Meeting Banner
Abstract #1047

Automatic AHA model segmentation of cardiac T1 maps with deep learning

Nicola Martini1, Daniele Della Latta1, Gianmarco Santini1,2, Gabriele Valvano1, Andrea Barison3, Francesco Avogliero1, Daniele De Marchi3, Luigi Landini1,2, and Dante Chiappino1

1Fondazione Toscana "G. Monasterio", Massa, Italy, 2University of Pisa, Pisa, Italy, 3Fondazione Toscana "G. Monasterio", Pisa, Italy

We proposed a fully automated approach for the segmental analysis of T1 mapping using a fully convolutional neural network architecture. T1 maps acquired using the MOLLI sequence from 394 subjects were considered. Excellent segmentation results are demonstrated by high Jaccard (0.969±0.023) and a Dice (0.984±0.012) indexes. No significant difference in the obtained segmental T1 values compared to manual measurements was found, with a mismatch percentage ranging from 0.95% to 3.14% across segments.

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.

Keywords