ArticleBMC medical imaging2018
Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.
Article in BMC medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- AdaptiveMagnetic resonance letters · 2025Article
- Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast.Magma (New York, N.Y.) · 2024Article
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.IEEE access : practical innovations, open solutions · 2024Article
- Magnetic resonance imaging in the diagnosis of periodontal and periapical disease.Dento maxillo facial radiology · 2023Review
- Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Joint multi-field TMagnetic resonance in medicine · 2021Article
- Article
- HF-SENSE: an improved partially parallel imaging using a high-pass filter.BMC medical imaging · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundMulti-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT).
methodsFirst, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ
resultsExperimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images.
conclusionsThe proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.