Evidence map›Paper›PMID 39975439›Full record

ArticleArXiv2025

Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research.

Fahad Salman, Abhisri Ramesh, Thomas Jochmann, Mirjam Prayer, Ademola Adegbemigun, Jack A Reeves, Gregory E Wilding, Junghun Cho, Dejan Jakimovski, Niels Bergsland and 3 more

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In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Fahad Salman
Abhisri Ramesh
Thomas Jochmann
Mirjam Prayer
Ademola Adegbemigun
Jack A Reeves
Gregory E Wilding
Junghun Cho
Dejan Jakimovski
Niels Bergsland
Michael G Dwyer
Robert Zivadinov
Ferdinand Schweser

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundQuantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified.

objectiveThis study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting.

methodsWe compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time.

resultsWe found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity.

conclusionsThe findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.

Identifiers

PMID39975439
PMCPMC11838697

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.