Evidence map›Paper›PMID 34576970›Full record

ArticleMolecules (Basel, Switzerland)2021

Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI.

Nitish Katoch, Bup-Kyung Choi, Ji-Ae Park, In-Ok Ko, Hyung-Joong Kim

Abstract readComparative Study
In one paragraph

Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nitish KatochDepartment of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea.ORCID 0000-0003-4488-201X
Bup-Kyung ChoiDepartment of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea.
Ji-Ae ParkDivision of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea.ORCID 0000-0001-6109-802X
In-Ok KoDivision of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea.
Hyung-Joong KimDepartment of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea.ORCID 0000-0001-7591-9079

Funding

Korea Institute of Radiological and Medical Sciences 50461-2021Ministry of Health and Welfare of Korea HI18C2435National Research Foundation of Korea 2019R1A2C2088573National Research Foundation of Korea 2020R1A2C200790611National Research Foundation of Korea 2021R1A2C2004299
6 · The paper itself

Abstract

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to

Indexed as

BrainDiffusion Tensor ImagingElectric ConductivityImage Processing, Computer-AssistedMagnetic Resonance ImagingPhantoms, ImagingHumansanisotropyconductivity tensor imaging (CTI)diffusion tensor imaging (DTI)electrical conductivitymagnetic resonance imaging (MRI)

Identifiers

PMID34576970
PMCPMC8467711

What OpenQuestion holds

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Registered trials

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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.