ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2014
A framework for accurate determination of the T₂ distribution from multiple echo magnitude MRI images.
Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2014. 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.
- MultidimensionalbioRxiv : the preprint server for biology · 2026Article
- Micron-resolution Imaging of Cortical Bone under 14 T Ultrahigh Magnetic Field.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Data inversion of multi-dimensional magnetic resonance in porous media.Magnetic resonance letters · 2023Article
- Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping.Medical image analysis · 2021Article
- Full-Harmonics Phasor Analysis: Unravelling Multiexponential Trends in Magnetic Resonance Imaging Data.The journal of physical chemistry letters · 2020Article
- Retaining information from multidimensional correlation MRI using a spectral regions of interest generator.Scientific reports · 2020Article
- Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.Magnetic resonance in medicine · 2019Article
- Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing.The Journal of chemical physics · 2016Article
- A majorize-minimize framework for Rician and non-central chi MR images.IEEE transactions on medical imaging · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Measurement of the T2 distribution in tissues provides biologically relevant information about normal and abnormal microstructure and organization. Typically, the T2 distribution is obtained by fitting the magnitude MR images acquired by a multi-echo MRI pulse sequence using an inverse Laplace transform (ILT) algorithm. It is well known that the ideal magnitude MR signal follows a Rician distribution. Unfortunately, studies attempting to establish the validity and efficacy of the ILT algorithm assume that these input signals are Gaussian distributed. Violation of the normality (or Gaussian) assumption introduces unexpected artifacts, including spurious cerebrospinal fluid (CSF)-like long T2 components; bias of the true geometric mean T2 values and in the relative fractions of various components; and blurring of nearby T2 peaks in the T2 distribution. Here we apply and extend our previously proposed magnitude signal transformation framework to map noisy Rician-distributed magnitude multi-echo MRI signals into Gaussian-distributed signals with high accuracy and precision. We then perform an ILT on the transformed data to obtain an accurate T2 distribution. Additionally, we demonstrate, by simulations and experiments, that this approach corrects the aforementioned artifacts in magnitude multi-echo MR images over a large range of signal-to-noise ratios.
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Registered trials
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