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Record W1967263252 · doi:10.1118/1.3591990

Dependence of image quality on geometric factors in breast tomosynthesis

2011· article· en· W1967263252 on OpenAlexafffund
James G. Mainprize, Aili K. Bloomquist, Xinying Wang, Martin J. Yaffe

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Cancer InstituteCanadian Breast Cancer Research AllianceBreast Cancer Alliance
KeywordsOffset (computer science)Image qualityImaging phantomDetectorIterative reconstructionComputer visionStandard deviationTomosynthesisProjection (relational algebra)Artificial intelligenceMathematicsComputer scienceOpticsAlgorithmPhysicsMammographyImage (mathematics)MedicineStatistics

Abstract

fetched live from OpenAlex

PURPOSE: Accurate and precise knowledge of the geometric relationships between the physical components (x-ray source, pivot point, and elements of the x-ray detector) critically influences the quality of reconstructed images in digital breast tomosynthesis (DBT). The sensitivity of image reconstruction to geometric inaccuracies is investigated by simulation of image formation and reconstruction for a DBT system. METHODS: A mathematical simulation of a partial isocentric system is described. A block "phantom" containing small calcific particles is used to evaluate the effect of three linear and three angular parameters on localization of structures within the reconstructed image and on lesion contrast. Two types of geometric errors are studied: fixed offset inaccuracies and random interprojection inaccuracies in the context of a filtered back projection reconstruction algorithm. RESULTS: It is shown that, in general, fixed offset errors lead to little degradation of image quality. However, a lack of precision in interprojection geometric parameters can cause a loss in lesion contrast and introduce artifacts. For example, projection mismatches of the gantry angle of 0.14 degrees (standard deviation) can reduce reconstructed lesion intensity by 20%. Reconstruction is particularly sensitive to detector yaw angle mismatches; even small fixed offset errors (0.31 degrees) in detector yaw can reduce lesion intensity by 20%. Interprojection variations in geometric parameters can also cause localization errors. For example, if detector yaw variations between projections occur and these are not accounted for, a standard deviation of 0.34 degrees can be expected to induce 1 mm root-mean-square error shift in lesion location. CONCLUSIONS: In a simulation of image acquisition in DBT, the sensitivities in image quality to six geometric parameters were evaluated. Image reconstructions are relatively tolerant of fixed offset errors except for detector yaw. However, uncorrected variations in interprojection geometric parameters induce losses in lesion contrast and localization. Lesion contrast is affected more strongly by these errors compared to lesion localization in tomosynthesis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations14
Published2011
Admission routes2
Has abstractyes

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