Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction
Cardiac cine magnetic resonance imaging (MRI) reconstruction is challenging due to spatial
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
Time-dependent deep image prior for dynamic MRI
We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic
resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for …
resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for …
NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction
Deep Learning has become a very promising avenue for magnetic resonance image (MRI)
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
Cine cardiac MRI motion artifact reduction using a recurrent neural network
Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac
diseases thanks to its ability to present cardiovascular features in excellent contrast. As …
diseases thanks to its ability to present cardiovascular features in excellent contrast. As …
Deep learning for accelerated and robust MRI reconstruction: a review
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
We introduce a generative smoothness regularization on manifolds (SToRM) model for the
recovery of dynamic image data from highly undersampled measurements. The model …
recovery of dynamic image data from highly undersampled measurements. The model …
Implicit neural networks with fourier-feature inputs for free-breathing cardiac MRI reconstruction
JF Kunz, S Ruschke, R Heckel - arXiv preprint arXiv:2305.06822, 2023 - arxiv.org
Cardiac magnetic resonance imaging (MRI) requires reconstructing a real-time video of a
beating heart from continuous highly under-sampled measurements. This task is …
beating heart from continuous highly under-sampled measurements. This task is …
Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI
Z Wang, H She, Y Zhang, YP Du - Medical image analysis, 2023 - Elsevier
Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical
and physiological limitations. The spatial-temporal dictionary learning (DL) approach …
and physiological limitations. The spatial-temporal dictionary learning (DL) approach …
Manifold learning via linear tangent space alignment (LTSA) for accelerated dynamic MRI with sparse sampling
The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging
(MRI) can be improved by reconstructing images from sparsely sampled-space data with …
(MRI) can be improved by reconstructing images from sparsely sampled-space data with …
Dynamic imaging using deep bi-linear unsupervised representation (DEBLUR)
Bilinear models such as low-rank and dictionary methods, which decompose dynamic data
to spatial and temporal factor matrices are powerful and memory-efficient tools for the …
to spatial and temporal factor matrices are powerful and memory-efficient tools for the …