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Record W2520894707 · doi:10.1118/1.4962469

A modified TEW approach to scatter correction for In‐111 and Tc‐99m dual‐isotope small‐animal SPECT

2016· article· en· W2520894707 on OpenAlexafffund
Paul Prior, Rachel Timmins, Julia Petryk, Jared Strydhorst, Yin Duan, Lihui Wei, R. Glenn Wells

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsNordion (Canada)Ottawa Heart InstituteCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotonPhysicsOpticsIsotopePhoton energySingle-photon emission computed tomographyEnergy (signal processing)GaussianDetectorNuclear medicineNuclear physics

Abstract

fetched live from OpenAlex

PURPOSE: In dual-isotope (Tc-99m/In-111) small-animal single-photon emission computed tomography (SPECT), quantitative accuracy of Tc-99m activity measurements is degraded due to the detection of Compton-scattered photons in the Tc-99m photopeak window, which originate from the In-111 emissions (cross talk) and from the Tc-99m emission (self-scatter). The standard triple-energy window (TEW) estimates the total scatter (self-scatter and cross talk) using one scatter window on either side of the Tc-99m photopeak window, but the estimate is biased due to the presence of unscattered photons in the scatter windows. The authors present a modified TEW method to correct for total scatter that compensates for this bias and evaluate the method in phantoms and in vivo. METHODS: The number of unscattered Tc-99m and In-111 photons present in each scatter-window projection is estimated based on the number of photons detected in the photopeak of each isotope, using the isotope-dependent energy resolution of the detector. The camera-head-specific energy resolutions for the 140 keV Tc-99m and 171 keV In-111 emissions were determined experimentally by separately sampling the energy spectra of each isotope. Each sampled spectrum was fit with a Linear + Gaussian function. The fitted Gaussian functions were integrated across each energy window to determine the proportion of unscattered photons from each emission detected in the scatter windows. The method was first tested and compared to the standard TEW in phantoms containing Tc-99m:In-111 activity ratios between 0.15 and 6.90. True activities were determined using a dose calibrator, and SPECT activities were estimated from CT-attenuation-corrected images with and without scatter-correction. The method was then tested in vivo in six rats using In-111-liposome and Tc-99m-tetrofosmin to generate cross talk in the area of the myocardium. The myocardium was manually segmented using the SPECT and CT images, and partial-volume correction was performed using a template-based approach. The rat heart was counted in a well-counter to determine the true activity. RESULTS: In the phantoms without correction for Compton-scatter, Tc-99m activity quantification errors as high as 85% were observed. The standard TEW method quantified Tc-99m activity with an average accuracy of -9.0% ± 0.7%, while the modified TEW was accurate within 5% of truth in phantoms with Tc-99m:In-111 activity ratios ≥0.52. Without scatter-correction, In-111 activity was quantified with an average accuracy of 4.1%, and there was no dependence of accuracy on the activity ratio. In rat myocardia, uncorrected images were overestimated by an average of 23% ± 5%, and the standard TEW had an accuracy of -13.8% ± 1.6%, while the modified TEW yielded an accuracy of -4.0% ± 1.6%. CONCLUSIONS: Cross talk and self-scatter were shown to produce quantification errors in phantoms as well as in vivo. The standard TEW provided inaccurate results due to the inclusion of unscattered photons in the scatter windows. The modified TEW improved the scatter estimate and reduced the quantification errors in phantoms and in vivo.

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)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.311
Teacher spread0.265 · 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
GenreMethods

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

Citations8
Published2016
Admission routes2
Has abstractyes

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