Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
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Unlabelled: A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using F-FDG, Ga-DOTATATE, and F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT).
Methods: Clinical whole-body PET/CT datasets of F-FDG (N = 113), Ga-DOTATATE (N = 76), and F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEM) and µ-MLAA (OSEM) were compared to the CT-based reconstruction (OSEM). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics.
Results: µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEM as the gold-standard, OSEM provided more accurate tumor quantification than OSEM for all three tracers, e.g., error in SUV for OSEM vs. OSEM: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for F-Fluciclovine (N = 44). OSEM also yielded more accurate tumor volume measures than OSEM, i.e., - 8.4 ± 14.5% (OSEM) vs. - 3.0 ± 15.0% for F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for F-Fluciclovine.
Conclusions: The proposed framework provides accurate and robust attenuation correction for whole-body F-FDG, Ga-DOTATATE and F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10725742 | PMC |
http://dx.doi.org/10.1007/s00259-022-05748-2 | DOI Listing |