Diffuse Optical Tomography is a diagnostic imaging modality where optical parameters such as absorption coefficient, scattering coefficient and refractive index distributions are recovered to form the internal tissue metabolic image. Near-infrared (NIR) light has the potential to be used as a noninvasive means of diagnostic imaging within the human breast. Due to the diffusive nature of light in tissue, computational model-based methods are required for functional imaging. The main goal is to recover the spatial variation of optical properties which shed light on the different metabolic states of tissue and tissue like media. This thesis addresses the issue of quantitative recovery of optical properties of tissue-mimicking phantom and pork tissue using diffuse optical tomography (DOT). The main contribution of the present work is the development of robust, efficient and fast optical property reconstruction algorithms for a direct 3-D DOT imaging system. There are both theoretical and experimental contributions towards the development of an imaging system and procedures to minimize accurate data collection time, overall system automation as well as development of computational algorithms. In nurturing the idea of imaging using NIR light into a fully developed direct 3-D imaging system, challenges from the theoretical and computational aspects have to be met. The recovery of the optical property distribution in the interior of the object from the often noisy boundary measurements on light, is an ill-posed ( and nonlinear) problem. This is particularly true, when one is interested in a direct 3-D image reconstruction instead of the often employed stacking of 2-D cross-sections obtained from solving a set of 2-D DOT problems. In order to render the DOT, a useful diagnostic imaging tool and a robust reconstruction procedure giving accurate and reliable parameter recovery in the scenario, where the number of unknowns far outnumbers the number of independent data sets that can be gathered (for example, the direct 3-D recovery mentioned earlier) is essential. Here, the inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. An interesting development in this direction has been the development of Broyden’ s and adjoint Broyden’ s methods that avoids direct Jacobian computation in each iteration thereby making the full 3-D a reality. Conventional model based iterative image reconstruction (MoBIIR) algorithm uses Newton’ s and it’s variant methods, where it required repeated evaluation of whole Jacobian, which consumes bulk time in reconstruction process. The explicit secant and adjoint information based fast 2-D/3-D image reconstruction algorithms without repeated evaluation of the Jacobian is proposed in diffuse optical tomography, where the computational time has been decreased many folds by updating the Jacobian successively through low rank update. An alternative route to the iterative solution is attempted by introducing an artificial dynamics in the system and treating the steady-state response of the artificially evolving dynamical system as a solution. The objective is to consider a novel family of pseudo-dynamical 2-D and 3-D systems whose numerical integration in time provides an asymptotic solution to the inverse problem at hand. We convert Gauss-Newton’ s equation for updates into a pseudo-dynamical (PD) form by explicitly adding a time derivative term. As the pseudo-time integration schemes do not need such explicit matrix inversion and depending on the pseudo-time step size, provides for a layer of regularization that in turn helps in superior quality of 2-D and 3-D image reconstruction. A cost effective frequency domain Matlab based 2-D/3-D automated imaging system is designed and built. The complete instrumentation (including PC-based control software) has been developed using a single modulated laser source (wavelength 830nm) and a photo-multiplier tube (PMT). The source and detector fiber change their positions dynamically allowing us to gather data at multiple source and detector locations. The fiber positions are adjusted on the phantom surface automatically for scanning variable size phantoms. A heterodyning scheme was used for reading out the measurement using a lock-in-amplifier. The Matlab program carries out sequence of actions such as instrument control, data acquisition, data organization, data calibration and reconstruction of image. The Gauss-Newton’ s, Broyden’ s, adjoint Broyden’ s and pseudo-time integration algorithms are evaluated using the simulation data as well as data from the experimental DOT system. Validation of the system and the reconstruction algorithms were carried out on a real tissue, a pork tissue with an embedded fat inhomogeneity. The results were found to match the known parameters closely.

