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  Joint sequence optimization beats pure neural network approaches for super-resolution TSE

Dang, H., Golkov, V., Endres, J., Weinmüller, S., Glang, F., Wimmer, T., et al. (2024). Joint sequence optimization beats pure neural network approaches for super-resolution TSE. Poster presented at ISMRM & ISMRT Annual Meeting & Exhibition 2024, Singapore.

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 Creators:
Dang, HN, Author
Golkov, V, Author
Endres, J, Author
Weinmüller, S, Author
Glang, F1, Author                 
Wimmer, T, Author
Cremers, D, Author
Dörfler, A, Author
Maier, A, Author
Zaiss, M1, Author                 
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: Motivation: TSE flip angle trains can have a strong influence on the actual resolution of the acquired image and have consequently a considerable impact on the performance of a super-resolution task. Goal(s): We demonstrate the advantage of end-to-end optimization of sequence and neural network parameter compared to pure network training approaches. Approach: This MR-physics-informed training procedure jointly optimizes radiofrequency pulse trains of a PD- and T2-weighted TSE and subsequently applied CNN to predict corresponding PDw and T2w super-resolution TSE images. Results: The method generalizes from simulation-based optimization to in vivo measurements and acquired super-resolution images show higher accuracy compared to pure network training approaches. Impact: Acquired super-resolution image may improve evaluation of the data. Reduction of acquisition time compared to direct high-resolution acquisition leads to increase in patient comfort and minimization of motion artifacts.

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 Dates: 2024-05
 Publication Status: Published online
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Title: ISMRM & ISMRT Annual Meeting & Exhibition 2024
Place of Event: Singapore
Start-/End Date: 2024-05-04 - 2024-05-09

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Title: ISMRM & ISMRT Annual Meeting & Exhibition 2024
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 0341 Start / End Page: 215 Identifier: -