Article... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing2017
Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRI.
Article in ... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
13 citing papers in PubMed, 17 citations in OpenAlex.
- Effect of basis choice on quantitative parameter estimation in accelerated subspace reconstructions.Magnetic resonance letters · 2026Article
- Clustering-based low-rank matrix approximation for multimodal medical image compression.BioData mining · 2026Article
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Efficient Decision Trees for Tensor Regressions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Dynamic Magnetic Resonance Imaging of Whole-Stomach Motility in Rats.NMR in biomedicine · 2025Article
- Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data.The neuroradiology journal · 2023Article
- Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development.Frontiers in cardiovascular medicine · 2022Review
- Free-breathing simultaneousMagnetic resonance in medicine · 2021Article
- Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction.Frontiers in cardiovascular medicine · 2020Review
- Functional LGE Imaging: Cardiac Phase-Resolved Assessment of Focal Fibrosis.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic CardiacIEEE transactions on computational imaging · 2019Article
- Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRI.... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative dynamic MRI acquisitions have the potential to diagnose diffuse diseases in conjunction with functional abnormalities. However, their resolutions are limited due to the long acquisition time. Such datasets are multi-dimensional, exhibiting interactions between ≥ 4 dimensions, which cannot be easily identified using sparsity or low-rank matrix methods. Hence, low-rank tensors are a natural fit to model such data. But in the presence of multitude of different tissue types in the field-of-view, it is difficult to find an appropriate value of tensor rank, which avoids under- or over-regularization. In this work, we propose a locally low-rank tensor regularization approach to enable high-resolution quantitative dynamic MRI. We show this approach successfully enables dynamic
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What OpenQuestion holds
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.