A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

A CNN-based four-layer DOI encoding detector using LYSO and BGO scintillators for small animal PET imaging. | LitMetric

. We propose a novel four-layer depth-of-interaction (DOI) encoding phoswich detector using lutetium-yttrium oxyothosilicate (LYSO) and bismuth germanate (BGO) scintillator crystal arrays for high sensitivity and high spatial resolution small animal PET imaging.. The detector was comprised of a stack of four alternating LYSO and BGO scintillator crystal arrays coupled to an 8 × 8 multi-pixel photon counter (MPPC) array and read out by a PETsys TOFPET2 application specific integrated circuit. The four layers from the top (gamma ray entrance) to the bottom (facing the MPPC) consisted of a 24 × 24 array of 0.99 × 0.99 × 6 mmLYSO crystals, a 24 × 24 array of 0.99 × 0.99 × 6 mmBGO crystals, a 16 × 16 array of 1.53 × 1.53 × 6 mmLYSO crystals and a 16 × 16 array of 1.53 × 1.53 × 6 mmBGO crystals.. Events that occurred in the LYSO and BGO layers were first separated by measuring the pulse energy (integrated charge) and duration (time over threshold (ToT)) from the scintillation pulses. Convolutional neural networks (CNNs) were then used to distinguish between the top and lower LYSO layers and between the upper and bottom BGO layers. Measurements with the prototype detector showed that our proposed method successfully identified events from all four layers. The CNN models achieved a classification accuracy of 91% for distinguishing the two LYSO layers and 81% for distinguishing the two BGO layers. The measured average energy resolution was 13.1% ± 1.7% for the top LYSO layer, 34.0% ± 6.3% for the upper BGO layer, 12.3% ± 1.3% for the lower LYSO layer, and 33.9% ± 6.9% for the bottom BGO layer. The timing resolution between each individual layer (from the top to the bottom) and a single crystal reference detector was 350 ps, 2.8 ns, 328 ps, and 2.1 ns respectively.. In conclusion, the proposed four-layer DOI encoding detector achieved high performance and is an attractive choice for next-generation high sensitivity and high spatial resolution small animal positron emission tomography systems.

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-6560/accc07DOI Listing

Publication Analysis

Top Keywords

doi encoding
12
lyso bgo
12
small animal
12
crystals array
12
bgo layers
12
four-layer doi
8
encoding detector
8
lyso
8
bgo
8
animal pet
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!