Reconstruction of DOT images requires inverting the forward mapping $J$. This is an under-determined and ill-posed problem, since countless distributions of $\mu a$ within the volume can explain the same surface measurement. Moreover, near-infrared light can pass skin and bone, but is highly attenuated with increasing depth, causing $J$ to be ill conditioned (or even singular), and the solution to the corresponding linear system to be prone to numerical instabilities. With a penetration depth of 3 to 4 cm, light can reach the cortex, but there is a vast sensitivity loss in the depth. This leads to a sparse matrix with very low sensitivity values in the largest fraction of the volume. Furthermore, small changes in optical properties at this depth have to be recovered from boundary measurements with nearby nodes that have a high sensitivity to superficial signals and are, therefore, sensitive to noise. Due to the ill-posed nature of the DOT inverse problem, a unique solution can only be obtained if the constraints are imposed on the distribution of the absorption coefficients. Moreover, since $J$ is ill-conditioned, a solution has to be found that optimally suppresses noise while still explaining the data. Many studies using DOT either add an additional regularization term to the model or eliminate the singular values smaller than a defined threshold from $J$. Both methods overcome the problem of very small singular values of $J$ causing amplification of noise upon inversion. However, the choice of a regularization parameter (either the number of singular values maintained or the relative weight of the regularization term in the cost function) is often made ad hoc^{8}^{,}^{17}^{–}^{19} and lacks objective criteria. For researchers, it may be challenging to find an appropriate regularization parameter, since the measured data vary highly between the experiments, depending on the setup, imaging device, tissue properties, and noise level.