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Record W1994365858 · doi:10.1118/1.2898189

A dual modality phantom for cone beam CT and ultrasound image fusion in prostate implant

2008· article· en· W1994365858 on OpenAlexaff
Angela Ng, Akbar Beiki‐Ardakan, Shidong Tong, D Moseley, Jeffrey H. Siewerdsen, David A. Jaffray, Ivan Yeung

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsFiducial markerImaging phantomCone beam computed tomographyProstate brachytherapyUltrasoundNuclear medicineMedicine3D ultrasoundImage registrationImage-guided radiation therapyImage fusionMedical imagingBrachytherapyBiomedical engineeringRadiologyComputer scienceRadiation therapyArtificial intelligenceComputed tomographyImage (mathematics)

Abstract

fetched live from OpenAlex

In transrectal ultrasound (TRUS) guided prostate seed brachytherapy, TRUS provides good delineation of the prostate while x-ray imaging, e.g., C-arm, gives excellent contrast for seed localization. With the recent availability of cone beam CT (CBCT) technology, the combination of the two imaging modalities may provide an ideal system for intraoperative dosimetric feedback during implantation. A dual modality phantom made of acrylic and copper wire was designed to measure the accuracy and precision of image coregistration between a C-arm based CBCT and 3D TRUS. The phantom was scanned with TRUS and CBCT under the same setup condition. Successive parallel transverse ultrasound (US) images were acquired through manual stepping of the US probe across the phantom at an increment of 1 mm over 7.5 cm. The CBCT imaging was done with three reconstructed slice thicknesses (0.4, 0.8, and 1.6 mm) as well as at three different tilt angles (0 degrees, 15 degrees, 30 degrees), and the coregistration between CBCT and US images was done using the Variseed system based on four fiducial markers. Fiducial localization error (FLE), fiducial registration error (FRE), and target registration error (TRE) were calculated for all registered image sets. Results showed that FLE were typically less than 0.4 mm, FRE were less than 0.5 mm, and TRE were typically less than 1 mm within the range of operation for prostate implant (i.e., < 6 cm to surface of US probe). An analysis of variance test showed no significant difference in TRE for the CBCT-US fusion among the three slice thicknesses (p = 0.37). As a comparison, the experiment was repeated with a US-conventional CT scanner combination. No significant difference in TRE was noted between the US-conventional CT fusion and that for all three CBCT image slice thicknesses (p = 0.21). CBCT imaging was also performed at three different C-arm tilt angles of 0 degrees, 15 degrees and 30 degrees and reconstructed at a slice thickness of 0.8 mm. There is no significant difference in TRE between 0 degrees and 15 degrees (p = 0.191) as well as between 0 degrees and 30 degrees (p = 0.275), which suggests that the C-arm may be tilted intraoperatively to acquire CBCT images without compromising the quality of image fusion. The results conclude a high degree of accuracy and precision for the CBCT-TRUS fusion, which could be useful toward achieving real time intraoperative dosimetry in prostate brachytherapy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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 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

Citations5
Published2008
Admission routes1
Has abstractyes

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