Since its introduction in 2001, the Thermographic Signal Reconstruction (TSR) method has emerged as one of the most widely used methods for enhancement and analysis of thermographic sequences, with applications extending beyond industrial NDT into biomedical research, art restoration and botany. The basic TSR process, in which a noise reduced replica of each pixel time history is created, yields improvement over unprocessed image data that is sufficient for many applications. However, examination of the resulting logarithmic time derivatives of each TSR pixel replica provides significant insight into the physical mechanisms underlying the active thermography process. The deterministic and invariant properties of the derivatives have enabled the successful implementation of automated defect recognition and measurement systems. Unlike most approaches to analysis of thermography data, TSR does not depend on flawbackground contrast, so that it can also be applied to characterization and measurement of thermal properties of flaw-free samples. We present a summary of recent advances in TSR, a review of the underlying theory and examples of its implementation.