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Parallel imaging with multiple receiver coils has become a standard in many MRI applications. Methods based on Deep Learning (DL) were shown to allow higher acceleration factors than conventional methods. In the case of diffusion-weighted imaging (DWI) where multiple repetitions of a slice are acquired, a DL-based reconstruction method should ideally make use of available redundancies. Based on the concept of Deep Sets which outlines a generic approach for operating on set-structured data, this work investigates the benefits of joint reconstruction of image repetitions in DWI. Evaluations show that, compared to separate processing of repetitions, reconstructions can be improved both qualitatively and quantitatively by incorporating simple and computationally inexpensive operations into an existing DL architecture.
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Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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