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Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations by Xu, Di; Liu, Hengjie; Miao, Xin; O'Connor, Daniel; Scholey, Jessica E.; Yang, Wensha; Feng, Mary; Ohliger, Michael; Lin, Hui; Ruan, Dan; Yang, Yang; Sheng, Ke is a scholarly article available to read on EtoBox.

What is Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations about?

The scanning time for a fully sampled MRI can be undesirably lengthy. Compressed sensing has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize on new cases. Image-domain-based deep learning methods (e.g., convolutional neural networks) emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition. In comparison, implicit neural representations can model continuous signals in the frequency domain and thus are compatible with arbitrary k-space sampling patterns. The current study develops a novel generative-adversarially trained implicit neural representations (k-GINR) for de novo undersampled non-Cartesian k-space reconstruction. k-GINR consists of two stages: 1) supervised training on an existing patient cohort; 2) self-supervised patient-specific optimization. In stage 1, the network is trained with the generative-adversarial network on diverse patients of the same anatomical region supervised by fully sampled acquisition. In stage 2, undersampled k-space data o

Author

Xu, Di; Liu, Hengjie; Miao, Xin; O'Connor, Daniel; Scholey, Jessica E.; Yang, Wensha; Feng, Mary; Ohliger, Michael; Lin, Hui; Ruan, Dan; Yang, Yang; Sheng, Ke

Published

2025

Language

EN