MétaCan
Menu
Back to cohort
Record W4255934998 · doi:10.1109/nssmic.2001.1009680

Design of a fast shaping amplifier for PET/CT APD detectors with depth-of-interaction

2005· article· en· W4255934998 on OpenAlexafffund
J.-F. Pratte, C.M. Pepin, D. Rouleau, O. Menard, J. Mouïne, Roger Lecomte

Bibliographic record

Venue2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310) · 2005
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetectorPhysicsAmplifierCMOSTransconductanceBand-pass filterOpticsOptoelectronicsElectronic engineeringTransistorVoltageEngineering

Abstract

fetched live from OpenAlex

An integrated, 0.35 /spl mu/m CMOS fast shaping amplifier has been designed for coincidence detection and zero-cross time pulse shape discrimination (PSD) in PET, and for high rate event counting in CT with APD-based detectors. Analytical simulations of CR-RC/sup n/ filters of various order and shaping time constant were carried out to optimize the timing performance, keeping in mind the stringent channel density requirements of the detector front-end electronics. The filter was implemented with the biquadratic bandpass architecture using a folded cascode transconductance amplifier. The electronic timing resolution of the coincidence circuit was 92 ps in the ideal case without an APD (Cin=0pF), and 243 ps with an APD (Cin=30 pF). By comparison, the same system with the CMOS shaper replaced by an Ortec 579 Fast Filter Amplifier set to a shaping time of 10 ns yielded an electronic time resolution of 79 ps in the ideal case without capacitance, and 236 ps with the APD. The 511 keV coincidence time resolution for an LSO/APD detector was measured to be 1.49 ns.

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)0.000
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 score0.945
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.327
Teacher spread0.259 · 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

Citations1
Published2005
Admission routes2
Has abstractyes

Explore more

Same venue2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310)Same topicMedical Imaging Techniques and ApplicationsFrench-language works237,207