Clinical validation of fully automated laminar knee cartilage transverse relaxation time (T2) analysis in anterior cruciate ligament (ACL)-injured knees- on behalf of the osteoarthritis (OA)-Bio consortium.
Articular cartilage composition
automated segmentation
convolutional neural network (CNN)
deep learning (DL)
transverse relaxation time (T2)
Journal
Quantitative imaging in medicine and surgery
ISSN: 2223-4292
Titre abrégé: Quant Imaging Med Surg
Pays: China
ID NLM: 101577942
Informations de publication
Date de publication:
01 Jul 2024
01 Jul 2024
Historique:
received:
29
01
2024
accepted:
06
05
2024
medline:
18
7
2024
pubmed:
18
7
2024
entrez:
18
7
2024
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees. We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner. Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).
Sections du résumé
Background
UNASSIGNED
Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees.
Methods
UNASSIGNED
We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner.
Results
UNASSIGNED
Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2
Conclusions
UNASSIGNED
This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).
Identifiants
pubmed: 39022226
doi: 10.21037/qims-24-194
pii: qims-14-07-4319
pmc: PMC11250285
doi:
Types de publication
Journal Article
Langues
eng
Pagination
4319-4332Informations de copyright
2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-194/coif). F.E., S.M. and W.W. declare they are employees and co-owners of Chondrometrics GmbH. F.E. also has received grants or contracts from Merck KGA, Kolon Tissuegene, Galapagos, Novartis, and the European Union (EU). He has provided consulting services to Merck KGA, Kolon Tissue Gene, Galapagos, Novartis, 4P Pharma, and Formation Bio. He has participated in data safety monitoring boards of Galapagos, 4P Pharma and Formation Bio. A.W. declares she is an employee of Chondrometrics GmbH. F.B. is shareholder of 4Moving Biotech and has received consulting or speaker fees from 4P Pharma, Grunenthal, GSK, Eli Lilly, Heel, AstraZeneca, Diffusion Rx, Nordic Bioscience, Novartis, Pfizer, Servier, Zoetis, and Viatris. He has participated in data safety monitoring boards of AstraZeneca, Sun Pharma and Nordic Bioscience. He owns stocks or stock options of 4P Pharma and 4Moving Biotech. The other authors have no conflicts of interest to declare.