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Record W2179179716 · doi:10.1109/rtc.2007.4382793

PESIC: An Integrated Front-End for PET Applications

2007· article· en· W2179179716 on OpenAlexaff
V. Herrero, Ricardo J. Colom, Á. Sebastiá, José M. Monzó, Christoph Lerche, J. Benlloch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsPreamplifierPhotomultiplierApplication-specific integrated circuitSilicon photomultiplierScintillatorComputer scienceFront and back endsDetectorAmplifierImage resolutionPhysicsOpticsCMOSElectronic engineeringOptoelectronicsComputer hardwareEngineering

Abstract

fetched live from OpenAlex

An ASIC front-end has been developed for multi-anode photomultiplier based nuclear imaging devices. Its architecture has been designed to improve resolution and decrease pile-up probability in Positron Emission Tomography systems which employ continuous scintillator crystals. Analog computation elements are isolated from the photomultiplier by means of a current sensitive preamplifier stage. This allows digitally programmable adjustment of every anode gain, also providing better resolution in gamma event position calculation and a shorter front-end deadtime. The preamplifier stage also offers the possibility of using other types of photomultiplier devices such as SiPM. The ASIC architecture includes measurement of the depth of interaction of the gamma event based on the width of the light distribution in order to reduce parallax error and increase spatial resolution during image reconstruction stage. An output stage of transresistance amplifiers offer voltage output signals which may be introduced in the A/D conversion stage with no further processing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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.

Study designNot applicable
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

Citations9
Published2007
Admission routes1
Has abstractyes

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