An Anatomical Method For the Calibration of Diffuse Optical Tomography |
Diffuse optical imaging is an emerging modality that usesNear Infrared (NIR) light to reveal structural and functional information ofdeep biological tissue. It provides contrast mechanisms for molecular,chemical, and anatomical imaging that is not available from other imagingmodalities. Diffuse Optical Tomography (DOT) deals with 3D reconstruction ofoptical properties of tissue given the measurements and a forward model ofphoton propagation. DOT has inherently low spatial resolution due to diffusenature of photons. In this work, we focus to improve the spatial resolution andthe quantitative accuracy of DOT by using a priori anatomical informationspecific to unknown image. Such specific a priori information can be obtainedfrom a secondary high-resolution imaging modality such as Magnetic Resonance(MR) or X-ray. Image reconstruction Is formulated within a Bayesian frameworkto determine the spatial distribution of the absorption coefficients of themedium. A spatially varying a priori probability density function is designedbased on the segmented anatomical information. Conjugate gradient method Isutilized to solve the resulting optimization problem. Proposed method isevaluated using simulation and phantom measurements collected with a noveltime- resolved optical imaging system. Results demonstrate that the proposedmethod leads to improved spatial resolution, quantitative accuracy and fasterconvergence than standard least squares approach. Diffuse optical tomography (DOT) Ls a sensitive andrelatively low cost imaging modality that reconstructs optical properties of ahighly scattering medium. However, due to the diffusive nature of lightpropagation, the problem Ls severely ill-conditioned and highly nonlinear. Eventhough nonlinear iterative methods have been commonly used, they arecomputationally expensive especially for three dimensional imaging geometry.Recently, compressed sensing theory has provided a systematic understanding ofhigh resolution reconstruction of sparse objects in many imaging problems:hence, the goal of this paper Ls to extend the theory to the diffuse opticaltomography problem. The main contributions of this paper are to formulate theimaging problem as a joint sparse recovery problem in a compressive sensingframework and to propose a novel no iterative and exact inversion algorithmthat achieves the l0 optimality as the rank of measurement increasesto the unknown sparsely level. The algorithm Ls based on the recentlydiscovered generalized MUSIC criterion, which exploits the advantages of bothcompressive sensing and array signal processing. A theoretical criterion foroptimizing the imaging geometry is provided, and simulation results confirmthat the new algorithm outperforms the existing algorithms and reliablyreconstructs the optical in homogeneities when we assume that the opticalbackground is known to a reasonable accuracy.