ArticleMagnetic resonance in medicine2020
Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 10 citations in OpenAlex.
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- Insights on Scan-Specific Deep-Learning Strategies for Brain MRI Parallel Imaging Reconstruction.NMR in biomedicine · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research.Scientific data · 2023Article
- Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.Bioengineering (Basel, Switzerland) · 2022Article
- Tissue volume estimation and age prediction using rapid structural brain scans.Scientific reports · 2022Article
- Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging.Human brain mapping · 2022Article
- Scout accelerated motion estimation and reduction (SAMER).Magnetic resonance in medicine · 2022Article
- Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm.Computational and mathematical methods in medicine · 2021Article
- FlowMRI-Net: A generalizable self-supervised 4D flow MRI reconstruction network.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeTo improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.
methodsData from our multi-contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min.
resultsAcross all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics.
conclusionBy leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.
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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.