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: 3122
Function: getPubMedXML
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
Assessment of iron overload in liver by T2* magnetic resonance imaging (MRI) is a widely used clinical procedure. In the common clinical practice, measurement is performed locally by manually drawing a small region of interest in liver. This procedure may be affected by a noticeable intra- and inter-observer variability. In this study, a new approach is proposed that performs a global semiautomatic measurement of T2* involving the whole liver extension. Parenchyma is automatically segmented by an adaptive fuzzy-clustering algorithm. The liver T2* global value is evaluated using a pixel-wise approach by introducing an appropriate signal decay model. The proposed method was tested on a synthetic software model and on MR images acquired from 30 thalassemia major patients. The methods was demonstrated to increase the measure precision in T2* assessment and to significantly reduce the intra- and inter-observer variability.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1109/IEMBS.2007.4352934 | DOI Listing |
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