Thanks to Eden Mama’s master work & Rona Shaharabani’s lipid phantom, the answer is yes — both protocols estimate WF, T1, and their dependency (tissue relaxivity) reliably across scanners & studies.

This connection between brain tissue properties and motor symptoms sheds light on the neural substrates of PD—and may support earlier diagnosis or targeted interventions.
Huge credit to Elior Drori and the team. More to come. #Parkinsons #qMRI

Notably, water content asymmetry in the posterior putamen correlates with motor asymmetry (MDS-UPDRS III).
This links a non-invasive MRI biomarker with a key behavioral hallmark of early PD.

We show that the putamen exhibits spatial gradients across several biophysical properties—water content, iron levels, and microstructural markers.
These gradients are altered in Parkinson’s, consistently pointing to the posterior putamen as a disease hotspot.

We collected a comprehensive multiparametric qMRI dataset in collaboration with Prof. Gilad Yahalom and the Shaare Zedek movement disorders clinic.
This goes beyond our previous clinical MRI work, offering richer insight into PD-related brain changes.

5/ Potential Impact: While not perfect, this approach could enhance and expand the use of large datasets like ADNI, UK Biobank, and PPMI for standardized, semi-quantitative brain analysis across clinical and research domains. Read More: doi.org/10.1002/hbm....

5/ And in cases where T2w images were unavailable (e.g., UK Biobank), we found that diffusion (B0) data can serve as an effective replacement.

4/ Adapting to Missing Data: When PDw images were unavailable (e.g., HCP), we optimized the T1w/T2w approach.

3/ Our Approach: Using simple mathematical operations derived from the signal equations (e.g., T1w/PDw, ln(T2w/PDw)), we present a method to approximate R1 & R2 values from weighted MRI. We validated this approach with synthetic, phantom, and human clinical datasets.

2/ Why It Matters: qMRI provides critical insights into brain microstructure (e.g., myelin, water content) but requires long scan times and is often missing in clinical studies. Our method attempts to bridge this gap by approximating qMRI-like insights using weighted MRI data.