ArticleMagnetic resonance in medicine2020
Magnetic resonance multitasking for multidimensional assessment of cardiovascular system: Development and feasibility study on the thoracic aorta.
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, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Imaging Biomarkers and Prevalence of Complex Aortic Plaque in Cryptogenic Stroke: A Systematic Review.Journal of the American Heart Association · 2023Pooled it
- High-resolution magnetic resonance vessel wall imaging provides new insights into Moyamoya disease.Frontiers in neuroscience · 2024Review
- Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease.Journal of magnetic resonance imaging : JMRI · 2023Article
- High-efficiency 3D black-blood thoracic aorta imaging with patch-based low-rank tensor reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- MR Multitasking-based multi-dimensional assessment of cardiovascular system (MT-MACS) with extended spatial coverage and water-fat separation.Magnetic resonance in medicine · 2023Article
- Latest Advances in Image Acceleration: All Dimensions are Fair Game.Journal of magnetic resonance imaging : JMRI · 2023Review
- Free-breathing, non-ECG, simultaneous myocardial TMagnetic resonance in medicine · 2022Article
- Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation.Physics in medicine and biology · 2022Article
- Three-dimensional simultaneous brain mapping of T1, T2,Magnetic resonance in medicine · 2022Article
- Vessel wall MR imaging of aortic arch, cervical carotid and intracranial arteries in patients with embolic stroke of undetermined source: A narrative review.Frontiers in neurology · 2022Review
- Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development.Frontiers in cardiovascular medicine · 2022Review
- Black-Blood Contrast in Cardiovascular MRI.Journal of magnetic resonance imaging : JMRI · 2022Review
- Using 5D flow MRI to decode the effects of rhythm on left atrial 3D flow dynamics in patients with atrial fibrillation.Magnetic resonance in medicine · 2021Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop an MR multitasking-based multidimensional assessment of cardiovascular system (MT-MACS) with electrocardiography-free and navigator-free data acquisition for a comprehensive evaluation of thoracic aortic diseases.
methodsThe MT-MACS technique adopts a low-rank tensor image model with a cardiac time dimension for phase-resolved cine imaging and a T
resultsThe MT-MACS images reconstructed from acquisitions as short as 6 minutes demonstrated good or excellent image quality for bright-blood (2.58 ± 0.46), dark-blood (2.58 ± 0.50), and gray-blood (2.17 ± 0.53) contrast weightings, respectively. The contrast-to-noise ratios for the three weightings were 49.2 ± 12.8, 20.0 ± 5.8 and 2.8 ± 1.8, respectively. There were good agreements in the lumen and wall area (intraclass correlation coefficient = 0.993, P < .001 for lumen; intraclass correlation coefficient = 0.969, P < .001 for wall area) and strain (intraclass correlation coefficient = 0.947, P < .001) between MT-MACS and conventional 2D sequences.
conclusionThe MT-MACS technique provides high-quality, multidimensional images for a comprehensive assessment of the thoracic aorta. Technical feasibility was demonstrated in healthy subjects and patients with thoracic aortic diseases. Further clinical validation is warranted.
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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.