<p>Small animal imaging is a valuable tool in preclinical biomedical research which relies on the use of animal models to understand human disease. Newly emerging optical imaging techniques such as bioluminescence tomography offer an inexpensive and sensitive alternative to more established imaging technologies. These techniques are capable of non-invasively imaging a variety of cellular and molecular processes <em>in vivo</em>. As an emerging technology, current bioluminescence imaging methods suffer from several limitations, preventing them from reaching their full potential.</p> <p>In this work, we describe the design and characterization of an integrated imaging system capable of multispectral bioluminescence tomography (BLT), diffuse optical tomography (DOT), and X-ray computed tomography (CT). The system addresses many of the inherent problems encountered in planar bioluminescence imaging techniques, allowing for the recovery of more accurate and quantitative bioluminescence data. The integrated X-ray CT scanner provides anatomical information which aids in the visualization and localization of the recovered bioluminescence distributions and also helps to constrain the inverse reconstruction in the diffuse optical tomography system. It was found that the inclusion of spatial priors from X-ray CT improved the reconstructed image quality dramatically. Four image reconstruction algorithms were evaluated for their ability to recover the effective attenuation coefficients of a series of test phantoms. Two of the algorithms (a modified Levenberg-Marquardt method, and a single-step Tikhonov method) did not use any <em>a priori</em> spatial information. Two other algorithms (hard priors and soft priors) used <em>a priori </em>structural information from X-ray CT to constrain the reconstruction process. The two methods incorporating spatial prior information resulted in recovered optical property distributions with RMS errors ranging from 8 % to 15 % in a series of test phantoms versus errors of 11 % to 26 % for non-spatial methods. The soft priors method was shown to be more resilient to imperfect <em>a priori</em> information.</p> <p>The multispectral BLT component was used to recover accurate bioluminescence distributions in test phantoms using <em>a priori</em> background optical properties recovered from the DOT system. Multispectral measurements were shown to provide an accurate method for estimating the position of a bioluminescence source due to the wavelength dependent attenuation of tissue. Experimental measurements are presented which explore the importance of accurate estimates of background optical properties in BLT. The hard spatial prior method was found to provide the best overall recovery of total source strength, position, and fidelity at all source depths up to 12.5 mm. The total source strength was recovered to within 8 %, while the source position was recovered to within 0.16 mm in all cases. Errors in recovered power and position showed no dependence on depth up to the maximum of 12.5 mm.</p> / Doctor of Philosophy (PhD)

No description available.

La tomographie optique diffuse (TOD) est une modalité d’imagerie biomédicale 3D peu dispendieuse et non-invasive qui permet de reconstruire les propriétés optiques d’un tissu biologique. Le processus de reconstruction d’images en TOD est difficile à réaliser puisqu’il nécessite de résoudre un problème non-linéaire et mal posé. Les propriétés optiques sont calculées à partir des mesures de surface du milieu à l’étude. Dans ce projet, deux méthodes de reconstruction non-linéaire pour la TOD ont été développées. La première méthode utilise un modèle itératif, une approche encore en développement qu’on retrouve dans la littérature. L’approximation de la diffusion est le modèle utilisé pour résoudre le problème direct. Par ailleurs, la reconstruction d’image à été réalisée dans différents régimes, continu et temporel, avec des mesures intrinsèques et de fluorescence. Dans un premier temps, un algorithme de reconstruction en régime continu et utilisant des mesures multispectrales est développé pour reconstruire la concentration des chromophores qui se trouve dans différents types de tissus. Dans un second temps, un algorithme de reconstruction est développé pour calculer le temps de vie de différents marqueurs fluorescents à partir de mesures optiques dans le domaine temporel. Une approche innovatrice a été d’utiliser la totalité de l’information du signal temporel dans le but d’améliorer la reconstruction d’image. Par ailleurs, cet algorithme permettrait de distinguer plus de trois temps de vie, ce qui n’a pas encore été démontré en imagerie de fluorescence. La deuxième méthode qui a été développée utilise l’apprentissage machine et plus spécifiquement l’apprentissage profond. Un modèle d’apprentissage profond génératif est mis en place pour reconstruire la distribution de sources d’émissions de fluorescence à partir de mesures en régime continu. Il s’agit de la première utilisation d’un algorithme d’apprentissage profond appliqué à la reconstruction d’images en TOD de fluorescence. La validation de la méthode est réalisée avec une mire aux propriétés optiques connues dans laquelle sont inséres des