Biomedical imaging is frequently used in the clinical management of musculoskeletal conditions. However, the limitations of biomedical imaging include high costs for selected procedures, disparities related to access, varying degrees of patient risk, and appropriate image interpretation. Innovative approaches to image processing and analysis may serve to lower medical costs and barriers to access through the improved utilization of opportunistic imaging and obtaining new insights from standard images through emerging analytic techniques. Examples of these approaches range from automated tissue segmentation of CT images and the use of convolutional neural network modeling to characterize muscle tissue from MRI scans to deriving estimates of lean body mass from sonograms and characterizing muscle quality using 2D texture analysis.
Emerging uses of biomedical imaging have expanded the ability of health care practitioners and investigators to assess post-injury and post-treatment changes in muscle morphology, appraise prehabilitation intervention strategies necessary to optimize condition prior to surgical or medical treatment, quantify myosteatosis in older adults, and examine articular cartilage defects in those with joint disease. Ultimately, the optimal use of biomedical imaging in rehabilitation will move beyond characterizing impairment and morbidity and help maximize functional status through enhanced clinical decision making.
This research topic will include the insights of disciplines including, but not limited to, physiatry, radiology, physical therapy, bioengineering, and movement science. The editors welcome scholarly contributions regarding screening, diagnosis, intervention, or analytic approaches (e.g., original research, reviews, perspectives, etc.) that serve to advance innovative forms of biomedical imaging to address the rehabilitation of musculoskeletal conditions.
Biomedical imaging is frequently used in the clinical management of musculoskeletal conditions. However, the limitations of biomedical imaging include high costs for selected procedures, disparities related to access, varying degrees of patient risk, and appropriate image interpretation. Innovative approaches to image processing and analysis may serve to lower medical costs and barriers to access through the improved utilization of opportunistic imaging and obtaining new insights from standard images through emerging analytic techniques. Examples of these approaches range from automated tissue segmentation of CT images and the use of convolutional neural network modeling to characterize muscle tissue from MRI scans to deriving estimates of lean body mass from sonograms and characterizing muscle quality using 2D texture analysis.
Emerging uses of biomedical imaging have expanded the ability of health care practitioners and investigators to assess post-injury and post-treatment changes in muscle morphology, appraise prehabilitation intervention strategies necessary to optimize condition prior to surgical or medical treatment, quantify myosteatosis in older adults, and examine articular cartilage defects in those with joint disease. Ultimately, the optimal use of biomedical imaging in rehabilitation will move beyond characterizing impairment and morbidity and help maximize functional status through enhanced clinical decision making.
This research topic will include the insights of disciplines including, but not limited to, physiatry, radiology, physical therapy, bioengineering, and movement science. The editors welcome scholarly contributions regarding screening, diagnosis, intervention, or analytic approaches (e.g., original research, reviews, perspectives, etc.) that serve to advance innovative forms of biomedical imaging to address the rehabilitation of musculoskeletal conditions.