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Abstract #4761

Fully automatic bullseye analysis on short-axis MOLLI mapping: LV segmentation and AHA 17 parcellation

Yun-Wen Wang1, Chun-Yu Huang1, Hsiao-Hui Huang1, and Teng-Yi Huang1

1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

The free-breathing MOLLI (FB-MOLLI) presented in our previous study allowed T1 mapping in vivo without breath-hold. In this study, we attempted to implement unsupervised reconstruction for FB-MOLLI data sets and used a deformable method for image registration to improve the reliability of free-breathing T1 mapping. The results supported that the method improved the image alignments of the FB-MOLLI data sets and thus increased the quality of the T1 map. The variations of the repeated T1 measurements were significantly reduced in the anterolateral of the LV walls.

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