Publicação
Advances in Shoulder Pain Imaging
| Resumo: | Shoulder pain is among the most frequent musculoskeletal complaints and remains a significant therapeutic challenge in clinical practice. A wide spectrum of conditions may contribute to this symptom, including rotator cuff tendinosis or tears, calcific tendinopathy, labral or capsuloligamentous injuries and degenerative changes of the glenohumeral joint. Accurate diagnosis requires an integrated approach that combines clinical history, physical examination, and imaging. However, variability in examination technique and interpretation often limits the reliability of clinical assessment alone. Diagnostic imaging plays a crucial role in evaluating the shoulder joint and its surrounding soft-tissue structures. Magnetic resonance imaging has become the gold standard for shoulder evaluation due to its high resolution and superior soft-tissue contrast, allowing for a detailed assessment of tendons, muscles, cartilage, and bone marrow. Magnetic resonance arthrography further enhances sensitivity for labroligamentous and cartilage injuries, and remains essential in many clinical scenarios. Recent technological advancements, such as radial imaging, kinematic or cine-MRI, 3D acquisition and reconstruction, dynamic contrast-enhanced sequences, ultrashort time-to-echo imaging, T2 mapping, and fat quantification, are expanding the diagnostic capabilities of MRI and promoting a shift from qualitative to quantitative evaluation of tissue integrity. Additionally, demand for faster imaging has driven the development of accelerated acquisition techniques that retain diagnostic image quality with shorter acquisition times. Emerging artificial intelligence-driven tools are beginning to influence every stage of imaging, from protocol optimization to automated segmentation and the extraction of quantitative biomarkers. These innovations promise to improve diagnostic accuracy, streamline workflows, and usher in a new era of patient-specific care in shoulder pain imaging. |
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| Autores principais: | Caetano, António Proença |
| Outros Autores: | Barros, André; Carpinteiro, Eduardo; Gaspar, Augusto; Ribeiro, Marina; Gonçalves, Fernando; Branco, Pedro Soares; Mascarenhas, Vasco Vogado |
| Assunto: | Advanced imaging Artificial intelligence Cine-MRI Deep learning Dynamic-MRI Fat quantification Magnetic resonance arthrography Magnetic resonance imaging Quantitative imaging Rotator cuff Shoulder T2 mapping Radiology Nuclear Medicine and imaging |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | recensão |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Shoulder pain is among the most frequent musculoskeletal complaints and remains a significant therapeutic challenge in clinical practice. A wide spectrum of conditions may contribute to this symptom, including rotator cuff tendinosis or tears, calcific tendinopathy, labral or capsuloligamentous injuries and degenerative changes of the glenohumeral joint. Accurate diagnosis requires an integrated approach that combines clinical history, physical examination, and imaging. However, variability in examination technique and interpretation often limits the reliability of clinical assessment alone. Diagnostic imaging plays a crucial role in evaluating the shoulder joint and its surrounding soft-tissue structures. Magnetic resonance imaging has become the gold standard for shoulder evaluation due to its high resolution and superior soft-tissue contrast, allowing for a detailed assessment of tendons, muscles, cartilage, and bone marrow. Magnetic resonance arthrography further enhances sensitivity for labroligamentous and cartilage injuries, and remains essential in many clinical scenarios. Recent technological advancements, such as radial imaging, kinematic or cine-MRI, 3D acquisition and reconstruction, dynamic contrast-enhanced sequences, ultrashort time-to-echo imaging, T2 mapping, and fat quantification, are expanding the diagnostic capabilities of MRI and promoting a shift from qualitative to quantitative evaluation of tissue integrity. Additionally, demand for faster imaging has driven the development of accelerated acquisition techniques that retain diagnostic image quality with shorter acquisition times. Emerging artificial intelligence-driven tools are beginning to influence every stage of imaging, from protocol optimization to automated segmentation and the extraction of quantitative biomarkers. These innovations promise to improve diagnostic accuracy, streamline workflows, and usher in a new era of patient-specific care in shoulder pain imaging. |
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