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Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma

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Abstract
Even a tiny functioning pituitary adenoma could cause symptoms; hence, accurate diagnosis and treatment are crucial for management. However, it is difficult to diagnose a small pituitary adenoma using conventional MR sequence. Deep learning-based reconstruction (DLR) using magnetic resonance imaging (MRI) enables high-resolution thin-section imaging with noise reduction. In the present single-institution retrospective study of 201 patients, conducted between August 2019 and October 2020, we compared the performance of 1 mm DLR MRI with that of 3 mm routine MRI, using a combined imaging protocol to detect and delineate pituitary adenoma. Four readers assessed the adenomas in a pairwise fashion, and diagnostic performance and image preferences were compared between inexperienced and experienced readers. The signal-to-noise ratio (SNR) was quantitatively assessed. New detection of adenoma, achieved using 1 mm DLR MRI, was not visualised using 3 mm routine MRI (overall: 6.5% [13/201]). There was no significant difference depending on the experience of the readers in new detections. Readers preferred 1 mm DLR MRI over 3 mm routine MRI (overall superiority 56%) to delineate normal pituitary stalk and gland, with inexperienced readers more preferred 1 mm DLR MRI than experienced readers. The SNR of 1 mm DLR MRI was 1.25-fold higher than that of the 3 mm routine MRI. In conclusion, the 1 mm DLR MRI achieved higher sensitivity in the detection of pituitary adenoma and provided better delineation of normal pituitary gland than 3 mm routine MRI.
Author(s)
이다현박지은남여경이준성김선옥김영훈김호성
Issued Date
2021
Type
Article
Keyword
AdenomaAdenoma - diagnostic imagingAdultAgedDeep LearningFemaleHumansImage ProcessingComputer-Assisted - methodsMagnetic resonance imagingMagnetic Resonance Imaging - methodsMaleMiddle AgedNoise reductionPituitaryPituitary Neoplasms - diagnostic imagingRetrospective StudiesTumors
DOI
10.1038/s41598-021-00558-2
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8485
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_b6938493378e49b8b3a8394da011f63c&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20learning-based%20thin-section%20MRI%20reconstruction%20improves%20tumour%20detection%20and%20delineation%20in%20pre-%20and%20post-treatment%20pituitary%20adenoma&offset=0&pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
미국
Language
영어
ISSN
2045-2322
Citation Volume
11
Citation Number
1
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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