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Record W2544463190 · doi:10.1088/0031-9155/61/22/7934

MR-compatibility of a high-resolution small animal PET insert operating inside a 7 T MRI

2016· article· en· W2544463190 on OpenAlexafffund
Jonathan D. Thiessen, Ehsan Shams, Greg Stortz, Graham Schellenberg, D. Bishop, Muhammad Salman Khan, Piotr Kozłowski, F. Retière, Vesna Sossi, Christopher J. Thompson, Andrew L. Goertzen

Bibliographic record

VenuePhysics in Medicine and Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsHealth Sciences CentreMontreal Neurological Institute and HospitalUniversity of British ColumbiaTRIUMFLawson Health Research InstituteUniversity of ManitobaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMFUniversity of ManitobaManitoba Health Research Council
KeywordsSilicon photomultiplierDetectorMaterials scienceLyso-ScannerElectromagnetic shieldingOpticsNuclear medicineNuclear magnetic resonancePhysicsScintillatorMedicine

Abstract

fetched live from OpenAlex

Abstract A full-ring PET insert consisting of 16 PET detector modules was designed and constructed to fit within the 114 mm diameter gradient bore of a Bruker 7 T MRI. The individual detector modules contain two silicon photomultiplier (SiPM) arrays, dual-layer offset LYSO crystal arrays, and high-definition multimedia interface (HDMI) cables for both signal and power transmission. Several different RF shielding configurations were assessed prior to construction of a fully assembled PET insert using a combination of carbon fibre and copper foil for RF shielding. MR-compatibility measurements included field mapping of the static magnetic field ( B 0 ) and the time-varying excitation field ( B 1 ) as well as acquisitions with multiple pulse sequences: spin echo (SE), rapid imaging with refocused echoes (RARE), fast low angle shot (FLASH) gradient echo, and echo planar imaging (EPI). B 0 field maps revealed a small degradation in the mean homogeneity (+0.1 ppm) when the PET insert was installed and operating. No significant change was observed in the B 1 field maps or the image homogeneity of various MR images, with a 9% decrease in the signal-to-noise ratio (SNR) observed only in EPI images acquired with the PET insert installed and operating. PET detector flood histograms, photopeak amplitudes, and energy resolutions were unchanged in individual PET detector modules when acquired during MRI operation. There was a small baseline shift on the PET detector signals due to the switching amplifiers used to power MRI gradient pulses. This baseline shift was observable when measured with an oscilloscope and varied as a function of the gradient duty cycle, but had no noticeable effect on the performance of the PET detector modules. Compact front-end electronics and effective RF shielding led to minimal cross-interference between the PET and MRI systems. Both PET detector and MRI performance was excellent, whether operating as a standalone system or a hybrid PET/MRI.

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)0.001
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.178
GPT teacher head0.401
Teacher spread0.223 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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