[The developments and applications of functional ultrasound imaging].

In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.

Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment.

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.