Brain shift during neurosurgery currently limits the effectiveness of stereotactic guidance systems that rely on preoperative image modalities like magnetic resonance (MR). The authors propose a process for quantifying intraoperative brain shift using spatially-tracked freehand intraoperative ultrasound (iUS). First, one segments a distinct feature from the preoperative MR (tumor, ventricle, cyst, or falx) and extracts a faceted surface using the marching cubes algorithm. Planar contours are then semi-automatically segmented from two sets of iUS b-planes obtained (a) prior to the dural opening and (b) after the dural opening. These two sets of contours are reconstructed in the reference frame of the MR, composing two distinct sparsely-sampled surface descriptions of the same feature segmented from MR. Using the Iterative Closest Point (ICP) algorithm one obtains discrete estimates of the feature deformation performing point-to-surface matching. Vector subtraction of the matched points then can be used as sparse deformation data inputs for inverse biomechanical brain tissue models. The results of these simulations are then used to modify the pre-operative MR to account for intraoperative changes. The proposed process has undergone preliminary evaluations in a phantom study and was applied to data from two clinical cases. In the phantom study, the process recovered controlled deformations with an RMS error of 1.1 mm. These results also suggest that clinical accuracy would be on the order of 1-2mm. This finding is consistent with prior work by the Dartmouth Image-Guided Neurosurgery (IGNS) group. In the clinical cases, the deformations obtained were used to produce qualitatively reasonable updated guidance volumes.