Approaches to standardising the magnetic resonance image analysis of equine tendon lesions

Author:

Doll Carla Ulrike1,Bohner Melanie2,Berner Dagmar34ORCID,Buettner Kathrin5,Horstmeier Carolin2,Winter Karsten26,Burk Janina12ORCID

Affiliation:

1. Equine Clinic (Surgery, Orthopaedics) Justus‐Liebig‐University Giessen Giessen Germany

2. Translational Centre for Regenerative Medicine (TRM) University of Leipzig Leipzig Germany

3. Department for Horses Faculty of Veterinary Medicine University of Leipzig Leipzig Germany

4. Equine Referral Hospital Royal Veterinary College Hawkshead Road University of London Hatfield UK

5. Unit for Biomathematics and Data Processing Justus‐Liebig‐University Giessen Giessen Germany

6. Institute of Anatomy University of Leipzig Leipzig Germany

Abstract

AbstractBackgroundLow‐field magnetic resonance imaging (MRI) has gained increasing importance to monitor equine tendon lesions. Comparing results between studies and cases is hampered, because image analysis approaches vary strongly. This study aimed to improve reliability, comparability and time efficiency of quantitative MRI image analysis.MethodsInduced tendon lesions were studied over a 24‐week period with 10 follow‐up MRI examinations. Signal intensities (SIs) of tendons, tendon lesions, cortical bone and background, as well as lesion cross‐sectional areas (CSAs) were measured. Lesion SI standardisation with different formulas was evaluated, using histological findings as reference. Different types of region of interest (ROI) for lesion SI measurement were compared. Lesion CSA measurement at different levels was evaluated, using the calculated total lesion volume as reference. Subjective lesion identification and manual CSA and SI measurements were compared to an automated, algorithm‐based approach.ResultsLesion SI standardised using a quotient of lesion and background or cortical bone SI, correlated best with histologically determined lesion severity. Lesion SI in circular ROIs correlated strongly with lesion SI in free‐hand whole‐lesion ROIs. The level of the maximum lesion CSA shifted over time; the CSA maximum correlated strongly with lesion volume. In sequences with short acquisition time, algorithm‐based automated lesion detection showed almost perfect agreement with subjective lesion identification. Automated measurement of CSA and SI was also feasible, with stronger correlation and better agreement with the manually obtained data for the SI than for the CSA.ConclusionOur study may provide guidance for MRI image analysis of tendon healing. Reliable image analysis can be performed time‐efficiently, particularly regarding lesion SI quantification.

Publisher

Wiley

Subject

General Veterinary

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