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Record W3005313316 · doi:10.1055/s-0040-1701211

Evaluating an Image-Guided Operating Room with Cone Beam CT for Skull Base Surgery

2020· article· en· W3005313316 on OpenAlexaffabout
Nidal Muhanna, Catriona M. Douglas, Michael J. Daly, Harley Chan, Robert Weersink, Jason L. Townson, Eric Monteiro, Eugene Yu, Emilie Weimer, Walter Kucharczyk, David A. Jaffray, Jonathan C. Irish, John R. de Almeida

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCone beam computed tomographyFiducial markerSkullSoft tissueRadiologyNuclear medicineSurgeryComputed tomography

Abstract

fetched live from OpenAlex

Abstract Importance Skull base surgery requires precise preoperative assessment and intraoperative management of the patient. Surgical navigation is routinely used for complex skull base cases; however, the image guidance is commonly based on preoperative scans alone. Objective The primary objective of this study was to assess the image quality of intraoperative cone-beam computed tomography (CBCT) within anatomical landmarks used in sinus and skull base surgery. The secondary objective was to assess the registration error of a surgical navigation system based on intraoperative CBCT. Design Present study is a retrospective case series of image quality after intraoperative cone beam CT. Setting The study was conducted at Toronto General Hospital and Princess Margaret Cancer Centre, University Health Network, Toronto. Participants A total of 46 intraoperative scans (34 patients, 21 skull base, 13 head and neck) were studied. Main Outcome and Measures Thirty anatomical landmarks (vascular, soft tissue, and bony) within the sinuses and anterior skull base were evaluated for general image quality characteristics: (1) bony detail visualization; (2) soft-tissue visualization; (3) vascular visualization; and (4) freedom from artifacts (e.g., metal). Levels of intravenous (IV) contrast enhancement were quantified in Hounsfield's units (HU). Standard paired-point registration between imaging and tracker coordinates was performed using 6 to 8 skin fiducial markers and the corresponding fiducial registration error (FRE) was measured. Results Median score for bony detail on CBCT was 5, remaining at 5 after administration of IV contrast. Median soft-tissue score was 2 for both pre- and postcontrast. Median vascular score was 1 precontrast and 3 postcontrast. Median score for artifacts on CBCT were 2 for both pre-and postcontrast, and metal objects were noted to be the most significant source of artifact. Intraoperative CBCT allowed preresection images and immediate postresection images to be available to the skull base surgeon. There was a significant improvement in mean (standard deviation [SD]) CT intensity in the left carotid artery postcontrast 334 HU (67 HU) (p < 10−10). The mean FRE was 1.8 mm (0.45 mm). Conclusion Intraoperative CBCT in complex skull base procedures provides high-resolution bony detail allowing immediate assessment of complex resections. The use of IV contrast with CBCT improves the visualization of vasculature. Image-guidance based on CBCT yields registration errors consistent with standard techniques.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.125
GPT teacher head0.337
Teacher spread0.211 · 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 teacher head, not a consensus.

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

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

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