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Spatial compounding is an imaging technique that aims to improve image contrast by combining partially decorrelated images acquired at different angles or positions. In conventional spatial compounding, data sets are combined with equal weighting. Here, we describe an alternative method of reconstruction using algorithms which weight the data based on a “quality” matrix. The quality matrix is derived from beam-forming characteristics. For each data set, the reliability of the data is assumed to vary spatially. By compounding the data based on the quality matrix, a complete image is formed. Here, we describe the construction of a rotational translation stage and tissue-mimicking phantoms that are used in conjunction with a commercial medical ultrasound machine to test our reconstruction algorithms. The new algorithms were found to increase the contrast-to-speckle ratio of simulated cysts and tumors by 61% from raw data, and to significantly increase edge definition of small embedded targets. The new method shows promise as a computationally efficient method of improving contrast and resolution in ultrasound images. The method should be particularly useful in breast imaging, where images from multiple angles can be acquired without interference from bone or air.