Year: 2025
Type: article
Abstract: In quantitative susceptibility mapping (QSM), it is impossible to define an absolute reference for the reconstructed susceptibility values. Therefore, it has been suggested to use a relative reference, such as the mean susceptibility within an anatomical ROI. We investigated the theoretical basis of referencing, and what impact it may have on statistical ROI comparisons, particularly for clinical applications. We analysed a clinical epilepsy study and in-silico QSM reconstruction challenge data with various reference regions. The results are analysed as in a clinical study and resulting statistical variations are investigated from a theoretical point of view. We found that referencing has an impact on the significance of clinical findings. These effects may arise from a change in the precision of test statistics due to referencing. We also show potential biasing of results from referencing. Our findings suggest there may not be one "optimal" reference region, and care should always be taken with reference region selection depending on the specific pathology or cohort under investigation. Not explicitly referencing is less likely to lead to false positives than cherry picking a reference region to maximize statistically significant results. We encourage results to be published with their reference to facilitate future comparisons of datasets from different sources.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,090
Awards: DTP 2018-19 University College London, EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health), G5411552620, G6473515590, ROBUST ACCURATE AND FAST QUANTITATIVE SUSCEPTIBILITY MAPPING WITH A HYBRID DEEP LEARNING APPROACH, G7564478775, G8020401598, G8984831288
OpenAlex ID: W4409191304