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Communication Dans Un Congrès Année : 2023

Accelerating Phase and Quantitative susceptibility mapping with Scan-Specific Complex Convolutional Neural Networks

Résumé

MRI data is inherently complex-valued, the vast majority of deep learning frameworks do not yet support complex-valued data. Most reconstruction networks separate real and imaginary components into two separate real-valued channels, which may not be the most efficient way to represent complex numbers. Phase is essential for many MRI applications, including phase contrast velocity mapping and Quantitative Susceptibility Mapping (QSM) etc. We propose a new crRAKI, a scan-specific complex-valued residual convolutional neural network for 2D/3D MRI data for accelerating phase mapping and QSM. A comparison is made with GRAPPA and rRAKI for the accelerated reconstruction of MRI images.
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Dates et versions

hal-04269563 , version 1 (06-11-2023)

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  • HAL Id : hal-04269563 , version 1

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Nimje Swetali, Thierry Artières, Ludovic de Rochefort. Accelerating Phase and Quantitative susceptibility mapping with Scan-Specific Complex Convolutional Neural Networks. 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Jun 2023, Toronto, Canada. ⟨hal-04269563⟩
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