Bioluminescence tomography with CT/MRI co-registration.

We present a three-dimensional (3D) bioluminescence image reconstruction method with MRI and CT co-registration for small animal molecular imaging. The multi-spectral light intensity distribution of an optical luciferase-luciferin reporter system is measured at the tissue surface of a small animal for the purpose of 3D image reconstruction. The reporter probe distribution inside tissue is calculated with a linear matrix inversion method and a light propagation model based on the simplified spherical harmonics equations. The animal's surface geometry and anatomy is determined from co-registered CT and MR images in order to locate the reconstructed source distribution relative to the animal's anatomy. We present in vivo bioluminescence reconstruction results that demonstrate the performance of our co-registration method.

First clinical evaluation of sagittal laser optical tomography for detection of synovitis in arthritic finger joints.

OBJECTIVE: To identify classifiers in images obtained with sagittal laser optical tomography (SLOT) that can be used to distinguish between joints affected and not affected by synovitis. METHODS: 78 SLOT images of proximal interphalangeal joints II-IV from 13 patients with rheumatoid arthritis were compared with ultrasound (US) images and clinical examination (CE). SLOT images showing the spatial distribution of scattering and absorption coefficients within the joint cavity were generated. The means and standard errors for seven different classifiers (operator score and six quantitative measurements) were determined from SLOT images using CE and US as diagnostic references. For classifiers showing significant differences between affected and non-affected joints, sensitivities and specificities for various cut off parameters were obtained by receiver operating characteristic (ROC) analysis. RESULTS: For five classifiers used to characterise SLOT images the mean between affected and unaffected joints was statistically significant using US as diagnostic reference, but statistically significant for only one classifier with CE as reference. In general, high absorption and scattering coefficients in and around the joint cavity are indicative of synovitis. ROC analysis showed that the minimal absorption classifier yields the largest area under the curve (0.777; sensitivity and specificity 0.705 each) with US as diagnostic reference. CONCLUSION: Classifiers in SLOT images have been identified that show statistically significant differences between joints with and without synovitis. It is possible to classify a joint as inflamed with SLOT, without the need for a reference measurement. Furthermore, SLOT based diagnosis of synovitis agrees better with US diagnosis than CE.

Near-infrared diffuse optical tomography.

Diffuse optical tomography (DOT) is emerging as a viable new biomedical imaging modality. Using near-infrared (NIR) light, this technique probes absorption as well as scattering properties of biological tissues. First commercial instruments are now available that allow users to obtain cross-sectional and volumetric views of various body parts. Currently, the main applications are brain, breast, limb, joint, and fluorescence/bioluminescence imaging. Although the spatial resolution is limited when compared with other imaging modalities, such as magnetic resonance imaging (MRI) or X-ray computerized tomography (CT), DOT provides access to a variety of physiological parameters that otherwise are not accessible, including sub-second imaging of hemodynamics and other fast-changing processes. Furthermore, DOT can be realized in compact, portable instrumentation that allows for bedside monitoring at relatively low cost. In this paper, we present an overview of current state-of-the -art technology, including hardware and image-reconstruction algorithms, and focus on applications in brain and joint imaging. In addition, we present recent results of work on optical tomographic imaging in small animals.

Iterative reconstruction scheme for optical tomography based on the equation of radiative transfer.

We report on the development of an iterative image reconstruction scheme for optical tomography that is based on the equation of radiative transfer. Unlike the commonly applied diffusion approximation, the equation of radiative transfer accurately describes the photon propagation in turbid media without any limiting assumptions regarding the optical properties. The reconstruction scheme consists of three major parts: (1) a forward model that predicts the detector readings based on solutions of the time-independent radiative transfer equation, (2) an objective function that provides a measure of the differences between the detected and the predicted data, and (3) an updating scheme that uses the gradient of the objective function to perform a line minimization to get new guesses of the optical properties. The gradient is obtained by employing an adjoint differentiation scheme, which makes use of the structure of the finite-difference discrete-ordinate formulation of the transport forward model. Based on the new guess of the optical properties a new forward calculation is performed to get new detector predictions. The reconstruction process is completed when the minimum of the objective function is found within a defined error. To illustrate the performance of the code we present initial reconstruction results based on simulated data.

Gradient-based iterative image reconstruction scheme for time-resolved optical tomography.

Currently available tomographic image reconstruction schemes for optical tomography (OT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. Furthermore, these algorithms usually require the inversion of large, full, ill-conditioned Jacobian matrixes. In this work a gradient-based iterative image reconstruction (GIIR) method is presented that promises to overcome current limitations. The code consists of three major parts: 1) A finite-difference, time-resolved, diffusion forward model is used to predict detector readings based on the spatial distribution of optical properties; 2) An objective function that describes the difference between predicted and measured data; 3) An updating method that uses the gradient of the objective function in a line minimization scheme to provide subsequent guesses of the spatial distribution of the optical properties for the forward model. The reconstruction of these properties is completed, once a minimum of this objective function is found. After a presentation of the mathematical background, two- and three-dimensional reconstruction of simple heterogeneous media as well as the clinically relevant example of ventricular bleeding in the brain are discussed. Numerical studies suggest that intraventricular hemorrhages can be detected using the GIIR technique, even in the presence of a heterogeneous background.