Isotropic multichannel total variation framework for joint
reconstruction of multicontrast parallel MRI
- URL: http://arxiv.org/abs/2006.04128v5
- Date: Sun, 20 Feb 2022 09:15:17 GMT
- Title: Isotropic multichannel total variation framework for joint
reconstruction of multicontrast parallel MRI
- Authors: Erfan Ebrahim Esfahani
- Abstract summary: We introduce an isotropic MC image regularizer and attain its full potential by integrating it into compressed MC multicoil MRI.
The proposed isotropic regularizer outperforms many of the state-of-the-art reconstruction methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Purpose: To develop a synergistic image reconstruction framework that
exploits multicontrast (MC), multicoil, and compressed sensing (CS)
redundancies in magnetic resonance imaging (MRI).
Approach: CS, MC acquisition, and parallel imaging (PI) have been
individually well developed, but the combination of the three has not been
equally well studied, much less the potential benefits of isotropy within such
a setting. Inspired by total variation theory, we introduce an isotropic MC
image regularizer and attain its full potential by integrating it into
compressed MC multicoil MRI. A convex optimization problem is posed to model
the new variational framework and a first-order algorithm is developed to solve
the problem.
Results: It turns out that the proposed isotropic regularizer outperforms
many of the state-of-the-art reconstruction methods not only in terms of
rotation-invariance preservation of symmetrical features, but also in
suppressing noise or streaking artifacts, which are normally encountered in PI
methods at aggressive undersampling rates. Moreover, the new framework
significantly prevents intercontrast leakage of contrast-specific details,
which seems to be a difficult situation to handle for some variational and
low-rank MC reconstruction approaches.
Conclusions: The new framework is a viable option for image reconstruction in
fast protocols of MC parallel MRI, potentially reducing patient discomfort in
otherwise long and time-consuming scans.
Related papers
- Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction [3.9681863841849623]
We build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them.
Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process.
Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information.
arXiv Detail & Related papers (2024-05-09T05:51:33Z) - DuDoUniNeXt: Dual-domain unified hybrid model for single and
multi-contrast undersampled MRI reconstruction [24.937435059755288]
We propose DuDoUniNeXt, a unified dual-domain MRI reconstruction network that can accommodate to scenarios involving absent, low-quality, and high-quality reference images.
Experimental results demonstrate that the proposed model surpasses state-of-the-art SC and MC models significantly.
arXiv Detail & Related papers (2024-03-08T12:26:48Z) - Fill the K-Space and Refine the Image: Prompting for Dynamic and
Multi-Contrast MRI Reconstruction [31.404228406642194]
The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information.
We propose a two-stage MRI reconstruction pipeline to address these limitations.
Our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.
arXiv Detail & Related papers (2023-09-25T02:51:00Z) - JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network
for Multi-contrast MRI [49.29851365978476]
The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure.
The acceleration ratio of each image contrast is also learnable and can be driven by a downstream task performance.
arXiv Detail & Related papers (2022-10-22T20:46:56Z) - Model-Guided Multi-Contrast Deep Unfolding Network for MRI
Super-resolution Reconstruction [68.80715727288514]
We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix.
In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
arXiv Detail & Related papers (2022-09-15T03:58:30Z) - Transformer-empowered Multi-scale Contextual Matching and Aggregation
for Multi-contrast MRI Super-resolution [55.52779466954026]
Multi-contrast super-resolution (SR) reconstruction is promising to yield SR images with higher quality.
Existing methods lack effective mechanisms to match and fuse these features for better reconstruction.
We propose a novel network to address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques.
arXiv Detail & Related papers (2022-03-26T01:42:59Z) - Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality.
In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network.
Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
arXiv Detail & Related papers (2021-10-15T13:16:59Z) - Multi-Modal MRI Reconstruction with Spatial Alignment Network [51.74078260367654]
In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study.
Recent researches demonstrate that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence.
In this paper, we integrate the spatial alignment network with reconstruction, to improve the quality of the reconstructed target modality.
arXiv Detail & Related papers (2021-08-12T08:46:35Z) - Adaptive Gradient Balancing for UndersampledMRI Reconstruction and
Image-to-Image Translation [60.663499381212425]
We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique.
In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
arXiv Detail & Related papers (2021-04-05T13:05:22Z) - Robust Image Reconstruction with Misaligned Structural Information [0.27074235008521236]
We propose a variational framework which jointly performs reconstruction and registration.
Our approach is the first to achieve this for different modalities and outranks established approaches in terms of accuracy of both reconstruction and registration.
arXiv Detail & Related papers (2020-04-01T17:21:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.