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Abstract #2648

Deep learning-based MR image reconstruction: coil-by-coil reconstruction versus direct mapping

Yan Wu1, Megan Hu2, Cagan Alkan1, Julio Oscanoa1, Yajun Ma2, Ali Syed1, Congyu Liao1, Catherine Huang3, Marcus Alley1, Fan Zhang3, Aiqi Sun1, John Pauly1, and Shreyas Vasanawala1
1Stanford University, Stanford, CA, United States, 2university of california san diego, San Diego, CA, United States, 3Stanford Hospital, Stanford, CA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Deep learning has been increasingly applied in image reconstruction. However, existing methods have limitations.

Goal(s): We propose DL-based multi-coil image reconstruction methods that exploit data redundancy in the coil dimension.

Approach: New coil-by-coil reconstruction approaches are proposed, where individual coil images are reconstructed and combined with or without incorporating coil sensitivity. Additionally, a direct mapping model predicts coil-combined images directly from undersampled coil images.

Results: Using the proposed methods, high-quality knee images are derived with 6× and 10× acceleration. Incorporation of conventionally calculated coil sensitivity improves coil-by-coil image reconstruction. The direct mapping approach demonstrates slightly better performance even without including coil sensitivity.

Impact: Comparing DL-based direct mapping with coil-by-coil reconstruction and incorporating coil sensitivity in different ways are two fundamental questions in multi-coil MRI reconstruction. This work provides evidence that implicit estimation and integrated use of coil sensitivities may provide improved reconstruction.

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Keywords