marqueurs fluorescents. La robustesse de cette méthode est démontrée même dans les situations où le nombre de mesures est limité et en présence de bruit. / Abstract : Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult problem, because the inversion process is nonlinear and ill-posed. During DOT image reconstruction, the optical properties of the medium are recovered from the boundary measurements at the surface of the medium. In this work, two approaches are proposed for non-linear DOT image reconstruction. The first approach relies on the use of iterative model-based image reconstruction, which is still under development for DOT and that can be found in the literature. A 3D forward model is developed based on the diffusion equation, which is an approximation of the radiative transfer equation. The forward model developed can simulate light propagation in complex geometries. Additionally, the forward model is developed to deal with different types of optical data such as continuous-wave (CW) and time-domain (TD) data for both intrinsic and fluorescence signals. First, a multispectral image reconstruction algorithm is developed to reconstruct the concentration of different tissue chromophores simultaneously from a set of CW measurements at different wavelengths. A second image reconstruction algorithm is developed to reconstruct the fluorescence lifetime (FLT) of different fluorescent markers from time-domain fluorescence measurements. In this algorithm, all the information contained in full temporal curves is used along with an acceleration technique to render the algorithm of practical use. Moreover, the proposed algorithm has the potential of being able to distinguish more than 3 FLTs, which is a first in fluorescence imaging. The second approach is based on machine learning techniques, in particular deep learning models. A deep generative model is proposed to reconstruct the fluorescence distribution map from CW fluorescence measurements. It is the first time that such a model is applied for fluorescence DOT image reconstruction. The performance of the proposed algorithm is validated with an optical phantom and a fluorescent marker. The proposed algorithm recovers the fluorescence distribution even from very noisy and sparse measurements, which is a big limitation in fluorescence DOT imaging.

Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro- vides functional information of the soft biological tissues in-vivo, such as breast and brain tissues. The near infrared (NIR) light (600-1000 nm) is the interrogating radiation, which is typically delivered and collected using fiber bundles placed on the boundary of the tissue. The internal optical property distribution is estimated via model-based image reconstruction algorithm using these limited boundary measurements. Image reconstruction problem in DOT is known to be non-linear, ill-posed, and some times under-determined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of the regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of non-quadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases were obtained automatically using the generalized cross-validation (GCV) method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that while quadratic penalty may be able to provide better separation between two closely spaced targets, it's contrast recovery capability is limited and the sparseness promoting penalties, such as `1, Cauchy, Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets with Geman-McClure penalty being the most optimal one. Effective usage of image guidance by incorporating the refractive index (RI) variation in computational modeling of light propagation in tissue is investigated to assess its impact on optical-property estimation. With the aid of realistic patient breast three-dimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in image-guided diffuse optical tomography (IG-DOT) using numerical simulations. It is also shown that by assuming identical RI for all regions of tissue would lead to erroneous estimation of optical properties. The a priori knowledge of the RI for the segmented regions of tissue in IG-DOT, which is difficult to obtain for the in vivo cases, leads to more accurate estimates of optical properties. Even inclusion of approximated RI values, obtained from the literature, for the regions of tissue resulted in better estimates of optical properties, with values comparable to that of having the correct knowledge of RI for different regions of tissue. Image reconstruction in IG-DOT procedure involves reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem to be well-determined compared to traditional under- determined case. Still, the methods that are deployed in this case are same as the one used for traditional diffuse optical image reconstruction, which involves regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method was proposed here to perform the image reconstruction procedure and shown to be providing solutions that are closely matching with ones obtained using established methods. The proposed method also has the distinctive advantage of being more efficient due to being regularization free, involving only repeated forward calculations.

