Reliability of 3D texture analysis: A multicenter MRI study of the brain

Abstract

Background Texture analysis (TA) is an image‐analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown. Purpose To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC). Study Type Prospective multicenter data with harmonized MR sequence parameters acquired from five sites. Population Six healthy subjects. Field Strength 3T 3D‐MPRAGE and 3D‐SPGR T1‐weighted MRI of the brain. Assessment Voxel‐based 3D TA was performed on the whole brain to produce texture maps. Statistical Tests Intra‐ and intersite reliability of texture features was assessed using a two‐way mixed‐effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region‐of‐interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain. Results In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40–0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0–0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures. Data Conclusion Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI‐based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1200–1209.

Multicenter evaluation of MRI‐based radiomic features: A phantom study

“…Several important findings were made in this study. The phantom developed in this study has several advantages over previous test objects 15,[33][34][35][36][37][38][39] and can be used for radiomics analysis. It is stable and can be printed to create customized shapes and patterns.…”

The Canadian ALS Neuroimaging Consortium (CALSNIC) - a multicentre platform for standardized imaging and clinical studies in ALS

“…CALSNIC publications are forthcoming that report results from multicentre MRS, 15 DTI, 27 structural, 24 texture, 41,43 , and neuropathology data. The richness and extensiveness of the data provides a unique opportunity for advanced analytical methods (multivariate classifiers, AI) to help discriminate biological variability.…”

“…The travelling head images are available to derive scaling and corrective factors to transform data from sites into a common data space prior to analysis, and to assess inter-site and intra-site variance. The travelling head data was used for reliability assessments for MRS, 15 DTI, 27 and texture 41 modalities.…”

3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks

“…Ta et al mentioned that 3D texture analysis has the potential for the detection of cerebral degeneration which is not easily noticeable for the naked eye. They have found that 3D texture analysis has shown potential and enough reliability that it can be used for longitudinal studies [24].…”