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Updated: Jun 6, 2026

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats
Published on: September 19, 2025
Retrospective respiratory triggering renal perfusion MRI.
Ulrike I Attenberger1, Steven P Sourbron, Henrik J Michaely
1Department of Clinical Radiology and Nuclear Medicine, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. ulrike.attenberger@medma.uni-heidelberg.de
Respiratory motion artifacts in kidney DCE-MRI are reduced by a new retrospective triggering method. This approach improves data quality and parametric maps, offering a clinically applicable solution for enhanced renal imaging.
Area of Science:
- Medical Imaging
- Renal Physiology
- Magnetic Resonance Imaging
Background:
- Respiratory motion significantly limits the quality of dynamic contrast-enhanced MRI (DCE-MRI) in kidney imaging.
- Motion artifacts can obscure important details in renal perfusion measurements.
Purpose of the Study:
- To introduce and assess a retrospective triggering technique for minimizing respiratory motion effects in renal DCE-MRI.
- To evaluate the impact of this method on the accuracy of renal perfusion parameters.
Main Methods:
- Nine patients underwent renal perfusion measurements using a 2D saturation-recovery TurboFLASH sequence.
- Three distinct triggering regions of interest (ROIs) were manually defined to assess their impact on results.
- Renal function parameters including plasma flow (F(P)), plasma volume (V(P)), and transit times (T(P), T(T)) were calculated.
Main Results:
- The triggered data demonstrated sufficient temporal resolution for accurate blood flow measurement.
- Average renal function parameters were calculated, with low variability across different triggering ROIs (e.g., F(P) variability: 0.81%-9.87%).
- The method effectively reduced oscillations and sharpened parametric maps.
Conclusions:
- Retrospective triggering is a clinically viable method to reduce respiratory motion artifacts in renal DCE-MRI.
- This technique enhances signal curve consistency and improves the clarity of parametric maps.
- While effective, a semi-automatic method for ROI selection is recommended to decrease user dependency.
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