In many medical imaging modalities, as photons travel from the emission source to the detector, they are scattered by the biological tissue. Often this scatter is viewed as a phenomenon that degrades image quality, and most research is focused on designing methods for either discarding the scattered photons or correcting for scatter. However, the scattered photons also carry information about the tissue that they pass through, which can perhaps be extracted. In this research, we investigate methods to retrieve information from the scattered photons in two specific medical imaging modalities: diffuse optical tomography (DOT) and single photon emission computed tomography (SPECT). To model the scattering of photons in biological tissue, we investigate using the Neumann-series form of the radiative transport equation (RTE). Since the scattering phenomenon are different in DOT and SPECT, the models are individually designed for each modality. In the DOT study, we use the developed photon-propagation model to investigate signal detectability in tissue. To study this detectability, we demonstrate the application of a surrogate figure of merit, based on Fisher information, which approximates the Bayesian ideal observer performance. In the SPECT study, our aim is to determine if only the SPECT emission data acquired in list-mode (LM) format, including the scattered-photon data, can be used to compute the tissue-attenuation map. We first propose a path-based formalism to process scattered photon data, and follow it with deriving expressions for the Fisher information that help determine the information content of LM data. We then derive a maximum-likelihood expectation-maximization algorithm that can jointly reconstruct the activity and attenuation map using LM SPECT emission data. While the DOT study can provide a boost in transition of DOT to clinical imaging, the SPECT study will provide insights on whether it is worth exposing the patient to extra X-ray radiation dose in order to obtain an attenuation map. Finally, although the RTE can be used to model light propagation in tissues, it is computationally intensive and therefore time consuming. To increase the speed of computation in the DOT study, we develop software to implement the RTE on parallel computing architectures, specifically the NVIDIA graphics processing units (GPUs).

La tomographie optique diffuse de fluorescence permet la reconstruction tridimensionnelle de fluorophores présents dans un tissu biologique. La modalité la plus simple de cette technique repose sur une illumination continue du milieu et s'intéresse aux mesures d'atténuation du faisceau incident en différentes positions. En raison de la forte diffusion des tissus, la modalité continue souffre d'une faible résolution en profondeur.On considère aujourd'hui que la modalité résolue en temps, qui fournit pour chaque photon détecté son temps de vol, permettrait l'étude de tissus plus épais, ouvrant ainsi la porte à des applications cliniques. L'objet de cette thèse est de chercher comment tirer profit de l'information temporelle et de quantifier son apport par rapport à la modalité continue.La tomographie optique diffuse de fluorescence est un problème inverse mal conditionné. Dans un contexte où tout écart au modèle doit être limité, nous nous intéressons tout d'abord au modèle direct et montrons que la densité de photons est un modèle satisfaisant de la quantité expérimentalement mesurée. Nous passons ensuite au crible la méthode de reconstruction fondée sur l'exploitation des moments temporels des mesures. Étudiant théoriquement les propriétés des moments, nous montrons que cette approche nécessite, pour s'avérer intéressante, la détection d'un nombre élevé de photons. Nous introduisons enfin une nouvelle approche permettant d'exploiter l'information temporelle pour un nombre de photons plus limité. Cette approche, reposant sur une transformation en ondelettes des mesures, offre une qualité de reconstruction accrue par rapport à celle offerte par l'approche des moments. / Fluorescence diffuse optical tomography enables the three-dimensional reconstruction of fluorescence markers injected within a biological tissue, with light in the near infrared range. The simple continuous modality uses steady excitation light and operates from the measurements at different positions of the attenuation of the incident beam. This technique is low-cost, non-ionizing, and easy to handle, but subject to low resolution for thick tissues due to diffusion. Hopefully, the time-resolved modality, which provides the time of flight of any detected photon, could overcome this limitation and pave the way to clinical applications. This thesis aims at determining the best way to exploit the time resolved information and at quantifying the advantages of this modality over the standard continuous wave one.Model deviations must be carefully limited when ill-posed problems as fluorescence diffuse optical tomography are considered. As a result, we have first addressed the modelling part of the problem. We have shown that the photons density models to good approximation the measurable quantity that is the quantity measured by an actual acquisition set-up. Then, the moment-based reconstruction scheme has been thoroughly evaluated by means of a theoretical analysis of the moments’ properties. It was found that the moment-based approach requires high photon counts to be profitable compared to the continuous wave modality. Last, a novel wavelet-based approach, which enables an improved reconstruction quality, has been introduced. This approach has shown good ability to exploit the temporal information at lower photon counts.

