Thick-slice image acquisitions are sometimes inevitable in magnetic resonance imaging due to limitations posed by pulse sequence timing and signal-to-noise-ratio. The estimation of an isotropic high-resolution volume from thick-slice MRI scans is desired for improved image analysis and evaluation. In this article we formulate a maximum a posteriori (MAP) estimation algorithm for high-resolution volumetric MRI reconstruction. As compared to the previous techniques, this probabilistic formulation relies on a slice acquisition model and allows the incorporation of image priors. We focus on image priors based on image gradients and compare the developed MAP estimation approach to scattered data interpolation (SDI) and maximum likelihood reconstruction. The results indicate that the developed MAP estimation approach outperforms the SDI techniques and appropriate image priors may improve the volume estimation when the acquired thick-slice scans do not sufficiently sample the imaged volume. We also report applications in pediatric and fetal imaging.