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Record W4324128674 · doi:10.1002/mp.16360

Simulation studies of a full‐ring, CZT SPECT system for whole‐body imaging of <sup>99m</sup>Tc and <sup>177</sup>Lu

2023· article· en· W4324128674 on OpenAlexaff
Yoonsuk Huh, J. Caravaca Rodríguez, Jae Hyuk Kim, Y. Cui, Qiu Huang, G.T. Gullberg, Youngho Seo

Bibliographic record

VenueMedical Physics · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersNuclear PhysicsNational Heart, Lung, and Blood InstituteOffice of ScienceNational Institute of Biomedical Imaging and BioengineeringU.S. Department of Energy
KeywordsCadmium zinc tellurideCollimatorDetectorPhysicsSpect imagingSingle-photon emission computed tomographyImaging phantomMonte Carlo methodCorrection for attenuationIterative reconstructionImage resolutionSensitivity (control systems)OpticsEmission computed tomographyScannerMedical imagingNuclear medicineMedical physicsAttenuationPositron emission tomographyComputer scienceArtificial intelligenceMedicineMathematics

Abstract

fetched live from OpenAlex

Abstract Background Single photon emission computed tomography (SPECT) is an imaging modality that has demonstrated its utility in a number of clinical indications. Despite this progress, a high sensitivity, high spatial resolution, multi‐tracer SPECT with a large field of view suitable for whole‐body imaging of a broad range of radiotracers for theranostics is not available. Purpose With the goal of filling this technological gap, we have designed a cadmium zinc telluride (CZT) full‐ring SPECT scanner instrumented with a broad‐energy tungsten collimator. The final purpose is to provide a multi‐tracer solution for brain and whole‐body imaging. Our static SPECT does not rely on the dual‐ and the triple‐head rotational SPECT standard paradigm, enabling a larger effective area in each scan to increase the sensitivity. We provide a demonstration of the performance of our design using a realistic model of our detector with simulated body‐sized phantoms filled with 99m Tc and 177 Lu. Methods We create a realistic model of our detector by using a combination of a Geant4 Application for Tomographic Emission (GATE) Monte Carlo simulation and a finite element model for the CZT response, accounting for low‐energy tail effects in CZT that affects the sensitivity and the scatter correction. We implement a modified dual‐energy‐window scatter correction adapted for CZT. Other corrections for attenuation, detector and collimator response, and detector gaps and edges are also included. The images are reconstructed using the maximum‐likelihood expectation‐maximization. Detector and reconstruction performance are characterized with point sources, Derenzo phantoms, and a body‐sized National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom for both 99m Tc and 177 Lu. Results Our SPECT design can resolve 7.9 mm rods for 99m Tc (140 keV) and 9.5 mm for 177 Lu (208 keV) in a hot‐rod Derenzo phantom with a 3‐min exposure and reach an image contrast of 78% for 99m Tc and 57% for 177 Lu using the NEMA IQ phantom with a 6‐min exposure. Our modified scatter correction shows an improved contrast‐recovery ratio compared to a standard correction. Conclusions In this paper, we demonstrate the good performance of our design for whole‐body imaging purposes. This adds to our previous demonstration of improved qualitative and quantitative 99m Tc imaging of brain perfusion and 123 I imaging of dopamine transport with respect to state‐of‐the‐art NaI dual‐head cameras. We show that our design provides similar IQ and contrast to the commercial full‐ring SPECT VERITON for 99m Tc. Regarding 177 Lu imaging of the 208 keV emissions, our design provides similar contrast to that of other state‐of‐the‐art SPECTs with a significant reduction in exposure. The high sensitivity and extended energy range up to 250 keV makes our SPECT design a promising alternative for clinical imaging and theranostics of emerging radionuclides.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.365
Teacher spread0.321 · 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 designSimulation or modeling
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

Citations11
Published2023
Admission routes1
Has abstractyes

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