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Record W1994792041 · doi:10.3171/2009.2.jns08828

Dynamic imaging of a model of intracranial saccular aneurysms using ultra-high-resolution flat-panel volumetric computed tomography

2009· article· en· W1994792041 on OpenAlexaff
Alim P. Mitha, Benjamin Reichardt, Michael Grasruck, Eric A. Macklin, Soenke H. Bartling, Christianne Leidecker, Bernhard Schmidt, Thomas Flohr, Thomas J. Brady, Christopher S. Ogilvy, Rajiv Gupta

Bibliographic record

VenueJournal of neurosurgery · 2009
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsFoothills Medical Centre
FundersBoston Scientific Corporation
KeywordsMedicineRadiologyClipping (morphology)AngiographyAneurysmArtifact (error)Image qualityComputed tomography angiographyDynamic imagingNuclear medicineImage processingComputer visionComputer science

Abstract

fetched live from OpenAlex

OBJECT: Imaging of intracranial aneurysms using conventional multidetector CT (MDCT) is limited because of nonvisualization of features such as perforating vessels, pulsatile blebs, and neck remnants after clip placement or coil embolization. In this study, a model of intracranial saccular aneurysms in rabbits was used to assess the ultra-high resolution and dynamic scanning capabilities of a prototype flat-panel volumetric CT (fpVCT) scanner in demonstrating these features. METHODS: Ten New Zealand white rabbits underwent imaging before and after clipping or coil embolization of surgically created aneurysms in the proximal right carotid artery. Imaging was performed using a prototype fpVCT scanner, a 64-slice MDCT scanner, and traditional catheter angiography. In addition to the slice data and 3D views, 4D dynamic views, a capability unique to fpVCT, were also created and reviewed. The images were subjectively compared on 1) 4 image quality metrics (spatial resolution, noise, motion artifacts, and aneurysm surface features); 2) 4 posttreatment features reflecting the metal artifact profile of the various imaging modalities (visualization of clip or coil placement, perianeurysmal clip/coil anatomy, neck remnant, and white-collar sign); and 3) 2 dynamic features (blood flow pattern and aneurysm pulsation). RESULTS: Flat-panel volumetric CT provided better image resolution than MDCT and was comparable to traditional catheter angiography. The surface features of aneurysms were demonstrated with much higher resolution, detail, and clarity by fpVCT compared with MDCT and angiography. Flat-panel volumetric CT was inferior to both MDCT and angiography in terms of image noise and motion artifacts. In fpVCT images, the metallic artifacts from clips and coils were significantly fewer than those in MDCT images. As a result, clinically important information about posttreatment aneurysm neck remnants could be derived from fpVCT images but not from MDCT images. Time-resolved dynamic sequences were judged slightly inferior to conventional angiography but superior to static MDCT images. CONCLUSIONS: The spatial resolution, surface anatomy visualization, metal artifact profile, and 4D dynamic images from fpVCT are superior to those from MDCT. Flat-panel volumetric CT demonstrates aneurysm surface features to better advantage than angiography and is comparable to angiography in metal artifact profile. Even though the temporal resolution of fpVCT is not quite as good as that of angiography, fpVCT images yield clinically important anatomical information about aneurysm surface features and posttreatment neck remnants not attainable with either angiography or MDCT images.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.260
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 designBench or experimental
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

Citations6
Published2009
Admission routes1
Has abstractyes

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