Novel fusion algorithms for medical ultrasound tomography

Ultrasound tomography is a bioimaging method that combines the geometry of X-ray computed tomography with the non-ionizing energy of ultrasound. This modality has potential clinical utility in breast cancer screening and diagnosis. In conventional ultrasound tomography, data sets from different interrogation angles are used to reconstruct an estimate of a biomechanical property of the tissue, such as sound velocity, in the form of an image. Here we describe an alternative method of reconstruction using novel algorithms which weight the data based on a "quality" score. The quality score is derived from beamforming characteristics, for example, the weighting of angle-dependent data by its distance from the transmit focal zones. The new approach is that for each data set (taken at a different view angle), the reliability of the data (in the range dimension) is assumed to vary. By fusing (combining) the data based on the quality score, a complete image is formed. In this paper, 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 by 114% from raw data over a 77% improvement by spatial compounding (averaging), and to decrease wire target width by 54% over a 39% reduction by spatial compounding alone. The new method shows promise as a computationally efficient method of improving contrast and resolution in ultrasound images.