measurement of the three components of the velocity inside a volume. A comparison of two techniques, optical scanning tomography and algebraic reconstruction tomography method, is performed with 3D displacement tests of transparent solid blocks.

Fast automatic segmentation of anatomical structures in x-ray computed tomography images to improve fluorescence molecular tomography reconstruction

The recent development of hybrid imaging scanners that integrate fluorescence molecular tomography (FMT) and x-ray computed tomography (XCT) allows the utilization of x-ray information as image priors for improving optical tomography reconstruction. To fully capitalize on this capacity, we consider a framework for the automatic and fast detection of different anatomic structures in murine XCT images. To accurately differentiate between different structures such as bone, lung, and heart, a combination of image processing steps including thresholding, seed growing, and signal detection are found to offer optimal segmentation performance. The algorithm and its utilization in an inverse FMT scheme that uses priors is demonstrated on mouse images.