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Record W4413449053 · doi:10.1186/s41747-025-00622-w

MRI R2* and quantitative susceptibility mapping in brain tissue with extreme iron overload

2025· article· en· W4413449053 on OpenAlexaff
Christoph Birkl, Marlene Panzer, Christian Kames, Anna Maria Birkl-Toeglhofer, Alexander Rauscher, Bernhard Glodny, Elke R. Gizewski, Heinz Zoller

Bibliographic record

VenueEuropean Radiology Experimental · 2025
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersAustrian Science Fund
KeywordsNeuroradiologyQuantitative susceptibility mappingMedicineMagnetic resonance imagingPsychologyRadiologyNeurologyNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: R2* and quantitative susceptibility mapping (QSM) are regarded as robust techniques for assessing iron content in the brain. While these techniques are established for normal or moderate iron levels, their usability in extreme iron overload, as seen in aceruloplasminemia (ACP), is unclear. We aimed to evaluate various R2* and QSM algorithms in assessing brain iron levels in patients with ACP compared to healthy controls. MATERIALS AND METHODS: We acquired a three-dimensional multiecho gradient-echo sequence for R2* and QSM in three patients with ACP and three healthy subjects. Six algorithms each for R2* and QSM were compared. QSM was performed with referencing to whole brain, to cerebrospinal fluid and without referencing. R2* and QSM values were assessed in the caudate nucleus, putamen, globus pallidus, and thalamus. RESULTS: R2* values varied significantly across algorithms, particularly in the putamen (F(5,50) = 16.51, p < 0.001). For QSM, reference region choice (F(5,150) = 264, p < 0.001) and algorithm selection (F(2,9) = 10, p < 0.001) had an impact on susceptibility values. In patients, referencing to whole brain yielded lower susceptibility values than cerebrospinal fluid (median = 0.147 ppm, range = 0.527 ppm versus median = 0.279 ppm, range = 0.593 ppm). CONCLUSION: Extreme iron overload amplifies variability in R2* and QSM measurements. QSM referencing is particularly challenging in diffuse whole-brain iron accumulation; thus, analysis with multiple reference regions might mitigate bias. Both algorithm selection and referencing approaches play a pivotal role in determining measurement accuracy and clinical interpretation under extreme brain iron overload. RELEVANCE STATEMENT: As QSM transitions into clinical use, it will encounter cases of extreme iron overload. Our study in patients with aceruloplasminemia revealed that the choice of reference region significantly influences susceptibility values, with variations exceeding algorithm-dependent differences. KEY POINTS: R2* and QSM vary across algorithms in brain tissue with iron overload. Whole-brain referenced QSM leads to lower susceptibility values in aceruloplasminemia patients. QSM, if properly processed, provides reliable maps in iron overload brain regions. In brain regions with extremely high iron content, R2* mapping might fail.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.306
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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