Lucian Mihai Itu

TL;DR: By combining this methodology with arterial wall growth models and by comparing simulation results and patient evolution over different time ranges, such an approach is useful for predicting patient-specific disease evolution and outcome.

TL;DR: This chapter illustrates three clinical applications of the approaches presented so far in this book, including an AI based model for pressure drop computation in coarctation of the aorta (CoA), and a methodology for generating purely synthetic CoA anatomical models based on a purely synthetic training database.

TL;DR: An analysis of ten different transit time estimation methods on routine clinical angiographic data acquired at the Clinical Emergency Hospital of Bucharest by using the time density curves finds the most robust methods are the mean transit time, the mean arrival time and the cross correlation method.

TL;DR: This paper presents two pre-training methods, formulated as self-supervised classification problems, which aim to label echocardiographies according to how they are represented: flipped or non-flipped, and shows that transfer learning and self- supervision hold the potential to yield significant improvements in the learning process of the target task.

TL;DR: It is demonstrated that IFR and other patient-specific features of rest-state coronary hemodynamics can be quantified via a novel approach based on reduced-order computational modeling of blood flow.