In this paper, we present a wavelet-based approach to solve the reconstruction problem encountered in
diffuse optical tomography (DOT). The DOT reconstruction problem using model based iterative image
reconstruction (MoBIIR) procedure involves repeated implementation of the three steps: (i) solution to the
diffusion equation (DE) to generate the simulated data from the current optical properties, (ii) estimation of
the Jacobian for the current optical property values and (iii) inversion of the perturbation equation leading
to the update vectors for the optical properties. Consequently, there are three approaches to a wavelet
based solution to the DOT problem: (i) waveletization of the perturbation equation, (ii) application of
wavelets for computation of the Jacobian for use in the perturbation equation and (iii) the solution to the
DE in the wavelet domain. While the first of these approaches has been addressed earlier, the other two
have not been attempted to the best of our knowledge. In this work, we have attempted the second
approach, i.e., waveletization of the perturbation equation for each measurement, which requires computing
the Jacobian in the wavelet domain. Our results show that this method outperforms the earlier method.
With each measurement appropriately represented in wavelet domain, the localization and de-noising
property of wavelets are exploited. Our simulation results show that the mean-square-error at convergence
is not affected by the increase in noise in data (up to 4% additive Gaussian noise). In addition, the usual V-cycle
strategy of wavelets is attempted.