Superresolution Spatial Compounding Techniques, with Application to 3D Breast Ultrasound Imaging

摘要

Standard spatial compounding, via averaging acquisitions from different angles, has proved to be an efficient technique for speckle pattern reduction in ultrasound B-mode images. However, the resulting images may be blurred due to the averaging of point spread functions and the misalignment of the different views. These blurring artefacts result in a loss of important anatomical features that may be critical for medical diagnosis. In this paper, we present new spatial compounding techniques, focusing on how to combine the different acquisitions. The evaluated methods are: weighed averaging, wavelet coefficient fusion and multiview deconvolution. To some extent, these techniques take into account the limitations of spatial compounding, by proposing alternative fusion methods that can reduce speckle artefacts while preserving standard spatial resolution and anatomical features. We experiment these compounding methods with synthetic images to show that these advanced techniques could outperform traditional averaging. In particular, multiview deconvolution techniques perform best, while weighted averaging is the least time consuming. Wavelet fusion offers an interesting trade-off between performance and computational cost. Experiments on 3D breast ultrasound imaging show consistent results with those obtained on synthetic images, i.e. obtention of a high-resolution volume, with better tissue delineation and less speckle patterning.