Evidence map›Paper›PMID 34859004›Full record

ArticleFrontiers in medicine2021

Reducing Calibration Time in PET Systems Based on Monolithic Crystals.

Marta Freire, Gabriel Cañizares, Sara Echegoyen, Andrea Gonzalez-Montoro, Antonio J Gonzalez

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Marta FreireInstituto de Instrumentación para Imagen Molecular, Centro Mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Gabriel CañizaresInstituto de Instrumentación para Imagen Molecular, Centro Mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Sara EchegoyenInstituto de Instrumentación para Imagen Molecular, Centro Mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Andrea Gonzalez-MontoroInstituto de Instrumentación para Imagen Molecular, Centro Mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Antonio J GonzalezInstituto de Instrumentación para Imagen Molecular, Centro Mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Instituto de Instrumentación para Imagen Molecular · ESUniversitat Politècnica de València · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past years, the gamma-ray detector designs based on the monolithic crystals have demonstrated to be excellent candidates for the design of high-performance PET systems. The monolithic crystals allow to achieve the intrinsic detector resolutions well below state-of-the-art; to increase packing fraction thus, increasing the system sensitivity; and to improve lesion detectability at the edges of the scanner field of view (FOV) because of their intrinsic depth of interaction (DOI) capabilities. The bottleneck to translate to the clinical PET systems based on a large number of monolithic detectors is eventually the requirement of mechanically complex and time-consuming calibration processes. To mitigate this drawback, several methods have been already proposed, such as using non-physically collimated radioactive sources or implementing the neuronal networks (NN) algorithms trained with simulated data. In this work, we aimed to simplify and fasten a calibration process of the monolithic based systems. The

Indexed as

calibrationmonolithic crystalspositron emission tomographytotal-body PETwhole-body PET

Identifiers

PMID34859004
PMCPMC8631296
OpenAlexW3214715472

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.