Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems

H Hisako Katano H Haruka Kaneko E Eiji Sasaki N Naofumi Hashiguchi K Kanto Nagai M Muneaki Ishijima Y Yasuyuki Ishibashi N Nobuo Adachi R Ryosuke Kuroda M Makoto Tomita J Jun Masumoto I Ichiro Sekiya

Abstract

Purpose Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical consideration. Our purposes were to quantify and compare same-model variability (between repeated scans on the same MRI system) and cross-model variability (across different MRI systems) in knee cartilage thickness measurements using MRI scanners from five manufacturers, as analyzed with a specific 3D volume analysis software. Methods Ten healthy volunteers (eight males and two females, aged 22–60 years) underwent two scans of their right knee on 3T MRI systems from five manufacturers (Canon, Fujifilm, GE, Philips, and Siemens). The imaging protocol included fat-suppressed spoiled gradient echo and proton density weighted sequences. Cartilage regions were automatically segmented into 7 subregions using a specific deep learning-based 3D volume analysis software. This resulted in 350 measurements for same-model variability and 2,800 measurements for cross-model variability. Results For same-model variability, 82% of measurements showed variability ≤0.10 mm, and 98% showed variability ≤0.20 mm. For cross-model variability, 51% showed variability ≤0.10 mm, and 84% showed variability ≤0.20 mm. The mean same-model variability (0.06 ± 0.05 mm) was significantly lower than cross-model variability (0.11 ± 0.09 mm) (p < 0.001). Conclusion This study demonstrates that knee cartilage thickness measurements exhibit significantly higher variability across different MRI systems compared to repeated measurements on the same system, when analyzed using this specific software. This finding has important implications for multi-center studies and longitudinal assessments using different MRI systems and highlights the software-dependent nature of such variability assessments.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 6
Published June 13, 2025
Pages e0324912
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (12)

H

Hisako Katano

H

Haruka Kaneko

E

Eiji Sasaki

N

Naofumi Hashiguchi

K

Kanto Nagai

M

Muneaki Ishijima

Y

Yasuyuki Ishibashi

N

Nobuo Adachi

R

Ryosuke Kuroda

M

Makoto Tomita

J

Jun Masumoto

I

Ichiro Sekiya