Introduction and problem statement: Ultrasound imaging is one of the most preferred modalities for image-guided procedures owing to its low-cost, non-ionizing nature, and real-time capability. However, its interpretation is complex, and hence considerable research has gone into developing methods that can help improve the interpretation of ultrasound images. One such solution is to use ultrasound simulation tools that can generate patient-specific ultrasound images from image data obtained from other modalities during pre-planning stage. Use of such tools can aid the user to have enough practice before the actual image-guided procedure. In this regard, ultrasound simulation from CT data has gained much interest over the past few years. Methodology: One of the most recent ways of simulating ultrasound images from CT is to combine the ultrasound echo reflection image, the transmission intensity map, and the scatter image of the region of interest from the CT image. However, the scatter image is simulated using Field II program, which is computationally very intensive. In this paper, we propose combining the traditional convolution method for scatter map generation along with ray tracing approaches to simulate an ultrasound image. The methodology is tested on CT data from Visible Human Project (VHP) in simulations and validated on multi-modality tissue-mimicking phantom in vitro. Simulation is done for a curvilinear array transducer at 2.5 MHz frequency and an imaging depth of 180 mm. Results and conclusion: The results obtained from simulation suggests that using convolution method reduces the computation time significantly, from almost 2.4 h to about 17 s for a chosen region of interest in VHP data of dimensions 180 mm x 60 mm x 1 mm without affecting the image quality. In case of in-vitro phantom, the computation time is reduced from almost 3 h to about 10 s for a chosen region of interest of dimensions 180 mm x 60 mm x 1.25 mm. Thus, convolution method may be preferred over Field II for generating ultrasound scatter image since the method provides a comparable image to that of real-time images, but takes only significantly less computation time. © 2018 Elsevier Ltd