Motion-robust 2D-RADTSE can provide a high-resolution composite, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. We use deep-learning CNN for segmentation of liver in abdominal RADTSE images. An enhanced UNET architecture with generalized dice loss based objective function was implemented. Three nets were trained, one for each image type obtained from the sequence. On evaluating net performances on the validation set, we found that nets trained on TE images or T2 maps had higher average dice scores than the one trained on composites, implying information regarding T2 variation aids in segmentation.
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.
Keywords