Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images

Vidar Frøysa; Gøran J Berg; Trygve Eftestøl; Leik Woie; Stein Ørn

doi:10.1016/j.ejro.2021.100387

Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images

Eur J Radiol Open. 2021 Dec 3:8:100387. doi: 10.1016/j.ejro.2021.100387. eCollection 2021.

Authors

Vidar Frøysa¹, Gøran J Berg², Trygve Eftestøl², Leik Woie², Stein Ørn^{1

2}

Affiliations

¹ Department of Cardiology, Stavanger University Hospital, Armauer Hansens vei 20, 4011, Stavanger, Norway.
² Department of Electrical and Computer Science, University of Stavanger, P.O. box 8600, 4036 Stavanger, Norway.

Abstract

Purpose: To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI.

Methods: This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. The TPM was compared with three signal intensity-based methods: manual segmentation, full-width-half-maximum (FWHM), and 5-standard deviation (5-SD). TPM is generated using machine learning techniques, expressing the probability of scarring in pixels. The probability is derived by comparing the texture of the 3 × 3 pixel matrix surrounding each pixel with reference dictionaries from patients with established myocardial scars. The Sørensen-Dice coefficient was used to find the optimal TPM range. A non-parametric test was used to test the correlation between infarct size and remodeling parameters. Bland-Altman plots were performed to assess agreement among the methods.

Results: The study included 52 patients (76.9% male; median age 64.5 years (54, 72.5)). A TPM range of 0.328-1.0 was found to be the optimal probability interval to predict scar size compared to manual segmentation, median dice (25th and 75th percentiles)): 0.69(0.42-0.81). There was no significant difference in the scar size between TPM and 5-SD. However, both 5-SD and TPM yielded larger scar sizes compared with FWHM (p < 0.001 and p = 0.002). There were strong correlations between scar size measured by TPM, and left ventricular ejection fraction (LVEF, r = -0.76, p < 0.001), left ventricular end-diastolic volume index (r = 0.73, p < 0.001), and left ventricular end-systolic volume index (r = 0.75, p < 0.001).

Conclusion: The TPM method is comparable with current SI-based methods, both for the scar size assessment and the relationship with left ventricular remodeling when applied on LGE-CMRI.

Keywords: 5-SD, 5 standard deviation; CMR, Cardiac magnetic resonance; Cardiac magnetic resonance. Late gadolinium enhancement. Myocardial infarction. Machine learning. Left ventricular remodeling; FWHM, Full-width-half-maximum; LGE, Late gadolinium enhancement; LV, Left ventricle; MI, Myocardial infarction; SI, Signal intensity; TPM, Texture-based probability mapping.