Hyperspectral image reconstruction for diffuse optical tomography

Method for Quantitative Broadband Diffuse Optical Spectroscopy of Tumor-Like Inclusions

A hybrid reflectance-based diffuse optical imaging (DOI) technique combining discrete wavelength frequency-domain (FD) near-infrared spectroscopy (NIRS) with broadband continuous wave NIRS measurements was developed to quantify the broadband optical properties of deep tumor-like inclusions. This method was developed to more accurately measure the broadband optical properties of human tumors using a compact handheld imaging probe and without requiring a priori spectral constraints. We simulated the reconstruction of absorption and scattering spectra (650–1000 nm) of human breast tumors in a homogeneous background at depths of 0 to 10 mm. The hybrid DOI technique demonstrated enhanced performance in reconstruction of optical absorption with a mean accuracy over all 71 wavelengths of 8.39% versus 32.26% for a 10 mm deep tumor with the topographic DOI method. The new hybrid technique was also tested and validated on two heterogeneous tissue-simulating phantoms with inclusion depths of 2, 7, and 9 mm. The mean optical absorption accuracy over all wavelengths was similarly improved up to 5x for the hybrid DOI method versus topographic DOI for the deepest inclusions.

Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity

Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e., a DOT image. In this paper, we consider the hyperspectral DOT (hyDOT) inverse problem as a multiple-measurement vector (MMV) problem by exploiting the joint sparsity of the images to be reconstructed. Then we propose a fast stochastic greedy algorithm based on the MMV stochastic gradient matching pursuit (MStoGradMP) and the mini-batching technique. Numerical results show that the proposed algorithm can achieve higher reconstruction accuracy with significantly reduced running time than the related gradient descent method with sparsity regularization.

Spatial Distribution of Changes in Oxidised Cytochrome C Oxidase During Visual Stimulation Using Broadband Near Infrared Spectroscopy Imaging

Functional hyperaemia, characterised as an increase in concentration of oxyhaemoglobin [HbO₂] and a decrease in concentration of deoxyhaemoglobin [HHb] in response to neuronal activity, can be precisely mapped using diffuse optical spectroscopy. However, such techniques do not directly measure changes in metabolic activity during neuronal activation. Changes in the redox state of cerebral oxidised cytochrome c oxidase Δ[oxCCO] measured by broadband spectroscopy may be a more specific marker of neuronal metabolic activity. This study aims to investigate the spatial distribution of Δ[oxCCO] responses during the activation of the visual cortex in the healthy adult human brain, and reconstruct images of these changes.

Multi-channel broadband NIRS measurements were collected from the left visual cortex of four healthy volunteers using an in-house broadband spectrometer during an inverting checkerboard visual stimulation paradigm. Δ[HbO₂], Δ[HHb] and Δ[oxCCO] were calculated by fitting the broadband spectra between 780 and 900 nm using the UCLn algorithm. Centre of gravity analysis was applied to the concentration data to determine the centres of activation for [HbO2], [HHb] and [oxCCO].

All four subjects showed similar changes in [oxCCO] in the presence of a typical visual-evoked haemodynamic response in channels overlying the visual cortex. Image reconstruction of the optical data showed a clear and spatially localized activation for all three chromophores. Centre of gravity analysis showed different localisation of the changes in each of the three chromophores across the visual cortex with the x-y coordinates of the mean centres of gravity (across 4 subjects) of HbO₂, HHb and oxCCO at (63.1 mm; 24.8 mm), (56.2 mm; 21.0 mm) and (63.7 mm; 23.8 mm), respectively.

The spatial distribution of Δ[oxCCO] response appears distinct from the haemodynamic response in the human visual cortex. Image reconstruction of Δ[oxCCO] shows considerable promise as a technique to visualise regional variation in [oxCCO] in a range of scenarios.

Dimensionality Reduction Based Optimization Algorithm for Sparse 3-D Image Reconstruction in Diffuse Optical Tomography

Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.

Scanning, non-contact, hybrid broadband diffuse optical spectroscopy and diffuse correlation spectroscopy system

A scanning system for small animal imaging using non-contact, hybrid broadband diffuse optical spectroscopy (ncDOS) and diffuse correlation spectroscopy (ncDCS) is presented. The ncDOS uses a two-dimensional spectrophotometer retrieving broadband (610-900 nm) spectral information from up to fifty-seven source-detector distances between 2 and 5 mm. The ncDCS data is simultaneously acquired from four source-detector pairs. The sample is scanned in two dimensions while tracking variations in height. The system has been validated with liquid phantoms, demonstrated in vivo on a human fingertip during an arm cuff occlusion and on a group of mice with xenoimplanted renal cell carcinoma.

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.

Reflectances from a supercontinuum laser-based instrument: hyperspectral, polarimetric and angular measurements

Recent developments of active hyperspectral systems require optical characterization of man-made materials for instrument calibration. This work presents an original supercontinuum laser-based instrument designed by Onera, The French Aerospace Lab, for fast hyperspectral polarimetric and angular reflectances measurements. The spectral range is from 480 nm to 1000 nm with a 1 nm spectral resolution. Different polarization configurations are made possible in whole spectrum. This paper reviews the design and the calibration of the instrument. Hyper-spectral polarimetric and angular reflectances are measured for reference and man-made materials such as paint coatings. Physical properties of reflectances as positivity, energy conservation and Helmholtz reciprocity are retrieved from measurements.

Model-resolution based regularization improves near infrared diffuse optical tomography

Diffuse optical tomographic imaging is known to be an ill-posed problem, and a penalty/regularization term is used in image reconstruction (inverse problem) to overcome this limitation. Two schemes that are prevalent are spatially varying (exponential) and constant (standard) regularizations/penalties. A scheme that is also spatially varying but uses the model information is introduced based on the model-resolution matrix. This scheme, along with exponential and standard regularization schemes, is evaluated objectively based on model-resolution and data-resolution matrices. This objective analysis showed that resolution characteristics are better for spatially varying penalties compared to standard regularization; and among spatially varying regularization schemes, the model-resolution based regularization fares well in providing improved data-resolution and model-resolution characteristics. The verification of the same is achieved by performing numerical experiments in reconstructing 1% noisy data involving simple two- and three-dimensional imaging domains.

A parametric level-set approach to simultaneous object identification and background reconstruction for dual-energy computed tomography

Dual-energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual-energy processing algorithm, with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown cluttered background. Physical properties of the objects, particularly the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object and the fundamental physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and, thus, can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods that define the level-set function nonparametrically, i.e., as a dense set of pixel values, we define our level set parametrically via radial basis functions and employ a Gauss-Newton-type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives an adequate reconstruction of the background.

Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been show to be particularly well suited for ill-posed inverse problems such as the one of interest here. We report on reconstructions for multiple chromophores from simulated and experimental data where the PaLS method provides a more accurate estimation of chromophore concentrations compared to a pixel-based method.