Recent techniques now allow us to study the developing fetal brain anatomy in 3D from the reconstruction of motion corrected sets of multiple 2D images. The use of iterative, as opposed to interpolation based, reconstruction to form a 3D image from the slice data can provide improved anatomical detail. However, the lack of strategy on deciding the regularization parameter has prevented us from getting the optimal result. Here we implemented the L-curve method, which took into account of the trade-off between the fit and the data error, on choosing the optimal regularization parameter. We reviewed the 3D reconstruction quality from clinically acquired data through the examination of tissue contrast and spatial resolution and compared the results against interpolation methods. In this study, we analyzed a series of T2-weighted MRI scans from the developing fetuses and focused on the main fetal brain sectors –cortical plate (CP), deep gray nuclei (DG), germinal matrix (GMAT), subplate and intermediate zone (SP+IZ), and ventricles (VENT). Through these evaluations, we were able to obtain the optimal iterative reconstructed images that work best with different numbers of image stacks, image resolution, image signal strength as well as presenting clear anatomical details within critical brain regions.