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Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis.
Su, Tong; Zhang, Zhe; Chen, Yu; Wang, Yun; Li, Yumei; Xu, Min; Wang, Jian; Li, Jing; Tian, Xinping; Jin, Zhengyu.
Affiliation
  • Su T; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Zhang Z; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Chen Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. bjchenyu@126.com.
  • Wang Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Li Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Xu M; CT Business Unit, Canon Medical Systems (China), Beijing, China.
  • Wang J; CT Business Unit, Canon Medical Systems (China), Beijing, China.
  • Li J; Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Di
  • Tian X; Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Di
  • Jin Z; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Korean J Radiol ; 25(4): 384-394, 2024 Apr.
Article de En | MEDLINE | ID: mdl-38528696
ABSTRACT

OBJECTIVE:

To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize the cervical artery wall in patients with Takayasu arteritis (TAK). MATERIALS AND

METHODS:

This prospective study continuously recruited 53 patients with TAK (mean age 33.8 ± 10.2 years; 49 females) between January and July 2022 who underwent head-neck CTA scans. The arterial- and delayed-phase images were reconstructed using HIR and DLR. Subtracted images of the arterial-phase from the delayed-phase were then added to the original delayed-phase using a denoising filter to generate the final-dark-blood images. Qualitative image quality scores and quantitative parameters were obtained and compared among the three groups of images Delayed-HIR, Dark-blood-HIR, and Dark-blood-DLR.

RESULTS:

Compared to Delayed-HIR, Dark-blood-HIR images demonstrated higher qualitative scores in terms of vascular wall visualization and diagnostic confidence index (all P < 0.001). These qualitative scores further improved after applying DLR (Dark-blood-DLR compared to Dark-blood-HIR, all P < 0.001). Dark-blood DLR also showed higher scores for overall image noise than Dark-blood-HIR (P < 0.001). In the quantitative analysis, the contrast-to-noise ratio (CNR) values between the vessel wall and lumen for the bilateral common carotid arteries and brachiocephalic trunk were significantly higher on Dark-blood-HIR images than on Delayed-HIR images (all P < 0.05). The CNR values were significantly higher for Dark-blood-DLR than for Dark-blood-HIR in all cervical arteries (all P < 0.001).

CONCLUSION:

Compared with Delayed-HIR CTA, the dark-blood method combined with DLR improved CTA image quality and enhanced visualization of the cervical artery wall in patients with TAK.
Sujet(s)
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Maladie de Takayashu / Apprentissage profond Limites: Adult / Female / Humans Langue: En Journal: Korean J Radiol / Korean j. radiol / Korean journal of radiology Sujet du journal: RADIOLOGIA Année: 2024 Type de document: Article Pays d'affiliation: Chine Pays de publication:

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Maladie de Takayashu / Apprentissage profond Limites: Adult / Female / Humans Langue: En Journal: Korean J Radiol / Korean j. radiol / Korean journal of radiology Sujet du journal: RADIOLOGIA Année: 2024 Type de document: Article Pays d'affiliation: Chine Pays de publication: