Software Beamforming in Medical Ultrasound Imaging - a blessing and a curse

Abstract

Medical ultrasound (US) imaging is a non-invasive imaging modality. Smaller and cheaper US systems make US imaging available to more people, leading to a democratization of medical US imaging. The improvements of general processing hardware allow the reconstruction of US images to be done in software. These implementations are known as software beamforming and provide access to the US data earlier in the processing chain. Adaptive beamforming exploits the early access to the full US data with algorithms adapting the processing to the data. Adaptive beamforming claims improved image quality. The improved image will potentially result in an improved diagnosis. Adaptive beamformers have seen enormous popularity in the research community with exponential growth in the number of papers published. However, the complexity of the algorithms makes them hard to re-implement, making a thorough comparison of the algorithms difficult. The UltraSound ToolBox (USTB https://www.USTB.no) is an open source processing framework facilitating the comparison of imaging techniques and the dissemination of research results. The USTB, including the implementation of several state-of-the-art adaptive beamformers, has partly been developed in this thesis and used to produce most of the results presented. The results show that some of the contrast improvements reported in the literature turn out to be from secondary effects of adaptive processing. More specifically, we show that many state-of-the-art algorithms alter the dynamic range. These dynamic range alterations are invalidating the conventional contrast metrics. Said differently; many adaptive algorithms are so flexible that they instead of improving the image quality are merely optimizing the metrics used to evaluate the image quality. We suggest a dynamic range test, compromising data, and code, to assess whether an algorithm alters the dynamic range. A thorough review of the contrast metrics used in US imaging shows there is no consensus on the metrics used in the research literature. Therefore, our introduction of the generalized contrast to noise ratio (GCNR) is essential since this is a contrast metric immune to dynamic range alterations. The GCNR is a remedy for the curse of the metric breaking abilities of software beamforming. Software beamforming also has its blessings. The flexible implementations made possible by software beamforming does lead to improved image quality. The improved resolution of the minimum variance adaptive beamformer does lead to enhanced visualization of the interventricular septum in the human heart. The ability to do beamforming in software allows the implementation of the full reconstruction chain from raw data to the final rendered images on an iPhone. As well as the results presented in the published papers, this thesis does a thorough review of the software beamforming processing chain as implemented in the USTB.