Narrative review highlights deep learning applications in musculoskeletal MRI, indicating potential solutions for image quality issues, diagnostic interpretation errors, and long scan times.
with early triage [4] and radiological reports [5].Magnetic resonance imaging (MRI) is a valuable imaging tool for diagnosing and treating musculoskeletal and spinal disorders by visualizing the anatomy and pathology ranging from the bones and cartilages to muscles [6].However, MRI has several drawbacks, including image quality issues, radiologist errors, and long acquisition times, which can result in patient discomfort [7,8].Herein, we discuss recent methods and future directions to overcome these drawbacks from the perspective of DL applications in the image interpretation and reconstruction of musculoskeletal MRI.
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Baek et al. (2023) studied this question.
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