ArticleMagnetic resonance in medicine2020
Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.Magnetic resonance in medicine · 2025Article
- Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation.The British journal of radiology · 2023Article
- Model-constrained reconstruction accelerated with Fourier-based undersampling for hyperpolarized [1-Magnetic resonance in medicine · 2023Article
- Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.IEEE transactions on medical imaging · 2022Article
- Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.NMR in biomedicine · 2022Article
- An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques.Tomography (Ann Arbor, Mich.) · 2022Article
- Sparse precontrast TMagnetic resonance in medicine · 2021Article
- Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects.Magnetic resonance in medicine · 2020Article
- Feasibility of measuring blood-brain barrier permeability using ultra-short echo time radial magnetic resonance imaging.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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4 authors.
Funding
Abstract
purposeTo evaluate the impact of (k,t) data sampling on the variance of tracer-kinetic parameter (TK) estimation in high-resolution whole-brain dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints.
methodsThree anatomically and physiologically realistic brain-tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone-based, lattice, pseudo-random, and pseudo-radial; with 50-time frames and 4-fold to 25-fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image-time-series reconstruction followed by model fitting), and direct estimation from the under-sampled data. We evaluated methods based on the Cramér-Rao bound and Monte-Carlo simulations, over the range of signal-to-noise ratio (SNR) seen in clinical brain DCE-MRI.
resultsLattice-based sampling provided the lowest SDs, followed by pseudo-random, pseudo-radial, and zone-based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo-random sampling resulted in 19% higher averaged SD compared to lattice-based sampling. Zone-based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice-based and pseudo-random sampling up to undersampling factors of 25.
conclusionLattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice-based and pseudo-random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25-fold undersampling.
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