Quantifying Brain Iron in Hereditary Hemochromatosis Using R2* and Susceptibility Mapping
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Brain iron dyshomeostasis is increasingly recognized as an important contributor to neurodegeneration. Hereditary hemochromatosis is the most commonly inherited disorder of systemic iron overload. Although there is an increasing interest in excessive brain iron deposition, there is a paucity of evidence showing changes in brain iron exceeding that in healthy controls. Quantitative susceptibility mapping and R2* mapping are established MR imaging techniques that we used to noninvasively quantify brain iron in subjects with hereditary hemochromatosis. MATERIALS AND METHODS: Fifty-two patients with hereditary hemochromatosis and 47 age- and sex-matched healthy controls were imaged using a multiecho gradient-echo sequence at 3T. Quantitative susceptibility mapping and R2* data were generated, and regions within the deep gray matter were manually segmented. Mean susceptibility and R2* relaxation rates were calculated for each region, and iron content was compared between the groups. RESULTS: We noted elevated iron levels in patients with hereditary hemochromatosis compared with healthy controls using both R2* and QSM methods in the caudate nucleus, putamen, pulvinar thalamus, red nucleus, and dentate nucleus. Additionally, the substantia nigra showed increased susceptibility while the thalamus showed an increased R2* relaxation rate compared with healthy controls, respectively. CONCLUSIONS: Both quantitative susceptibility mapping and R2* showed abnormal levels of brain iron in subjects with hereditary hemochromatosis compared with controls. Quantitative susceptibility mapping and R2* can be acquired in a single MR imaging sequence and are complementary in quantifying deep gray matter iron.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".