A Path Towards Clinical Adaptation of Accelerated MRI.
Autor: | Yao MS; Microsoft Research, University of Pennsylvania, Department of Bioengineering, University of Pennsylvania, School of Medicine., Hansen MS; Microsoft Research. |
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Jazyk: | angličtina |
Zdroj: | Proceedings of machine learning research [Proc Mach Learn Res] 2022 Nov; Vol. 193, pp. 489-511. |
Abstrakt: | Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier F Competing Interests: Competing Interests The authors declare no competing interests related to this work. |
Databáze: | MEDLINE |
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