Coil Geometry Effects on Deep-learning-based MR 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.