Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images. (2021)

• Record Type: Journal Article
• Authors: Frøysa, Vidar; Berg, Gøran J.; Eftestøl, Trygve; Woie, Leik; Ørn, Stein
• Is Part Of: European journal of radiology open. Volume 8(2021)
• Publication Date: 2021
• Subjects: CMR Cardiac magnetic resonance -- MI Myocardial infarction -- LGE Late gadolinium enhancement -- FWHM Full-width-half-maximum -- 5-SD 5 standard deviation -- LV Left ventricle -- TPM Texture-based probability mapping -- SI Signal intensity; Cardiac magnetic resonance. Late gadolinium enhancement. Myocardial infarction. Machine learning. Left ventricular remodeling; Medical radiology -- Periodicals; 616.075705
• DOI: 10.1016/j.ejro.2021.100387
• Languages: English
• ISSNs: 2352-0477

Abstract

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. Highlights: Texture based probability mapping can be used to evaluate myocardial scar size. The method can assess myocardial fibrosis independent of signal intensity. The TPM method shows strong correlations between scar size and left ventricular ejection fraction.