伦敦国王学院心脏解剖学博士

Computational cardiac anatomy: novel shape biomarkers

Host: King’s College of London (KCL)

Objectives: to propose novel clinical diagnostic metrics based on the concept of statistical models of anatomy. This project will benefit from the huge amount of imaging data stored in health information systems, and build statistical atlases of the cardiac ventricles and main vessels. The description of the range of variability in healthy population will then offer the opportunity to detect earlier deviations caused by disease (F12, F13, F14 & F15). The patient-specific and anatomically detailed geometrical models will also be used to run simulations to predict the impact of therapy in heart failure, arrhythmia or valve conditions (F8, F10 and F14).

Planned secondment(s):

• Industrial exposure to GEVU. For 2M in Y2, in order to investigate the full automation in the generation of atlases using machine learning technologies.

• Clinical exposure to HCB/AQuAS (co-located in Barcelona), for 2M in Y1, in order to get clinical data and to identify clinical problems that could benefit from the atlas technology developed.