Our study introduced a network enabling fully automated BCA on MRI, achieved through a novel approach using DL-based style transfer. This approach facilitated modality transfer by effectively adapting DL models to MRI data, facilitating comprehensive whole-body BCA. By reducing annotation effort and achieving high evaluation scores across nearly all body-region and body-part classes, our work demonstrated the feasibility of leveraging style transfer for fully-automated adaption of BCA on the T2-weighted MRI sequences. By automating the transformation of CT-based segmentation masks to a synthetic MRI format, CycleGAN reduced the manual annotation workload, thus making MRI-based BCA more feasible and efficient for clinical integration. Using the provided models in clinical settings could have a transformative impact on personalized medicine, which could be used for more precise diagnosis and treatment planning across various conditions as well as enhancing patient care by utilizing routine clinical images to extract relevant body composition markers at no additional procedural burden. This approach maximizes the utility of existing imaging data, providing a more accurate and detailed view of the patient’s body composition and overall health. One of the primary advantages of opportunistic screening is the potential to uncover and quantify relevant image-based biomarkers like sarcopenia or different adipose tissue markers supporting a proactive, preventive care model that could identify early risk factors and allow for timely interventions and personalized lifestyle modifications ^32-34. Moreover, BCA-MR allows for detailed tracking of body composition changes over time, providing valuable insights into aging, disease progression, and treatment effects in longitudinal studies ^19,35,36. 

While our study demonstrates BCA-MR's promise, further research is needed to explore its full potential in clinical settings. Validation in larger and more diverse patient groups is crucial to ensure generalizability. Additionally, research is needed to explore the utility of BCA-MR in specific clinical scenarios, such as monitoring treatment responses for obesity, sarcopenia, or rehabilitation. Another limitation of the study is that not all relevant tissues for BCA are covered by the resulting T2-weighted MRI model, for example visceral adipose tissue (VAT) or intramuscular adipose tissue (IMAT). Therefore, in future studies those tissues need to be added to the model to provide more relevant BCA tissue classes which could enhance relevant clinical and research questions as well as detailed body composition monitoring.