Time-lapse seismic data are widely used for monitoring subsurface changes. A quantitative assessment of how reservoir properties have changed allows for better interpretation of fluid substitution and fluid migration during processes such as oil and gas production and carbon sequestration. Full-waveform inversion (FWI) has been proposed as a way to retrieve quantitative estimates of subsurface properties through seismic waveform fitting. However, for some monitoring systems, the offset range versus depth of interest is not large enough to provide information about the low-wavenumber component of the velocity model. We evaluated an image domain wavefield tomography (IDWT) method using the local warping between baseline and monitor images as the cost function. This cost function is sensitive to volumetric velocity anomalies, and it is capable of handling large velocity changes with very limited acquisition apertures, where traditional FWI fails. We described the theory and workflow of our method. Layered model examples were used to investigate the performance of the algorithm and its robustness to velocity errors and acquisition geometry perturbations. The Marmousi model was used to simulate a realistic situation in which IDWT successfully recovers time-lapse velocity changes.