Segmentation of left ventricle in late gadolinium enhanced MRI through 2D-4D registration for infarct localization in 3D patient-specific left ventricular model.

Autor: Leong CO; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia., Lim E; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia., Tan LK; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia., Abdul Aziz YF; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia., Sridhar GS; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Socrates D; Department of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia., Chee KH; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Lee ZV; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Liew YM; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Jazyk: angličtina
Zdroj: Magnetic resonance in medicine [Magn Reson Med] 2019 Feb; Vol. 81 (2), pp. 1385-1398. Date of Electronic Publication: 2018 Sep 19.
DOI: 10.1002/mrm.27486
Abstrakt: Purpose: To evaluate a 2D-4D registration-cum-segmentation framework for the delineation of left ventricle (LV) in late gadolinium enhanced (LGE) MRI and for the localization of infarcts in patient-specific 3D LV models.
Methods: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.
Results: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.
Conclusion: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.
(© 2018 International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
Externí odkaz: