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Record W2077613656 · doi:10.1002/jmri.22129

Overestimation of cerebral aneurysm wall thickness by black blood MRI?

2010· letter· en· W2077613656 on OpenAlexafffundabout
David A. Steinman, Luca Antiga, Bruce A. Wasserman

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2010
Typeletter
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of Toronto
FundersIstituto di Ricerche Farmacologiche Mario Negri - IRCCSUniversity of TorontoJohns Hopkins University
KeywordsAneurysmMedicineLumen (anatomy)Materials scienceNuclear medicineRadiologyAnatomyGeologySurgery

Abstract

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Recently, Park et al (1) reported unruptured saccular cerebral aneurysm (USCA) wall thicknesses on the order of 0.5 mm using a black blood double-inversion (BBDI) protocol having an in-plane resolution of 0.5 mm and slice thickness of 3 mm. Early in their discussion of the results the authors concede that their measurements were greater than the “20–500 micron” (p. 1181) wall thicknesses reported by specimen-based studies and attribute this to “different state of the aneurysms between [their] study (in vivo and unruptured state) and previous studies (ex vivo and ruptured state) (p. 1182).” Later, in discussing the potential limitations of their study, they add that their measured wall thicknesses were “similar to the pixel size of the BBDI; therefore, there might have been measurement errors (p. 1183).” In fact, there is a good chance those measurement errors, rather than differences in the states of the aneurysms, are at the root of the discrepancy between the MRI- and specimen-based thickness measurements. Referring to fig. 8 of our recent study on wall thickness (2) overestimation, an aneurysmal wall having a true thickness of, say, 250 μm would, at 0.5 mm acquired resolution, appear to have a thickness closer to 0.7 mm. That the authors report 0.5–0.6 mm wall thickness suggests that ours is likely the worst-case scenario, since it ignores the potentially salutary influence of surrounding tissue, and of operator judgment in manual segmentation vs. our automated edge detection. Nevertheless, it seems likely that the BBDI protocol used by the authors has served to overestimate the USCA wall thickness by more than they imply. Moreover, as we (2) and others (3) have also shown, such overestimation can be exacerbated by thick slice acquisitions when anatomical complexity and wall curvature may introduce obliqueness between the wall and slice plane, such as might be the case for smaller aneurysms. This effect only gets worse if slice thickness is sacrificed for higher in-plane resolution. As such, the authors are correct to conclude, “near isotropic resolution with 1024 matrices may be necessary for evaluation of the entire USCA wall (p. 1183).” However, what they fail to mention is that this will require at least an order-of-magnitude reduction in voxel volume compared to their current acquisition. While not wishing to discourage the noninvasive measurement of cerebral aneurysm wall thickness, we feel that a stronger message regarding the potential for severe overestimation due to partial volume effects is warranted in light of the spatial resolutions currently achievable, even at 3 Tesla (4). We do encourage the authors and others to carry out ex vivo imaging studies to demonstrate the true capabilities and limitations of MRI for resolving the walls of USCA. David A. Steinman PhD [email protected]*, Luca Antiga PhD , Bruce A. Wasserman MD , * Biomedical Simulation Laboratory Department of Mechanical & Industrial Engineering University of Toronto Toronto, ON, Canada, Biomedical Engineering Department Mario Negri Institute for Pharmacological Research Ranica (BG), Italy, Department of Radiology Johns Hopkins School of Medicine Baltimore, Maryland, USA.

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)0.014
metaresearch head score (Gemma)0.063
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: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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
GenreCommentary

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

Citations11
Published2010
Admission routes3
Has abstractyes

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