Computer-aided quantification of non-contrast 3D black blood MRI as an efficient alternative to reference standard manual CT angiography measurements of abdominal aortic aneurysms.


Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Jan 2021
Historique:
received: 29 07 2020
revised: 12 10 2020
accepted: 02 11 2020
pubmed: 21 11 2020
medline: 15 4 2021
entrez: 20 11 2020
Statut: ppublish

Résumé

Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA). Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson correlation and Bland-Altman analyses. Finally, automated measurements from non-contrast 3D black blood MRI were evaluated against manual CTA measurements using the Wilcoxon test, Pearson correlation and Bland-Altman analyses. Semi-automatic segmentations had excellent agreement with manual segmentations (lumen DSC: 0.91 ± 0.03 and 0.94 ± 0.03; total aneurysm DSC: 0.92 ± 0.02 and 0.94 ± 0.03, for black blood MRI and CTA, respectively). Automated volume and maximum diameter measurements also had excellent correlation to their manual counterparts for both black blood MRI (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001) and CTA (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001). Compared to manual CTA measurements, bias and limits of agreement (LOA) for automated MRI measurements (lumen volume: 1.49, [-4.19 7.17] cm Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.

Sections du résumé

BACKGROUND BACKGROUND
Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management.
PURPOSE OBJECTIVE
To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA).
MATERIALS AND METHODS METHODS
Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson correlation and Bland-Altman analyses. Finally, automated measurements from non-contrast 3D black blood MRI were evaluated against manual CTA measurements using the Wilcoxon test, Pearson correlation and Bland-Altman analyses.
RESULTS RESULTS
Semi-automatic segmentations had excellent agreement with manual segmentations (lumen DSC: 0.91 ± 0.03 and 0.94 ± 0.03; total aneurysm DSC: 0.92 ± 0.02 and 0.94 ± 0.03, for black blood MRI and CTA, respectively). Automated volume and maximum diameter measurements also had excellent correlation to their manual counterparts for both black blood MRI (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001) and CTA (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001). Compared to manual CTA measurements, bias and limits of agreement (LOA) for automated MRI measurements (lumen volume: 1.49, [-4.19 7.17] cm
CONCLUSION CONCLUSIONS
Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.

Identifiants

pubmed: 33217686
pii: S0720-048X(20)30586-6
doi: 10.1016/j.ejrad.2020.109396
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109396

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Yan Wang (Y)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Bing Tian (B)

Department of Radiology, Changhai Hospital, Shanghai, China. Electronic address: bing.tian@hotmail.com.

Fei Xiong (F)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Evan Kao (E)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Yue Zhang (Y)

Department of Surgery, University of California San Francisco, California, United States.

Xinke Liu (X)

Capital Medical University, Beijing Tiantan Hospital, Beijing, China.

Xia Tian (X)

Department of Radiology, Changhai Hospital, Shanghai, China.

Henrik Haraldsson (H)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Chengcheng Zhu (C)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Joseph Leach (J)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Jing Liu (J)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Michael D Hope (MD)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

Dimitrios Mitsouras (D)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

David Saloner (D)

Department of Radiology and Biomedical Imaging, University of California San Francisco, United States.

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