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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.

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Title: 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.
Authors: 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.
Source: BMC medical imaging [BMC Med Imaging] 2025 Apr 29; Vol. 25 (1), pp. 139. Date of Electronic Publication: 2025 Apr 29.
Publication Type: Journal Article
Language: English
Journal Info: 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-
MeSH Terms: 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
Abstract: 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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Zhonghua Yi Xue Za Zhi. 2023 May 23;103(19):1477-1482. (PMID: 37198110)
Radiology. 2018 Oct;289(1):111-118. (PMID: 29916772)
Eur J Radiol. 2015 Nov;84(11):2307-13. (PMID: 26212557)
Radiology. 2022 Apr;303(1):202-212. (PMID: 35040674)
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J Neuroradiol. 2016 Feb;43(1):1-5. (PMID: 26452610)
Acad Radiol. 2023 Nov;30(11):2666-2673. (PMID: 37758584)
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Eur Radiol. 2014 Jan;24(1):151-61. (PMID: 23995880)
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J Neuroradiol. 2012 Dec;39(5):342-5. (PMID: 22197402)
Eur J Radiol. 2022 Jan;146:110070. (PMID: 34856519)
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
Database: MEDLINE
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  Data: 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.
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  Data: &lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Tong+J%22&quot;&gt;Tong J&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Su+T%22&quot;&gt;Su T&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Chen+Y%22&quot;&gt;Chen Y&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China. bjchenyu@126.com.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Zhang+X%22&quot;&gt;Zhang X&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Yao+M%22&quot;&gt;Yao M&lt;/searchLink&gt;; Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Wang+Y%22&quot;&gt;Wang Y&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Liu+H%22&quot;&gt;Liu H&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Xu+M%22&quot;&gt;Xu M&lt;/searchLink&gt;; Canon Medical Systems (China), Building 205, Yard No. A 10, JiuXianQiao North Road, Chaoyang District, Beijing, 100015, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Wang+J%22&quot;&gt;Wang J&lt;/searchLink&gt;; Canon Medical Systems (China), Building 205, Yard No. A 10, JiuXianQiao North Road, Chaoyang District, Beijing, 100015, China.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Jin+Z%22&quot;&gt;Jin Z&lt;/searchLink&gt;; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, No.1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
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  Data: 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.&lt;br /&gt;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-DLR&lt;subscript&gt;tMIP&lt;/subscript&gt; and L-DLR&lt;subscript&gt;tAve&lt;/subscript&gt;. 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.&lt;br /&gt;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-DLR&lt;subscript&gt;tMIP&lt;/subscript&gt; demonstrated higher CT values in vessels compared to R-HIR (all P &amp;lt; 0.05). The CNR of vessels in L-HIR were statistically inferior to R-HIR (all P &amp;lt; 0.001). There was no significant different in image noise and CNR of vessels between L-DLR and R-HIR (all P &gt; 0.05, except P = 0.05 for CNR of ICAs, 77.19 &#177; 21.64 vs 73.54 &#177; 37.03). However, the L-DLR&lt;subscript&gt;tMIP&lt;/subscript&gt; and L-DLR&lt;subscript&gt;tAve&lt;/subscript&gt; presented lower image noise, higher CNR (all P &amp;lt; 0.05) and subjective scores (all P &amp;lt; 0.001) in vessels than R-HIR. The diagnostic accuracy in Group B was excellent (ICC = 0.944).&lt;br /&gt;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.&lt;br /&gt; (&#169; 2025. The Author(s).)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: 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.
– Name: Ref
  Label: References
  Group: RefInfo
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  Data: &lt;i&gt;Keywords: &lt;/i&gt;Cerebral arteries; Computed tomography angiography; Deep learning; Radiation dose reduction
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      – SubjectFull: Radiation Dosage
        Type: general
      – SubjectFull: Middle Aged
        Type: general
      – SubjectFull: Retrospective Studies
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Signal-To-Noise Ratio
        Type: general
      – SubjectFull: Adult
        Type: general
      – SubjectFull: Computed Tomography Angiography methods
        Type: general
      – SubjectFull: Radiographic Image Interpretation, Computer-Assisted methods
        Type: general
      – SubjectFull: Image Processing, Computer-Assisted methods
        Type: general
      – SubjectFull: Deep Learning
        Type: general
    Titles:
      – TitleFull: 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.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Tong J
      – PersonEntity:
          Name:
            NameFull: Su T
      – PersonEntity:
          Name:
            NameFull: Chen Y
      – PersonEntity:
          Name:
            NameFull: Zhang X
      – PersonEntity:
          Name:
            NameFull: Yao M
      – PersonEntity:
          Name:
            NameFull: Wang Y
      – PersonEntity:
          Name:
            NameFull: Liu H
      – PersonEntity:
          Name:
            NameFull: Xu M
      – PersonEntity:
          Name:
            NameFull: Wang J
      – PersonEntity:
          Name:
            NameFull: Jin Z
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 29
              M: 04
              Text: 2025 Apr 29
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1471-2342
          Numbering:
            – Type: volume
              Value: 25
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: BMC medical imaging
              Type: main
ResultId 1