La tomographie optique diffuse (TOD) est une technique d'imagerie médicale émergente utilisant la lumière proche infrarouge pour sonder les tissus biologiques. A partir de mesures non-invasives, cette technique permet d'obtenir les cartes en trois dimensions des coefficients d'absorption et de diffusion à l'intérieur des organes. Avec une approche multi-spectrale, la distribution spatiale des chromophores endogènes (hémoglobine, eau) peut aussi être obtenue. Pour certaines applications cliniques, il est souhaitable d'effectuer les mesures de TOD avec une sonde compacte qui regroupe tous les couples source-détecteur. Cependant, dans cette configuration, la sensibilité en profondeur est un défi majeur. Dans le cadre de cette thèse, nous proposons d'adresser ce challenge en utilisant des mesures résolues en temps. Une approche résolue en temps est développée pour optimiser la TOD dans le cas des mesures de réflectance à faibles distances source-détecteur. Cette approche inclut des aspects méthodologiques concernant le traitement des mesures résolues en temps par des algorithmes de TOD basés sur la transformée de Mellin-Laplace. Cette approche comporte aussi un volet instrumental qui consiste à optimiser la chaîne de détection sur deux points précis pour améliorer la détection et la localisation de contraste d'absorption en profondeur dans les milieux diffusants. Tout d'abord, l'impact de la réponse temporelle du détecteur est étudié avec des détecteurs de photons uniques disponibles dans le commerce (photomultiplicateurs classiques et hybrides). Dans un second temps, l'augmentation de la profondeur sondée avec de nouveaux détecteurs de photons uniques, les fast-gated single-photon avalanche diodes, est explorée au cours d'une collaboration avec le Politecnico de Milan. Pour finir, une étude illustre les performances de l'approche proposée en termes de résolution spatiale en profondeur pour différents arrangements des sources et détecteurs dans une sonde optique. Des sondes optiques dont la largeur est limitée à quelques centimètres ouvrent la voie à de nouvelles applications cliniques pour la TOD. Ces sondes peuvent accéder à des organes internes comme la prostate ou faciliter les examens médicaux sur des organes externes comme le sein ou le cerveau. / Diffuse optical tomography (DOT) is an emerging medical imaging technique using near-infrared light to probe biological tissues. This technique can retrieve three-dimensional maps of absorption and scattering coefficients inside organs from non-invasive measurements. With a multispectral approach, the spatial distribution of endogenous chromophores (hemoglobin, water) can even be obtained. For some clinical applications, it is desirable to carry out the measurements for DOT with a compact probe including all sources and detectors. However, the depth sensitivity is a real challenge in this configuration. We propose to tackle this challenge by using time-resolved measurements. A time-resolved approach is developed to perform DOT with reflectance measurements at short source-detector separation. This approach involves methodological aspects including the processing of time-resolved signals by DOT algorithms based on the Mellin-Laplace transform. Then, this approach consists in optimizing the detection chain on two aspects for enhancing the detection and localization of absorption contrast in depth in diffusive media. First, the impact of the temporal response of the detector is studied with commercially available single-photon detectors (classical and hybrid photomultipliers). Second, the enhancements in probed depth permitted with fast-gated single-photon avalanche diodes are explored in a joint work with the Politecnico di Milano. To finish, a study is carried out to illustrate the performance of the proposed approach with respect to spatial resolution in depth for different configurations of sources and detectors in the optical probe. Probes with a width limited to a few centimeters open the gate to multiple clinical interests. They could access intern organs like the prostate or facilitate the measurements on extern organs like the breast or the brain.

