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Record W1990818742 · doi:10.1118/1.2799492

Image quality and localization accuracy in C‐arm tomosynthesis‐guided head and neck surgery

2007· article· en· W1990818742 on OpenAlexaff
Gil N. Bachar, J. H. Siewerdsen, Michael J. Daly, David A. Jaffray, Jonathan C. Irish

Bibliographic record

VenueMedical Physics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
FundersNational Cancer Institute
KeywordsTomosynthesisSagittal planeImage qualityCoronal planeImaging phantomComputer visionCone beam computed tomographyMedical imagingArtificial intelligenceComputer scienceNuclear medicineMedicineRadiologyMammographyComputed tomographyImage (mathematics)

Abstract

fetched live from OpenAlex

The image quality and localization accuracy for C-arm tomosynthesis and cone-beam computed tomography (CBCT) guidance of head and neck surgery were investigated. A continuum in image acquisition was explored, ranging from a single exposure (radiograph) to multiple projections acquired over a limited arc (tomosynthesis) to a full semicircular trajectory (CBCT). Experiments were performed using a prototype mobile C-arm modified to perform 3D image acquisition (a modified Siemens PowerMobil). The tradeoffs in image quality associated with the extent of the source-detector arc (theta(tot)), the number of projection views, and the total imaging dose were evaluated in phantom and cadaver studies. Surgical localization performance was evaluated using three cadaver heads imaged as a function of theta(tot). Six localization tasks were considered, ranging from high-contrast feature identification (e.g., tip of a K-wire pointer) to more challenging soft-tissue delineation (e.g., junction of the hard and soft palate). Five head and neck surgeons and one radiologist participated as observers. For each localization task, the 3D coordinates of landmarks pinpointed by each observer were analyzed as a function of theta(tot). For all tomosynthesis angles, image quality was highest in the coronal plane, whereas sagittal and axial planes exhibited a substantial decrease in spatial resolution associated with out-of-plane blur and distortion. Tasks involving complex, lower-contrast features demonstrated steeper degradation with smaller tomosynthetic arc. Localization accuracy in the coronal plane was correspondingly high, maintained to < 3 mm down to theta(tot) approximately 30 degrees, whereas sagittal and axial localization degraded rapidly below theta(tot) approximately 60 degrees. Similarly, localization precision was better than approximately 1 mm within the coronal plane, compared to approximately 2-3 mm out-of-plane for tomosynthesis angles below theta(tot) approximately 45 degrees. An overall 3D localization accuracy of approximately 2.5 mm was achieved with theta(tot) approximately 90 degrees for most tasks. The high in-plane spatial resolution, short scanning time, and low radiation dose characteristic of tomosynthesis may enable the surgeon to collect near real-time images throughout the procedure with minimal interference to surgical workflow. Therefore, tomosynthesis could provide a useful addition to the image-guided surgery arsenal, providing on-demand, high quality image updates, complemented by CBCT at critical milestones in the surgical procedure.

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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

Citations72
Published2007
Admission routes1
Has abstractyes

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