Cardiac electrical imaging, that is, reconstructing car- diac electrical activity from body surface measurements, is a technology with great potential. However, ill-posedness of this problem hinders its routine usage in clinical envi- ronment and continues to motivate the search for improve- ments on current methods. Messnarz et al. introduced an algorithm that constraints the reconstructed transmem- brane potential (TMP) to be non-decreasing over time dur- ing QRS-complex. This physiologically meaningful con- straint reduces the solution space of the problem and reg- ularizes the solution. However, this approach is compu- tationally extensive and can become prohibitive as spatial and temporal resolution of the problem increase. Here we compare three distinct options to reduce the computational load: downsampling the measurements in time, downsam- pling the measurements after filtering with an algorithm based on principal component analysis and non-linearly interpolating the potentials with a spline-based method. The data used were simulated TMPs that were forward propagated to the body surface in a densely sampled ge- ometry. The resulting body surface potential simulations were corrupted with noise and the inverse computed using a much coarser mesh to take geometry errors into account. The results indicate that reducing the dimension of the sig- nal in time does not reduce the quality of the solutions obtained, while the computational requirements decrease considerably, especially for the spline method.