Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy

Abstract

The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist. Twelve histogram parameters and 5 gray-level co-occurrence matrix (GLCM) features were extracted during the TA. Differences in texture features between DCM patients and healthy controls were evaluated by t test. Support vector machine (SVM) was used to calculate the diagnostic accuracy of those texture parameters.Most histogram features were higher in the DCM group when compared to healthy controls, and 9 of these had significant differences between the DCM group and healthy controls. In terms of GLCM features, energy, correlation, and homogeneity were higher in the DCM group, when compared with healthy controls. In addition, entropy and contrast were lower in the DCM group. Moreover, entropy, contrast, and homogeneity had significant differences between these 2 groups. The diagnostic accuracy when using the SVM classifier with all these histogram and GLCM features was 0.85 ± 0.07.A computer-based TA and machine learning approach of T1 mapping can provide an objective tool for the diagnosis of DCM.

18F-FDG PET/CT Quantitative Parameters and Texture Analysis Effectively Differentiate Endometrial Precancerous Lesion and Early-Stage Carcinoma

“…Recent studies indicated that homogeneity, energy, and correlation were higher in patients with dilated cardiomyopathy than in healthy controls in terms of GLCM features, and the diagnostic accuracy using GLCM features was 0.85 ( 0.07). 32…”

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

“…In contrast, texture radiomic features allow the quantification of spatial inter-pixel relationships using more advanced matrix analysis methods (24,25). Through this, signal intensities patterns within the ROI may be numerically quantified using pre-agreed mathematical definitions.…”

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

“…al. [19] also shows that texture analysis of native T 1 maps can differentiate between DCM and control subjects. Similarly, Neisius, et.…”

“…For example, signal variation in cardiac MR images may contain additional information reflecting underlying pathophysiology [12][13][14] that is not being quantified. Radiomics [15] and texture image analysis have been recently applied to cardiac MR images [16][17][18][19][20] to extract new quantitative features that may provide diagnostic information. That is, radiomics quantitatively extract high-dimensional feature to differentiate images beyond mean signal value such as signal heterogeneity [17].…”