Medical ultrasound imaging is used for many purposes, e.g. for localizing and classifying cysts, lesions, and other processes. Almost any mass is first observed using B-mode imaging and later classified using e.g. color flow, strain, or attenuation imaging. It is therefore important that the B-mode images have high contrast. Like all imaging modalities, ultrasound is subject to a number of inherent artifacts that compromise image quality. The most prominent artifact is the degradation by coherent wave interference, known as “speckle”, which gives a granular appearance to an otherwise homogeneous region of parenchyma. A successful approach to reduce the speckle artifacts is spatial compounding, where images are acquired from a number of directions and combined after envelope-detection. Today, spatial compounding is implemented in all highend ultrasound systems and available when using a low pitch transducer with a fairly high number of independent channels. A drawback of conventional compounding is a reduction of the frame rate. In this disseration, a method for obtaining compound images using synthetic aperture data is proposed and investigated. The new approach allows spatial compounding to be performed for any number of angles without reducing the frame rate or temporal resolution. This important feature is an intrinsic property of how the compound images are constructed using synthetic aperture data and an improvement compared to how spatial compounding is obtained using conventional methods. The method is investigated using simulations and through measurements using both phased array and convex array transducers. The images all show an improved contrast compared to images without compounding, and by construction, imaging using an improved frame rate is possible. Using a phased array transducer, it is demonstrated through theoretical considerations that the compound effect achieved is close to a theoretical maximum for the amount of compounding attainable and using a -pitch convex array transducer, the first in-vivo images are created. The computational demands for an implementation are massive and the limiting factor is the amount of memory IO resources available. An equally high demand for memory throughput is found in the computer gaming industry, where a large part of the processing takes place on the graphics processing unit (GPU). Using the GPU, a framework for synthetic aperture imaging is implemented providing proof-of-concept for real-time implementations of synthetic aperture imaging.