7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment
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Updated: Jul 2, 2025
Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
Published on: September 7, 2018
Stefan Polei1, Tobias Lindner2, Kerstin Abshagen3
1Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany.
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) via spatially resolved error estimation.
Materials And Methods:
Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo 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 in vivo mouse liver measurement.
Results:
The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo 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 in vivo 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.
Insights
This study introduces a method to assess the reliability of proton density fat fraction (PDFF) measurements from MRI and MRS. Spatially resolved error maps improve the accuracy of PDFF data evaluation.
Area of Science:
- Biomedical Imaging
- Magnetic Resonance Technology
- Quantitative MRI
Background:
- Proton Density Fat Fraction (PDFF) is a key MRI/MRS metric for quantifying fat content.
- Robustness of PDFF measurements is crucial for accurate clinical and research applications.
- Current methods may lack detailed spatial error assessment.
Purpose of the Study:
- To evaluate the robustness of PDFF data derived from MRI/MRS.
- To implement a spatially resolved error estimation technique for PDFF analysis.
- To validate the error estimation method using both ex vivo and in vivo mouse liver data.
Main Methods:
- Acquisition of in vivo and ex vivo MRI/MRS data on a 7T small animal scanner.
- Calculation of PDFF maps using a magnitude-based approach with 24 echo times.
- Pixel-wise error estimation performed via propagation of uncertainty.
Main Results:
- Error maps successfully identified measurement errors as a cause for unexpected PDFF variations in explanted liver.
- In vivo error maps raised concerns about PDFF data quality, leading to identification of motion and bladder filling as error sources.
- The method demonstrated utility in distinguishing true biological variations from measurement artifacts.
Conclusions:
- Combining pixel-wise PDFF data with corresponding error maps enhances the specificity and spatial resolution of reliability evaluation.
- This approach provides a more reliable assessment of PDFF value accuracy.
- Spatially resolved error estimation is vital for robust quantitative MRI/MRS analysis.
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