Clinical vs. Preclinical Data Acquisition, Reconstruction & Translation
Jack Miller1
1University of Oxford, United Kingdom

Synopsis

Pre-clinical MRI is a powerful and important tool for addressing a variety of basic scientific questions, as well as providing a unique platform for technique development. This course explores its quantitative differences from clinical MR, with a strong emphasis on the statistical analysis of the data it provides, within the context of addressing basic scientific questions.

Syllabus

This talk will focus on the design and analysis of pre-clinical experiments, from the ways in which acquisition strategies need to be adapted, right the way through to the statistical analysis of the data that they provide.

Specifically, we will cover:
  • The technical challenges of pre-clinical imaging, namely, how to image small samples in high fields
  • How these challenges can be overcome, and what the main distinctions are in conceptual approach between clinical pulse sequence design (i.e. scan time, PNS and SAR limitations are paramount) to preclinical (PNS and SAR not always measured; time, while important, can be less so).
  • How many "well developed" clinically-based image analysis pipelines may not necessarily "like" to deal with pre-clinical data, and how to build an effective reconstruction pipeline.
  • Example reconstruction chains for preclinical studies, and their advantages / disadvantages.
  • Finally, once data is acquired, how to perform appropriate statistical analysis on the resulting data in order to answer scientific questions. Note that, in contrast to large-scale clinical trials, preclinical imaging studies are typically run on comparatively smaller numbers of subjects, usually for ethical reasons. Moreover, due to the setting of the experiment, the types of questions that are typically investigated are different. This can complicate the analysis.
At every point example code will be provided and disseminated, post-ISMRM, on an appropriate GitHub page. This will contain quantitative examples of both sequences, scanner reconstruction code, and statistical analysis packages in R.

Acknowledgements

I would like to acknowledge the financial support of a Novo Nordisk Postdoctoral Fellowship; support from St Hugh's College in the University of Oxford and a Junior Research Fellowship at Wadham College in the University of Oxford.

References

https://github.com/NeutralKaon/ismrm-preclinical/
Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)