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Record W2086156696 · doi:10.1118/1.3551990

Experimental validation of a method characterizing bow tie filters in CT scanners using a real‐time dose probe

2011· article· en· W2086156696 on OpenAlexaff
Sarah E. McKenney, Anita Nosratieh, Dale E. Gelskey, Kai Yang, Lin Chen, John M. Boone

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsShared Health
FundersNational Institute of Biomedical Imaging and BioengineeringUniversity of California, DavisNational Institutes of Health
KeywordsScannerFilter (signal processing)AttenuationSIGNAL (programming language)OpticsDosimeterAcousticsComputer sciencePhysicsRadiationComputer vision

Abstract

fetched live from OpenAlex

PURPOSE: Beam-shaping or "bow tie" (BT) filters are used to spatially modulate the x-ray beam in a CT scanner, but the conventional method of step-and-shoot measurement to characterize a beam's profile is tedious and time-consuming. The theory for characterization of bow tie relative attenuation (COBRA) method, which relies on a real-time dosimeter to address the issues of conventional measurement techniques, was previously demonstrated using computer simulations. In this study the feasibility of the COBRA theory is further validated experimentally through the employment of a prototype real-time radiation meter and a known BT filter. METHODS: The COBRA method consisted of four basic steps: (1) The probe was placed at the edge of a scanner's field of view; (2) a real-time signal train was collected as the scanner's gantry rotated with the x-ray beam on; (3) the signal train, without a BT filter, was modeled using peak values measured in the signal train of step 2; and (4) the relative attenuation of the BT filter was estimated from filtered and unfiltered data sets. The prototype probe was first verified to have an isotropic and linear response to incident x-rays. The COBRA method was then tested on a dedicated breast CT scanner with a custom-designed BT filter and compared to the conventional step-and-shoot characterization of the BT filter. Using basis decomposition of dual energy signal data, the thickness of the filter was estimated and compared to the BT filter's manufacturing specifications. The COBRA method was also demonstrated with a clinical whole body CT scanner using the body BT filter. The relative attenuation was calculated at four discrete x-ray tube potentials and used to estimate the thickness of the BT filter. RESULTS: The prototype probe was found to have a linear and isotropic response to x-rays. The relative attenuation produced from the COBRA method fell within the error of the relative attenuation measured with the step-and-shoot method. The BT filter thickness estimates resulting from the dual energy scans on the breast CT system were equivalent to the manufacturing specifications. The clinical CT evaluation produced data conceptually similar to previous computer simulations and plausible relative attenuation profiles were observed. CONCLUSIONS: The COBRA method is a fast and accurate method for BT filter characterization, which requires a simple experimental setup in a clinical environment. Because of the ease of data acquisition, multienergy scans can be acquired which allow characterization of the BT filter thickness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.343
Teacher spread0.288 · 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.

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

Citations61
Published2011
Admission routes1
Has abstractyes

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