Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations
Journal:
arXiv
Published Date:
Mar 7, 2025
Abstract
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 of individual patients is used to tailor the prior-embedded
network for patient-specific optimization. The UCSF StarVIBE T1-weighted liver
dataset was evaluated on the proposed framework. k-GINR is compared with an
image-domain deep learning method, Deep Cascade CNN, and a compressed sensing
method. k-GINR consistently outperformed the baselines with a larger
performance advantage observed at very high accelerations (e.g., 20 times).
k-GINR offers great value for direct non-Cartesian k-space reconstruction for
new incoming patients across a wide range of accelerations liver anatomy.
