Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model. Issue 2 (19th September 2018) - Leong, Chen Onn; Lim, Einly; Tan, Li Kuo; Abdul Aziz, Yang Faridah; Sridhar, Ganiga Srinivasaiah; Socrates, Dokos; Chee, Kok Han; Lee, Zhen‐Vin; Liew, Yih Miin
Abstract : 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.