Evidence map›Paper›PMID 33260634›Full record

ReviewMicromachines2020

Silicon 3D Microdetectors for Microdosimetry in Hadron Therapy.

Consuelo Guardiola, Celeste Fleta, David Quirion, Giulio Pellegrini, Faustino Gómez

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  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.

Consuelo GuardiolaUniversité Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France.
Celeste FletaCentro Nacional de Microelectrónica (IMB-CNM, CSIC), 08193 Bellaterra, Spain.
David QuirionCentro Nacional de Microelectrónica (IMB-CNM, CSIC), 08193 Bellaterra, Spain.
Giulio PellegriniCentro Nacional de Microelectrónica (IMB-CNM, CSIC), 08193 Bellaterra, Spain.ORCID 0000-0002-1606-3546
Faustino GómezDepartment de Física de Partículas, Universidad de Santiago de Compostela, 15782 Galicia, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present overview describes the evolution of new microdosimeters developed in the National Microelectronics Center in Spain (IMB-CNM, CSIC), ranging from the first ultra-thin 3D diodes (U3DTHINs) to the advanced 3D cylindrical microdetectors, which have been developed over the last 10 years. In this work, we summarize the design, main manufacture processes, and electrical characterization of these devices. These sensors were specifically customized for use in particle therapy and overcame some of the technological challenges in this domain, namely the low noise capability, well-defined sensitive volume, high spatial resolution, and pile-up robustness. Likewise, both architectures reduce the loss of charge carriers due to trapping effects, the charge collection time, and the voltage required for full depletion compared to planar silicon detectors. In particular, a 3D‒cylindrical architecture with electrodes inserted into the silicon bulk and with a very well‒delimited sensitive volume (SV) mimicked a cell array with shapes and sizes similar to those of mammalian cells for the first time. Experimental tests of the carbon beamlines at the Grand Accélérateur National d'Lourds (GANIL, France) and Centro Nazionale Adroterapia Oncologica (CNAO, Italy) showed the feasibility of the U3DTHINs in hadron therapy beams and the good performance of the 3D‒cylindrical microdetectors for assessing linear energy distributions of clinical beams, with clinical fluence rates of 5 × 10

Indexed as

hadron therapylinear energy transfer (LET)microdosimetersmicrodosimetry

Identifiers

PMID33260634
PMCPMC7760635

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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.