Keywords: AI/ML Image Reconstruction, Image Reconstruction
Motivation: Parallel imaging coil constraints can make it difficult to design comfortable coil arrays.
Goal(s): To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.
Approach: We synthesized an eight-channel head coil configuration and gradually increased coil overlap making the coils less ideal for parallel imaging. A DL reconstruction method was compared to a traditional non-DL method.
Results: As coil overlap increased, a smaller decrease in reconstruction performance was seen when using a DL method versus a non-DL method.
Impact: Our works suggests parallel imaging geometric coil constraints may be relaxed when using a deep learning reconstruction method. This flexibility would lead to an increased range of coil configurations that allow for improved patient comfort while decreasing scan times.
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