New emerging technologies in thermal therapy require precise monitoring and control of the delivered thermal dose in a variety of situations. The therapeutic temperature changes in target tissues range from few degrees for releasing chemotherapy drugs encapsulated in the thermosensitive liposomes to boiling temperatures in complete ablation of tumors via cell necrosis. High intensity focused ultrasound (HIFU) has emerged as a promising modality for noninvasive surgery due to its ability to create precise mechanical and thermal effects at the target without affecting surrounding tissues. An essential element in all these procedures, however, is accurate estimation of the target tissue temperature during the procedure to ensure its safety and efficacy.The advent of diagnostic imaging tools for guidance of thermal therapy was a key factor in the clinical acceptance of these minimally invasive or noninvasive methods. More recently, ultrasound and magnetic resonance (MR) thermography techniques have been proposed for guidance, monitoring, and control of noninvasive thermal therapies. MR thermography has shown acceptable sensitivity and accuracy in imaging temperature change and it is currently FDA-approved on clinical HIFU units. However, it suffers from limitations like cost of integration with ultrasound therapy system and slow rate of imaging for real time guidance. Ultrasound, on the other hand, has the advantage of real time imaging and ease of integration with the therapy system. An infinitesimal model for imaging temperature change using pulse-echo ultrasound has been demonstrated, including <italic>in vivo</italic> small-animal imaging. However, this model suffers from limitations that prevent demonstration in more clinically-relevant settings. One limitation stems from the infinitesimal nature of the model, which results in spatial inconsistencies of the estimated temperature field. Another limitation is the sensitivity to tissue motion and deformation during <italic>in vivo</italic>, which could result in significant artifacts.The first part of this thesis addresses the first limitation by introducing the Recursive Echo Strain Filter (RESF) as a new temperature reconstruction model which largely corrects for the spatial inconsistencies resulting from the infinitesimal model. The performance of this model is validated using the data collected during sub therapeutic temperature changes in the tissue mimicking phantom as well as <italic>ex vivo</italic> tissue blocks. The second part of this thesis deals with <italic>in vivo</italic> ultrasound thermography. Tissue deformations caused by natural motions (e.g. respiration, gasping, blood pulsation etc) can create non-thermal changes to the ultrasound echoes which are not accounted for in the derivation of physical model for temperature estimation. These fluctuations can create severe artifacts in the estimated temperature field. Using statistical signal processing techniques an adaptive method is presented which takes advantage of the localized and global availability of these interference patterns and use this data to enhance the estimated temperature in the region of interest. We then propose a model based technique for continuous tracking of temperature in the presence of natural motion and deformation. The method uses the direct discretization of the transient bioheat equation to derive a state space model of temperature change. This model is then used to build a linear estimator based on the Kalman filtering capable of robust estimation of temperature change in the presence of tissue motion and deformation. The robustness of the adaptive and model-based models in removing motion and deformation artifacts is demonstrated using data from <italic>in vivo</italic> experiments. Both methods are shown to provide effective cancellation of the artifacts with minimal effect on the expected temperature dynamics.