Importance of R 2 accuracy in susceptibility source separation

• Published in: Magnetic resonance in medicine
• Main Authors: Oliveira Assunção, Tereza Beatriz, Naji, Nashwan, Snyder, Jeff, Seres, Peter, Blevins, Gregg, Smyth, Penelope, Wilman, Alan H.
• Format: Journal Article
• Language: English
• Published: United States 16.08.2025
• Subjects: R2 map; susceptibility; transverse relaxation mapping; source separation
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.70034

To examine the importance of R accuracy on independent paramagnetic and diamagnetic outputs from susceptibility source separation in the brain from two publicly available methods. The effects of R errors, which translate into errors, on output maps from χ-separation and χ-sepnet were examined using data from 11 healthy volunteers. Baseline R values were determined by Bloch modeling a dual-echo turbo spin echo decay with measured flip angles. R errors were introduced from either simple exponential fitting, R multiplication factors, or R approximation using only . Altered R maps were then used as input for the susceptibility source separation models using either default or calculated relaxometric constant. Difference maps and mean percentage errors within regions of interest (ROIs) were measured. Errors in R , and hence , directly affected paramagnetic and diamagnetic components. χ-sepnet was less sensitive to R errors than χ-separation and had reduced variance among subjects. χ-sepnet susceptibility component errors did not reach more than ±20% in most ROIs for all alteration approaches. In contrast, χ-separation, with default relaxometric constant, reached 56% susceptibility component error with -25% R error input. Exponential fitting R error exceeded -25%, thus, even larger component errors occurred. -based approximation had -25% R mean error across ROIs (-18% across whole brain), yielding 57% mean susceptibility component error across ROIs. Paramagnetic and diamagnetic outputs of susceptibility source separation methods have variable responses to R error, that may occur with simple R fitting or R approximation, and can be strongly biased by it.