Voxel vs. ROI-Based Statistical Analyses: From Histograms to Patients
Kyrre E Emblem1
1Oslo University Hospital, Norway

Synopsis

Keywords: Image acquisition: Image processing, Transferable skills: Reproducible research, Image acquisition: Visualization

Making good use of MRI data from a clinical study can be a challenge, especially when faced with the task of analyzing data from advanced imaging techniques in small patient cohorts. This talk will address some of the current challenges with image analyses in a clinical setting. Using neuroimaging and cancer as examples, the talk will discuss potential strategies to help produce and evaluate robust, repeatable, and clinically meaningful image parameters in patient studies with the typical low sample size. Different approaches for assessing and analyzing resulting parametric maps will be presented, including use of dynamic and longitudinal imaging data.

Summary

This talk will shed light on some of the most common challenges, as well as relevant strategies, for quantification and readout of region-of-interest (ROI) image analyses. Making good use of MRI data from a clinical study can be a challenge, especially when faced with the task of analyzing data from advanced imaging techniques in small patient cohorts. Using neuroimaging and cancer as examples, the first part of this talk will focus on traditional approaches for analyzing region-based imaging data, and the need to choose your analysis tools wisely to help answer clinically meaningful research questions. Second, this lecture will discuss use of dynamic and longitudinal image acquisitions to help get the most out of your study. Here, a critical and somewhat underestimated source of error is the importance of image coregistration of conventional, anatomical MRIs with multiparametric maps. Finally, the talk will summarize strategies to design an imaging study that allows for good quality data and robust image interpretations even with small sample sizes. This also includes making certain compromises for standardized image acquisition protocols and post-processing approaches, while best trying to target the object or disease of interest.

Acknowledgements

No acknowledgement found.

References

No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)