Reflection ultrasound computed tomography with sparse data by residual diffusion models

Ultrasound computed tomography (UCT) has emerged as a promising modality to produce high-resolution and isotropic anatomical visuals. Yet, collecting extensive UCT data via numerous transmissions is time-intensive. Sparse transmission strategies offer a practical solution to streamline data collection, but traditional Delay-and-Sum (DAS) approaches can significantly compromise image quality. Addressing these challenges, this research introduces an efficient framework for the reconstruction of reflection UCT images utilizing sparse transmission data, grounded in a novel residual-based diffusion probabilistic model. This method employs a Markov chain to enable seamless transitions between high and low-resolution images by manipulating residuals. Through evaluation experiments utilizing in-vivo human limb imaging data, we demonstrate the ability of our proposed method to produce high-quality reflection UCT images with a reduced transmission count. Quantitative analysis demonstrates the method's proficiency in reconstructing superior reflection UCT images from limited transmission data.