Near Infrared (NIR) and Photoacoustic (PA) Imaging are promising imaging modalities that provides functional information of the soft biological tissues in-vivo, with applica-tions in breast and brain tissue imaging. These techniques use near infrared light in the wavelength range of (600 nm - 900 nm), giving an advantage of being non-ionizing imaging modality. This makes the prolong bed-side monitoring of tissue feasible, making them highly desirable medical imaging modalities in the clinic. The computation models that are deployed in these imaging scenarios are computationally demanding and often require a high performance computing systems to deploy them in real-time. This the-sis presents three computationally e cient models for near-infrared and photoacoustic imaging, without compromising the quality of measured functional properties, to make them more appealing in clinical scenarios. The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modi ed the BeerLambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concen-trations for NIR spectroscopic data analysis. Even though MBLL is e ective in terms of providing qualitative comparison, it su ers from its applicability across tissue types and tissue dimensions. A Lambert-W function-based modeling for light propagation in biological tissues is proposed and introduced, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coe cients o and . The model is validated against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. Numerous human and animal tissues are included to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is rst of its kind in providing accurate modeling of NIR light propagation in biological tissues. Model based image reconstruction techniques yield better quantitative accuracy in photoacoustic (PA) image reconstruction, especially in limited data cases. An exponen-tial ltering of singular values is proposed for carrying out the image reconstruction in photoacoustic tomography. The results were compared with widely popular Tikhonov regularization, time reversal, and the state of the art least-squares QR based reconstruc-tion algorithms for three digital phantom cases with varying signal-to-noise ratios of data. The exponential ltering provided superior photoacoustic images of better quanti-tative accuracy. Moreover, the proposed ltering approach was observed to be less biased towards regularization parameter and did not come with any additional computational burden as it was implemented within the Tikhonov ltering framework. It was also shown that the standard Tikhonov ltering becomes an approximation to the proposed exponential ltering. The model based image reconstruction techniques for photoacoustic tomography re-quire an explicit regularization. An error estimate minimization based approach was proposed and developed for the determination of regularization parameter for PA imag-ing. The regularization was used within Lanczos bidiagonalization framework, which provides the advantage of dimensionality reduction for a large system of equations. The proposed method was computationally faster than the state of the art techniques and provided similar performance in terms of quantitative accuracy in reconstructed im-ages.The estimate can also be utilized in determining suitable regularization parameter for other popular techniques such as Tikhonov,exponential ltering and `1 norm based regularization methods.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.