Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.

Improved adaptive tessellation rendering algorithm.

Background: The human body model in the virtual surgery system is generally nested by multiple complex models and each model has quite complex tangent and curvature change. In actual rendering, if all details of the human body model are rendered with high performance, it may cause the stutter due to insufficient hardware performance. If the human body model is roughly rendered, the details of the model cannot be well represented.