Medical imaging is critical for the detection and diagnosis of disease, guided biopsies, assessment of therapies, and administration of treatment. While computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultra-sound (US) are the more familiar modalities, interest in yet other modalities continues to grow. Among the motivations are reduction of cost, avoidance of ionizing radiation, and the search for new information, including biochemical and molecular processes. Fluorescence Molecular Tomography (FMT) is one such emerging technique and, like other techniques, has its advantages and limitations. FMT can reconstruct the distribution of fluorescent molecules in vivo using near-infrared radiation or visible band light to illuminate the subject. FMT is very safe since non-ionizing radiation is used, and inexpensive due to the comparatively low cost of the imaging system. This should make it particularly well suited for small animal studies for research. A broad range of cell activity can be identified by FMT, making it a potentially valuable tool for cancer screening, drug discovery and gene therapy. Since FMT imaging is scattering dominated, reconstruction of volume images is significantly more computationally intensive than for CT. For instance, to reconstruct a 32x32x32 image, a flattened matrix with approximately 10¹°, or 10 billion, elements must be dealt with in the inverse problem, while requiring more than 100 GB of memory. To reduce the error introduced by noisy measurements, significantly more measurements are needed, leading to a proportionally larger matrix. The computational complexity of reconstructing FMT images, along with inaccuracies in photon propagation models, has heretofore limited the resolution and accuracy of FMT. To surmount the problems stated above, we decompose the forward problem into a Khatri-Rao product. Inversion of this model is shown to lead to a novel reconstruction method that significantly reduces the computational complexity and memory requirements for overdetermined datasets. Compared to the well known SVD approach, this new reconstruction method decreases computation time by a factor of up to 25, while simultaneously reducing the memory requirement by up to three orders of magnitude. Using this method, we have reconstructed images up to 32x32x32. Also outlined is a two step approach which would enable imaging larger volumes. However, it remains a topic for future research. In achieving the above, the author studied the physics of FMT, developed an extensive set of original computer programs, performed COMSOL simulations on photon diffusion, and unavoidably, developed visual displays.

La Tomographie Optique Diffuse (TOD) est une nouvelle technique d'imagerie médicale permettant de reconstruire les propriétés optiques des tissus biologiques dans le but de détecter des tumeurs cancéreuses. Il s’agit, toutefois, d’un problème inverse mal-posé et sous-déterminé. Le travail de cette thèse s’articule autour de la résolution de ce problème en utilisant l’équation du transfert radiatif comme modèle de propagation de la lumière (modèle direct). L’analyse de sensibilité a montré que le facteur d’anisotropie g de la fonction de phase de Henyey-Greenstein est le paramètre le plus influant sur la sortie du modèle direct suivi du coefficient de diffusion µs puis du coefficient d’absorption µa. Dans un premier temps, un algorithme de Gauss-Newton a été implémenté en utilisant les fonctions de sensibilités. Toutefois, ce dernier ne permet d’estimer qu'un nombre très limité de paramètres optiques (supposés constants en espace). Dans un second temps, un algorithme de Quasi-Newton a été développé pour reconstruire les distributions spatiales des propriétés optiques. Le gradient de la fonction objectif a été calculé efficacement par la méthode adjointe à travers le formalisme de Lagrange avec une approche Multi-fréquences. Les reconstructions sont obtenues à partir des données simulées en surface. Le facteur g est reconstruit comme un nouvel agent de contraste en TOD. Le problème de diaphonie entre µs g a été donc mis en évidence dans cette thèse. Notre algorithme a permis de reconstruire en 2D et 3D une ou plusieurs inclusions tumorales présentant différentes formes. La qualité des images reconstruites a été examinée en fonction du nombre de fréquences, de la diaphonie, du niveau de contraste (Inclusion/Fond), du niveau de bruit et de la position des inclusions tumorales / Diffuse Optical Tomography (DOT) is a new medical imaging technique used to reconstruct the optical properties of biological tissues in order to detect cancerous tumors. However, this is an ill-posed and under-determined inverse problem. The work of this thesis deals with the resolution of this problem using the radiative transfer equation as a forward model of light propagation. The sensitivity analysis showed that the anisotropy factor g of the Henyey-Greenstein phase function is the most sensitive parameter of the forward model followed by the scattering coefficient µs and then the absorption coefficient µa. In a first step, a Gauss-Newton algorithm was implemented using the sensitivity functions. However, this algorithm allows to estimate a very limited number of the optical parameters (assumed to be constant in space). In a second step, a Quasi-Newton algorithm was developed to reconstruct the spatial distributions of the optical properties. The gradient of the objective function was efficiently computed by the adjoint method through the Lagrangian formalism with a Multi-frequency approach. The reconstructed images were obtained from simulated boundary data. The g factor was reconstructed as a new optical contrast agent in DOT and the crosstalk problem between this factor and µs has been studied. The results showed that the algorithm is efficient to reconstruct in 2D and 3D one or several tumor inclusions having different shapes. The quality of the reconstructed images was examined according to several parameters: the number of frequencies, the crosstalk, the contrast and the noise levels