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<p>Knee articular cartilage lesions resulting from acute or chronic injury are frequently encountered in routine clinical magnetic resonance imaging (MRI). Acute lesions typically occur following recreational or athletic activity and are often associated with concomitant internal knee derangements, whereaschronic lesions are thought to result from repetitive loading that exceeds physiological thresholds in both magnitude and frequency. Accurate characterization of cartilage lesion size, depth, and subchondral bone involvement is clinically relevant, as it directly influences therapeutic decision-making. In clinical practice, MRI evaluation of the knee cartilage is primarily performed using intermediate-weighted, two-dimensional fast spin-echo sequences, preferably with high spatial resolution and at 3 Tesla. Looking ahead, machine learning approaches hold promise for improving the prediction of osteoarthritis by integrating pathologic imaging findings with complementary sources of information such as clinical parameters, quantitative biomarkers, and patient-reported outcomes.</p>

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Acute and chronic injury of the knee articular cartilage: prevalence, injury mechanisms, MRI assessment and association with osteoarthritis

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Adrian A. Marth,
  • Thomas M. Link

摘要

Knee articular cartilage lesions resulting from acute or chronic injury are frequently encountered in routine clinical magnetic resonance imaging (MRI). Acute lesions typically occur following recreational or athletic activity and are often associated with concomitant internal knee derangements, whereaschronic lesions are thought to result from repetitive loading that exceeds physiological thresholds in both magnitude and frequency. Accurate characterization of cartilage lesion size, depth, and subchondral bone involvement is clinically relevant, as it directly influences therapeutic decision-making. In clinical practice, MRI evaluation of the knee cartilage is primarily performed using intermediate-weighted, two-dimensional fast spin-echo sequences, preferably with high spatial resolution and at 3 Tesla. Looking ahead, machine learning approaches hold promise for improving the prediction of osteoarthritis by integrating pathologic imaging findings with complementary sources of information such as clinical parameters, quantitative biomarkers, and patient-reported outcomes.