Synopsis

Keywords: Musculoskeletal: Muscular, Transferable skills: Reproducible research

In the MRI of muscular and neuromuscular diseases, reproducibility of the pipeline is of paramount importance. Usually, these diseases are so rare that single-center studies are underpowered to capture the variability of the population, but the step to effective multicenter trials passes through the availability of standardized and open methods, which can be implemented on a variety of machines and data. In this talk, I will present some of the current solutions, and the current challenges that are being faced to build a completely reproducible muscle MR workflow.

Muscle MRI has become an important tool for the evaluation of neuromuscular diseases, finding application not only in the diagnosis and staging of the disease, but also on the monitoring of clinical efficacy, to the point that MRI has become an important endpoint in dedicated clinical trials.However, academic research in this field is often hindered by the rarity of these diseases, to the point that even major institutions can only have access to a handful of patients.The ideal solution to this problem is to run multicenter trials; however, these trials are meaningless unless the acquired data can be compared and pooled together.At the same time, using a quantitative value as a trial endpoint requires meeting standards of repeatability and precision that need to be rigorously proven.In this light, reproducibility of the acquisition, reconstruction, and analysis pipelines are of paramount importance in the field of muscular and neuromuscular imaging.In this talk, I will show some of the tools that have been developed by the community and that can be reused to establish prospective or retrospective studies of muscle diseases, ranging from quantitative reconstruction to image segmentation. But I will also discuss the problems that need to be addressed to develop such reproducible pipelines, such as the necessity of standardization and the tradeoff between performance and generality.

Acknowledgements

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References

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