Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.
Συγγραφείς:	Tong J; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Su T; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Chen Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China. bjchenyu@126.com., Zhang X; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Yao M; Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Wang Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Liu H; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China., Xu M; Canon Medical Systems (China), Building 205, Yard No. A 10, JiuXianQiao North Road, Chaoyang District, Beijing, 100015, China., Wang J; Canon Medical Systems (China), Building 205, Yard No. A 10, JiuXianQiao North Road, Chaoyang District, Beijing, 100015, China., Jin Z; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Πηγή:	BMC medical imaging [BMC Med Imaging] 2025 Apr 29; Vol. 25 (1), pp. 139. Date of Electronic Publication: 2025 Apr 29.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: BioMed Central Country of Publication: England NLM ID: 100968553 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2342 (Electronic) Linking ISSN: 14712342 NLM ISO Abbreviation: BMC Med Imaging Subsets: MEDLINE
Imprint Name(s):	Original Publication: London : BioMed Central, [2001-
Ιατρικοί όροι (MeSH):	Computed Tomography Angiography/methods , Radiographic Image Interpretation, Computer-Assisted/methods , Image Processing, Computer-Assisted/methods , Deep Learning, Humans ; Male ; Female ; Radiation Dosage ; Middle Aged ; Retrospective Studies ; Aged ; Signal-To-Noise Ratio ; Adult
Περίληψη:	Background: To assess the effect of the combination of deep learning reconstruction (DLR) and time-resolved maximum intensity projection (tMIP) or time-resolved average (tAve) post-processing method on image quality of CTA derived from low-dose cerebral CTP. Methods: Thirty patients underwent regular dose CTP (Group A) and other thirty with low-dose (Group B) were retrospectively enrolled. Group A were reconstructed with hybrid iterative reconstruction (R-HIR). In Group B, four image datasets of CTA were gained: L-HIR, L-DLR, L-DLRtMIP and L-DLRtAve. The CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective images quality were calculated and compared. The Intraclass Correlation (ICC) between CTA and MRA of two subgroups were calculated. Results: The low-dose group achieved reduction of radiation dose by 33% in single peak arterial phase and 18% in total compared to the regular dose group (single phase: 0.12 mSv vs 0.18 mSv; total: 1.91mSv vs 2.33mSv). The L-DLRtMIP demonstrated higher CT values in vessels compared to R-HIR (all P < 0.05). The CNR of vessels in L-HIR were statistically inferior to R-HIR (all P < 0.001). There was no significant different in image noise and CNR of vessels between L-DLR and R-HIR (all P > 0.05, except P = 0.05 for CNR of ICAs, 77.19 ± 21.64 vs 73.54 ± 37.03). However, the L-DLRtMIP and L-DLRtAve presented lower image noise, higher CNR (all P < 0.05) and subjective scores (all P < 0.001) in vessels than R-HIR. The diagnostic accuracy in Group B was excellent (ICC = 0.944). Conclusion: Combining DLR with tMIP or tAve allows for reduction in radiation dose by about 33% in single peak arterial phase and 18% in total in CTP scanning, while further improving image quality of CTA derived from CTP data when compared to HIR. (© 2025. The Author(s).)
Competing Interests:	Declarations. Ethics approval and consent to participate: Approval was granted by the ethics committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Ethics approval number: I-24PJ0479), which agreed to waive informed consent due to the retrospective nature of this study. All methods were carried out in accordance with the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
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Contributed Indexing:	Keywords: Cerebral arteries; Computed tomography angiography; Deep learning; Radiation dose reduction
Entry Date(s):	Date Created: 20250429 Date Completed: 20250430 Latest Revision: 20250502
Update Code:	20260130
PubMed Central ID:	PMC12042446
DOI:	10.1186/s12880-025-01623-2
PMID:	40301751
Βάση Δεδομένων:	MEDLINE

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ISSN:	1471-2342
DOI:	10.1186/s12880-025-01623-2