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Record W1990154939 · doi:10.1118/1.4735804

MO-E-217A-01: Contrast-Enhanced Spectral Mammography - Physical Aspects and QA

2012· article· en· W1990154939 on OpenAlexaff
Martin J. Yaffe, Melissa L. Hill

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMammographyQuality assuranceDigital mammographyImage qualityComputer scienceMedical physicsMedical imagingModality (human–computer interaction)DetectorMedicineBreast cancerComputer visionArtificial intelligenceCancerTelecommunicationsImage (mathematics)

Abstract

fetched live from OpenAlex

PURPOSE: To describe the current state of dual energy contrast-enhanced digital mammography, to discuss those aspects of its operation that require evaluation or monitoring and to propose elements of a program for quality assurance of such systems. METHODS: The principles of dual-energy contrast imaging will be discussed and tools and techniques for assessment of performance will be described. RESULTS: Many of the elements affecting image quality and dose performance in digital mammography (eg noise, system linearity, consistency of x-ray output and detector performance, artifacts) remain important. In addition, the ability to register images can influence the resultant image quality. The maintenance of breast compression thickness during the imaging procedure and calibration of the system to allow quantification of iodine in the breast represent new challenges to quality assurance. CONCLUSION: CESM provides a means of acquiring new information regarding tumor angiogenesis and may reveal some cancers that will not be detectable on digital mammography. It may also better demonstrate the extent of disease. The medical physicist must understand the dependence of image quality on physical factors. Implementation of a relevant QA program will be required if the promise of this new modality is to be delivered.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.875

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 studies0.0000.001
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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