560-05-003 ISMRM Abstract

Optimizing perfusion quantification for phase-resolved functional lung MRI using Gaussian filtering

Primary: Body - Lung

Secondary: Analysis Methods - Data Processing

560-05-003 · Acquisition Strategies Across Organs and Field Strengths · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row A

Keywords: Image processing DCE-MRI Functional lung imaging PREFUL MRI Perfusion quantification

Accepted

Kirsten Schmidt ^1,2, Andreas Voskrebenzev^1,2, Julian Glandorf^1,2, Marius Klein^1,2, Lea Behrendt³, Frank Wacker^1,2, Jens Vogel-Claussen^1,2,4, Filip Klimeš^1,2,5

¹Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany

²German Center for Lung Research (DZL), Munich, Germany

³Department of Radiation Protection and Medical Physics, Hannover Medical School, Hannover, Germany

⁴Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany

⁵Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

Presenting Author: Kirsten Schmidt

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Voskrebenzev, A., Gutberlet, M., Klimeš, F., et al. Feasibility of quantitative regional ventilation and perfusion mapping with phase-resolved functional lung (PREFUL) MRI in healthy volunteers and COPD, CTEPH, and CF patients. (2017); Magnetic Resonance in Medicine, 79(4), 2306–2314. https://doi.org/10.1002/MRM.268932314. https://doi.org/10.1002/MRM.26893 [doi]

2. Glandorf, J., Klimeš, F., Behrendt, L., et al. Perfusion quantification using voxel-wise proton density and median signal decay in PREFUL MRI. (2021); Magnetic Resonance in Medicine, 86(3), 1482–1493. https://doi.org/10.1002/MRM.28787 [doi]

3. Hohlfeld, J. M., Vogel-Claussen, J., Biller, H., et al. Effect of lung deflation with indacaterol plus glycopyrronium on ventricular filling in patients with hyperinflation and COPD (CLAIM): a double-blind, randomised, crossover, placebo-controlled, single-centre trial. (2018); The Lancet Respiratory Medicine, 6(5), 368–378. https://doi.org/10.1016/S2213-2600(18)30054-7 [doi]

4. Miller, M. R., Hankinson, J., Brusasco, V., et al. Standardisation of spirometry. (2005); European Respiratory Journal, 26(2), 319–338. https://doi.org/10.1183/09031936.05.00034805https://doi.org/10.1183/09031936.05.00034805 [doi]

5. Avants, B. B., Tustison, N. J., Song, G., et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. (2011); NeuroImage, 54(3), 2033–2044. https://doi.org/10.1016/J.NEUROIMAGE.2010.09.025 [doi]

6. Isensee, F., Jaeger, P. F., Kohl et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. (2020); Nature Methods 2020 18:2, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z [doi]

7. Pöhler, G. H., Klimeš, F., Behrendt, L., et al. Repeatability of Phase-Resolved Functional Lung (PREFUL)-MRI Ventilation and Perfusion Parameters in Healthy Subjects and COPD Patients. (2021); Journal of Magnetic Resonance Imaging, 53(3), 915–927. https://doi.org/10.1002/JMRI.27385 [doi]

8. Behrendt, L., Voskrebenzev, A., Klimeš, F., et al. Validation of Automated Perfusion-Weighted Phase-Resolved Functional Lung (PREFUL)-MRI in Patients with Pulmonary Diseases. (2019); Journal of Magnetic Resonance Imaging, 52(1),103-114. https://doi.org/10.1002/jmri.27027 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-05-003