心血管血流动力学的变化与主动脉反流（AR）的发展密切相关，一种瓣膜心脏病。源自血液流量的压力梯度用于表示AR发作并评估其严重程度。可以使用四维（4D）流磁共振成像（MRI）来非侵入地获得这些度量，其中精度主要取决于空间分辨率。然而，分辨率不足通常由4D流动MRI和复杂的AR血流动力学的限制产生。为了解决这个问题，将计算流体动力学模拟转化为合成4D流动MRI数据，并用于培训各种神经网络。这些网络生成了超级分辨率，具有upsample因子的全场相位图像为4.结果显示速度误差，高结构相似度得分和从以前的工作的改进的学习能力。在两组体内4D流动MRI数据上进行进一步验证，并在去噪流量图像中展示了成功。这种方法呈现了以非侵入性方式全面分析AR血液动力学的机会。

There is a global aging population requiring the need for the right tools that can enable older adults' greater independence and the ability to age at home, as well as assist healthcare workers. It is feasible to achieve this objective by building predictive models that assist healthcare workers in monitoring and analyzing older adults' behavioral, functional, and psychological data. To develop such models, a large amount of multimodal sensor data is typically required. In this paper, we propose MAISON, a scalable cloud-based platform of commercially available smart devices capable of collecting desired multimodal sensor data from older adults and patients living in their own homes. The MAISON platform is novel due to its ability to collect a greater variety of data modalities than the existing platforms, as well as its new features that result in seamless data collection and ease of use for older adults who may not be digitally literate. We demonstrated the feasibility of the MAISON platform with two older adults discharged home from a large rehabilitation center. The results indicate that the MAISON platform was able to collect and store sensor data in a cloud without functional glitches or performance degradation. This paper will also discuss the challenges faced during the development of the platform and data collection in the homes of older adults. MAISON is a novel platform designed to collect multimodal data and facilitate the development of predictive models for detecting key health indicators, including social isolation, depression, and functional decline, and is feasible to use with older adults in the community.