ArticleMagnetic resonance in medicine2019
Biophysically motivated efficient estimation of the spatially isotropic
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Fiber-orientation independent component of RFrontiers in neuroscience · 2023Article
- Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for RHuman brain mapping · 2022Article
- Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging.Journal of neuroscience methods · 2021Review
- Biophysically motivated efficient estimation of the spatially isotropicMagnetic resonance in medicine · 2019Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeTo propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation-dependent part of
methodsThe proposed method utilized a temporal second-order approximation of the hollow-cylinder-fiber model, in which the parameter describing the linear signal decay corresponded to the orientation-independent part of
resultsUsing the phenomenological method on the classical signal model, the well-known separation of
conclusionsSince the proposed second-order model features orientation-dependent and -independent components at distinct temporal orders, it can be used to remove the orientation dependence of
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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.