Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

ABSTRACT:

Purpose

Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve.

Methods

Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC

SUBMITTER: Lamy J 

PROVIDER: S-EPMC11341256 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature