Abstract #0331

A 3D Dense-U-Net for fully automated 5D flow MRI segmentation

Liliana E. Ma^1,2, Haben Berhane^1,2, Justin Baraboo^1,2, Michael Sugimura³, Christopher W. Roy⁴, Mariana Falcão⁴, Jérôme Yerly⁴, Matthias Stuber^4,5, and Michael Markl^1,2

¹Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States, ²Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, ³Self-employed, Chicago, IL, United States, ⁴Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁵Center for Biomedical Imaging, Lausanne, Switzerland

Synopsis

Recently, a free-running 5D flow framework was introduced and validated. However, some 5D flow MRI is based on 3D radial imaging, which is limited by reduced SNR that can result in challenges with 3D segmentation. A number of previous studies have investigated automatic segmentation for 4D flow MRI, however these have been traditionally optimized for Cartesian datasets, which are typically acquired over much smaller spatial matrices and cover only one respiratory position. The purpose of this study was thus to adapt and expand a deep-learning framework to cardiac 5D flow MRI data for automatic segmentation of the thoracic aorta.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.