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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Purpose

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) spatially resolved error estimation.

Materials and Methods

Using standard T2* relaxation time measurement protocols, and MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an mouse liver measurement.

Results

The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as occurring reasons for the increased errors.

Conclusion

The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

© 2024 Polei et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-27

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/content/journals/cmir/10.2174/0115734056263741231117112245
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7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment
Curr. Med. Imaging 20, e15734056263741 (2024); https://doi.org/10.2174/0115734056263741231117112245
/content/journals/cmir/10.2174/0115734056263741231117112245
/content/journals/cmir/10.2174/0115734056263741231117112245
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  • Article Type:     Research Article
Keyword(s): Data quality; Liver fat quantification; MRI; NAFLD; PDFF; Small animal

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