Multiparametric functional MRI of the kidneys – evaluation of test-retest repeatability and effects of different manual and automatic image analysis strategies

Abstract

Objective

Multiparametric MRI is a promising technique for noninvasive structural and functional imaging of the kidneys that is gaining increasing importance in clinical research. Still, there are no standardized recommendations for analyzing the acquired images and there is a need to further evaluate the accuracy and repeatability of currently recommended MRI parameters. The aim of the study was to evaluate the test-retest repeatability of functional renal MRI parameters using different image analysis strategies.

Methods

Ten healthy volunteers were examined twice with a multiparametric renal MRI protocol including arterial spin labeling (ASL), diffusion-weighted imaging (DWI) with intravoxel incoherent motion (IVIM), blood-oxygen-dependent (BOLD) imaging, T1 and T2 mapping, and volumetry with an interval of one week. The quantitative results of both kidneys were determined by manual organ segmentation, ROI analysis, and automatic segmentation based on the nnUNet framework. Test-retest repeatability of each parameter was computed using the within-subject coefficient of variance (wCV) and the intraclass coefficient (ICC). Segmentation accuracy and inter-reader agreement were evaluated using the dice score.

Results

Structural tissue parameters (T1, T2) showed wCV (%) between 4 and 11 and an ICC between 0.2 and 0.8. Functional parameters (ASL, BOLD and DWI) showed wCV (%) between 3 and 38 and an ICC between 0.0 and 0.7. The highest variances between test-retest scans were observed in perfusion measurements with ASL and IVIM (wCV: 17–37%). Quantitative analysis of the cortex and medulla showed a better repeatability when acquired using manual segmentation compared to ROI-based image analysis. Comparable repeatability was achieved with manual and automatic segmentation of the total kidney.

Conclusion

Reasonable repeatability was achieved for all MR parameters. Structural MR parameters showed better repeatability compared to functional parameters. ROI-based image analysis showed overall lower repeatability compared to manual segmentation. Comparable repeatability to manual segmentation as well as acceptable segmentation accuracy could be achieved with automatic segmentation.

Key Points

• Reasonable test-retest repeatability can be achieved with multiparametric MRI of the kidneys.

• Image analysis based on manual segmentation of the cortex and medulla showed overall better repeatability compared to ROI-based analysis.

• Automatic segmentation of kidney volume showed similar repeatability of quantitative image analysis compared to manual segmentation.

Citation Format

• Liang C, Loster I, Ursprung S et al. Multiparametric functional MRI of the kidneys - evaluation of test-retest repeatability and effects of different manual and automatic image analysis strategies. Fortschr Röntgenstr 2024; DOI 10.1055/a-2480-4885

Zusammenfassung

Einleitung

Die multiparametrische MRT ist eine vielversprechende Technik zur nicht-invasiven strukturellen und funktionellen Bildgebung der Nieren, welche an zunehmender Bedeutung in der klinischen Forschung gewinnt. Allerdings gibt es noch keine standardisierten Empfehlungen zur Auswertung der erhobenen Bilddaten und auch die derzeit empfohlenen MR-Parameter müssen weiter hinsichtlich ihrer Genauigkeit und Wiederholbarkeit untersucht werden. Ziel dieser Studie war die Evaluation der Test-Retest-Wiederholbarkeit der funktionellen MR-Parameter an der Niere unter Verwendung verschiedener Strategien zur Bildanalyse.

Material und Methoden

10 gesunde Probanden wurden zweimal mit Abstand einer Woche mittels eines multiparametrischen MR-Protokolls der Nieren untersucht, welches folgende Parameter umfasste: arterial spin labeling (ASL), diffusion-weighted imaging (DWI) with intravoxel incoherent motion (IVIM), blood-oxygen-dependent (BOLD) imaging, T1 und T2 mapping und die Volumetrie. Die quantitativen Ergebnisse beider Nieren wurden mittels manueller Segmentierung, ROI-Analyse und automatischer Segmentierung basierend auf dem nnUNet framework erhoben. Die Test-Retest Wiederholbarkeit der einzelnen Parameter wurde mittels within-subject Variationskoeffizienten (wCV) und des Intraklassen-Korrelationskoeffizienten (ICC) ermittelt. Die Segmentierungsgenauigkeit sowie die Übereinstimmung zwischen den Bewertern wurde mittels Dice score evaluiert.

Ergebnisse

Strukturelle Gewebeparameter (T1, T2), zeigten eine wCV zwischen 4 und 11% sowie einen ICC zwischen 0,2 und 0,8. Funktionelle Parameter (ASL, BOLD und DWI) wiesen einen wCV zwischen 3 und 38 auf sowie einen ICC zwischen 0,0 und 0,7. Die höchste Varianz zwischen den Test-Retest-Scans konnte bei den Perfusionsmessungen (ASL und IVIM) beobachtet werden mit einem wCV zwischen 17 bis 37%. Die quantitative Analyse des Nierenkortex sowie der Medulla zeigte insgesamt eine bessere Wiederholbarkeit unter Verwendung der manuellen Segmentierung im Vergleich zur ROI-Analyse. Eine vergleichbare Wiederholbarkeit zur manuellen Segmentierung konnte mit der automatischen Segmentierung erzielt werden.

Schlussfolgerung

Insgesamt zeigten alle angewandten MR-Parameter eine akzeptable Test-Retest-Wiederholbarkeit. Strukturelle MR-Parameter zeigten eine bessere Wiederholbarkeit im Vergleich zu funktionellen Parametern. Mittels ROI-Analyse erhobene Daten wiesen eine geringere Wiederholbarkeit im Vergleich zu mittels manueller Segmentierung erhobenen Daten auf. Eine mit der manuellen Segmentierung vergleichbare Wiederholbarkeit sowie eine akzeptable Segmentierungsgenauigkeit konnte mittels automatischer Segmentierung erzielt werden.

Kernaussagen

• Eine angemessene Test-Retest-Wiederholbarkeit kann mittels multiparametrischer MRT der Nieren erreicht werden.

• Die manuelle Segmentierung des Kortex und der Medulla zeigte insgesamt eine bessere Wiederholbarkeit im Vergleich zur der ROI-Analyse.

• Die automatische Segmentierung des Nierenvolumens zeigte eine mit der manuellen Segmentierung vergleichbare Wiederholbarkeit bzgl. der quantitativen Bildanalyse.

Keywords

MRI - segmentation - kidney

Publikationsverlauf

Eingereicht: 17. August 2024

Angenommen nach Revision: 10. November 2024

Artikel online veröffentlicht:
10. Januar 2025

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