PURPOSE: Combination of various intraoperative imaging modalities potentially can reduce error of brain shift estimation during neurosurgical operations. In the present work, a new combination of surface imaging and Doppler US images is proposed to calculate the displacements of cortical surface and deformation of internal vessels in order to estimate the targeted brain shift using a Finite Element Model (FEM). Registration error in each step and the overall performance of the method are evaluated. METHODS: The preoperative steps include constructing a FEM from MR images and extracting vascular tree from MR Angiography (MRA). As the first intraoperative step, after the craniotomy and with the dura opened, a designed checkerboard pattern is projected on the cortex surface and projected landmarks are scanned and captured by a stereo camera (Int J Imaging Syst Technol 23(4):294-303, 2013. doi: 10.1002/ima.22064 ). This 3D point cloud should be registered to boundary nodes of FEM in the region of interest. For this purpose, we developed a new non-rigid registration method, called finite element drift that is more compatible with the underlying nature of deformed object. The presented algorithm outperforms other methods such as coherent point drift when the deformation is local or non-coherent. After registration, the acquired displacement vectors are used as boundary conditions for FE model. As the second step, by tracking a 2D Doppler ultrasound probe swept on the parenchyma, a 3D image of deformed vascular tree is constructed. Elastic registration of this vascular point cloud to the corresponding preoperative data results the second series of displacement vector applicable to closest internal nodes of FEM. After running FE analysis, the displacement of all nodes is calculated. The brain shift is then estimated as displacement of nodes in boundary of a deep target, e.g., a tumor. We used intraoperative MR (iMR) images as the references for measuring the performance of the brain shift estimator. In the present study, two set of tests were performed using: (a) a deformable brain phantom with surface data and (b) an alive brain of an approximately big dog with surface data and US Doppler images. In our designed phantom, small tubes connected to an inflatable balloon were considered as displaceable targets and in the animal model, the target was modeled by a cyst which was created by an injection. RESULTS: In the phantom study, the registration error for the surface points before FE analysis and for the target points after running FE model were <0.76 and 1.4 mm, respectively. In a real condition of operating room for animal model, the registration error was about 1 mm for the surface, 1.9 mm for the vascular tree and 1.55 mm for the target points. CONCLUSIONS: The proposed projected surface imaging in conjunction with the Doppler US data combined in a powerful biomechanical model can result an acceptable performance in calculation of deformation during surgical navigation. However, the projected landmark method is sensitive to ambient light and surface conditions and the Doppler ultrasound suffers from noise and 3D image construction problems, the combination of these two methods applied on a FEM has an eligible performance.