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Record W131175756

Use of SPECT-CT for specific dose calculations based on accurate quantitative measurements of activity distribution

2007· article· en· W131175756 on OpenAlexaff
Sergey Shcherbinin, A. Ćeller, Albert A. Driedger, Tarik Belhocine, R. VanderWerf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsWestern UniversityUniversity of British Columbia
Fundersnot available
KeywordsNuclear medicineImaging phantomCollimatorCorrection for attenuationAttenuationPhysicsMathematicsMedicinePositron emission tomographyOptics
DOInot available

Abstract

fetched live from OpenAlex

529 Objectives: Determination of patient specific dose for optimized radiotherapy requires accurate quantitation of activity distribution. We used SPECT-CT data to determine the levels of quantitative accuracy achievable for Tc-99m, I-123, I-131 and In-111 after attenuation correction (AC), scatter correction (SC), and resolution recovery (RR). Methods: Two hot sources (32ml bottles) with identical activities were placed at different depths in the thorax phantom (Data Spectrum Corp.) filled with cold and active water. The experimental data were acquired on an integrated SPECT-CT (Infinia-Hawkeye-4 and Infinia-Hawkeye, GE Healthcare) according to a clinical protocol (99mTc-MDP bone scan, 131/123I-MIBG, and 111In-Octreoscan). For I-123 and I-131 data, a second energy window was acquired for high energy cross-talk and collimator septal penetration correction (SPC). Images were reconstructed with OSEM using 4 iterations and 10 subsets. Data reconstructions with 2D-RR, 3D-RR, 3D-RR+AC, 3D-RR+AC+SPC and 3D-RR+AC+SPC+SC were performed using our qSPECT code, and were compared to partially corrected GE reconstructions (i.e. w/w-o AC). In this preliminary analysis the relative difference between the numbers of counts in each bottle was used as a measure of quantitative accuracy of the reconstruction. Results: Fully-corrected reconstructions showed much improved quantitation. In one example study, the relative errors decreased from 95%, 33%, 23% and 10% when no corrections were applied to 6%, 12%, 11% and 8% with the comprehensive set of corrections for Tc-99m, I-123, In-111 and I-131, respectively. Conclusions: For quantitation of activity distribution, the best level of accuracy was achieved when all corrections were applied. Accordingly, SPECT-CT opens promising avenues for patient specific dosimetry. The analysis of relative importance of corrections and absolute quantitation are currently being performed.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.274
GPT teacher head0.427
Teacher spread0.152 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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