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Record W2070048001 · doi:10.1118/1.4894730

Integration of optical imaging with a small animal irradiator

2014· article· en· W2070048001 on OpenAlexaff
Robert Weersink, Steve Ansell, An Wang, G. Wilson, Duoaud Shah, Patricia Lindsay, David A. Jaffray

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsImaging phantomOpticsFiducial markerMedical imagingCone beam computed tomographyMaterials sciencePhysicsComputer scienceComputer visionArtificial intelligenceMedicineComputed tomography

Abstract

fetched live from OpenAlex

PURPOSE: The authors describe the integration of optical imaging with a targeted small animal irradiator device, focusing on design, instrumentation, 2D to 3D image registration, 2D targeting, and the accuracy of recovering and mapping the optical signal to a 3D surface generated from the cone-beam computed tomography (CBCT) imaging. The integration of optical imaging will improve targeting of the radiation treatment and offer longitudinal tracking of tumor response of small animal models treated using the system. METHODS: The existing image-guided small animal irradiator consists of a variable kilovolt (peak) x-ray tube mounted opposite an aSi flat panel detector, both mounted on a c-arm gantry. The tube is used for both CBCT imaging and targeted irradiation. The optical component employs a CCD camera perpendicular to the x-ray treatment/imaging axis with a computer controlled filter for spectral decomposition. Multiple optical images can be acquired at any angle as the gantry rotates. The optical to CBCT registration, which uses a standard pinhole camera model, was modeled and tested using phantoms with markers visible in both optical and CBCT images. Optically guided 2D targeting in the anterior/posterior direction was tested on an anthropomorphic mouse phantom with embedded light sources. The accuracy of the mapping of optical signal to the CBCT surface was tested using the same mouse phantom. A surface mesh of the phantom was generated based on the CBCT image and optical intensities projected onto the surface. The measured surface intensity was compared to calculated surface for a point source at the actual source position. The point-source position was also optimized to provide the closest match between measured and calculated intensities, and the distance between the optimized and actual source positions was then calculated. This process was repeated for multiple wavelengths and sources. RESULTS: The optical to CBCT registration error was 0.8 mm. Two-dimensional targeting of a light source in the mouse phantom based on optical imaging along the anterior/posterior direction was accurate to 0.55 mm. The mean square residual error in the normalized measured projected surface intensities versus the calculated normalized intensities ranged between 0.0016 and 0.006. Optimizing the position reduced this error from 0.00016 to 0.0004 with distances ranging between 0.7 and 1 mm between the actual and calculated position source positions. CONCLUSIONS: The integration of optical imaging on an existing small animal irradiation platform has been accomplished. A targeting accuracy of 1 mm can be achieved in rigid, homogeneous phantoms. The combination of optical imaging with a CBCT image-guided small animal irradiator offers the potential to deliver functionally targeted dose distributions, as well as monitor spatial and temporal functional changes that occur with radiation therapy.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.316

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

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