Evidence map›Paper›PMID 30204125›Full record

ArticlePhysics in medicine and biology2018

Shared-photodetector readout to improve the sensitivity of positron emission tomography.

Junwei Du, Peng Peng, Xiaowei Bai, Simon R Cherry

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

4 authors.

Junwei DuDepartment of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, United States of America.
Peng PengDepartment of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, United States of America.
Xiaowei BaiDepartment of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, United States of America.
Simon R CherryDepartment of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, United States of America.

Funding

Innovative Silicon Photomultiplier Technologies for Small-Animal PETR01EB019439 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHERRY, SIMON R · 2015 to 2018
$1.4M
NIBIB NIH HHS R01 EB019439
6 · The paper itself

Abstract

Sensitivity is an important performance characteristic of positron emission tomography (PET) systems. Improved sensitivity can be used to reduce injected dose, reduce scan time, or improve the signal-to-noise ratio and temporal resolution for dynamic studies. One way to improve the sensitivity of PET scanners is to reduce the gaps between detector modules. In this paper, a new signal processing method, named the shared-photodetector readout method, is proposed and evaluated. In this method, the signals generated in nearest neighbor photodetectors adjacent to the detector module of interest, were used to help identify the interaction location in the detector module of interest. Using this method, scintillator array-based detector modules with almost 100% packing fraction can be built, and the edge crystals can be clearly resolved, even when the crystals are small compared to the photodetector size. To evaluate this signal processing concept in one dimension, a detector block with four dual-ended readout detector modules, was designed. The detector block consisted of eight 4  ×  4 arrays of SensL MicroFJ-30035 SiPMs coupled to both ends of a 14  ×  56 array of 0.9  ×  0.9  ×  20 mm

Indexed as

Signal-To-Noise RatioImage Processing, Computer-AssistedPositron-Emission Tomography

Identifiers

PMID30204125
PMCPMC6234418

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Registered trials

None linked

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.