3 research outputs found
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent
years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still
has some limitations, such as the robustness and flexibility of existing
methods have great deficiency. In this work, we propose a method to explore the
k-space domain learning via robust generative modeling for flexible
calibration-less PI reconstruction, coined weight-k-space generative model
(WKGM). Specifically, WKGM is a generalized k-space domain model, where the
k-space weighting technology and high-dimensional space augmentation design are
efficiently incorporated for score-based generative model training, resulting
in good and robust reconstructions. In addition, WKGM is flexible and thus can
be synergistically combined with various traditional k-space PI models, which
can make full use of the correlation between multi-coil data and
realizecalibration-less PI. Even though our model was trained on only 500
images, experimental results with varying sampling patterns and acceleration
factors demonstrate that WKGM can attain state-of-the-art reconstruction
results with the well-learned k-space generative prior.Comment: 11pages, 12 figure
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Decreasing magnetic resonance (MR) image acquisition times can potentially
make MR examinations more accessible. Prior arts including the deep learning
models have been devoted to solving the problem of long MRI imaging time.
Recently, deep generative models have exhibited great potentials in algorithm
robustness and usage flexibility. Nevertheless, no existing such schemes that
can be learned or employed directly to the k-space measurement. Furthermore,
how do the deep generative models work well in hybrid domain is also worth to
be investigated. In this work, by taking advantage of the deep en-ergy-based
models, we propose a k-space and image domain collaborative generative model to
comprehensively estimate the MR data from under-sampled measurement.
Experimental comparisons with the state-of-the-arts demonstrated that the
proposed hybrid method has less error in reconstruction and is more stable
under different acceleration factors.Comment: 10 pages,9 figures. arXiv admin note: text overlap with
arXiv:2109.0323