Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Abstract

ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).ResultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

“…Magnetic resonance images undergo of some intrinsic acquisition artifacts, and different scanners with various acquisition parameters, and protocols, severely impeded the multicenter applicability of MRI‐based radiomics, the image preprocessing takes an essential part in the facilitating quantification analysis and make more repeatable and comparable results. However, the lack of pre‐specific image standardization methods in the mMRI‐based radiomics analysis may also affect the reproducibility and reliability of the features.…”

“…Given that MRI undergoes of various inherent acquisition artifacts and noises such as lack of standard intensity for inter‐ and intra‐scanner variability even for the same protocol, body region, and patient; intensity non‐uniformity as a result of reduced radio frequency, coil uniformity, nonlinear fields, gradient field, magnetic field, and etc. ; image preprocessing method suchlike intensity normalization, bias field correction, and noise smoothing can facilitate quantitative MRI analysis and make the radiomics results more repeatable and comparable . Currently, many attentions of the GBM mMRI‐based radiomics studies were drowned to prognosis and prediction model, while they have not used a pre‐specific image preprocessing pipeline.…”

Editorial for “Texture Analysis of High b‐value Diffusion‐Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas”

“…Each component of the workflow is important, and a lack of standardization limits reproducibility . For example, it has been shown that variations in image acquisition and reconstruction parameters lead to inconsistent findings between different datasets . An additional obstacle is the lack of standardization for segmentation, as it may be performed manually, automatically, or semiautomatically.…”

Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization

“…Pooling data from different centers is necessary, but it runs the risk of introducing bias to the values of texture features. Indeed, each step of the radiomics process can introduce variability independently from the intrinsic heterogeneity of the tumor; for instance: MRI field strength, manufacturers, coils, acquisition parameters, segmentation, voxel‐size resampling, normalization techniques, or gray‐level discretization . Previous studies have demonstrated that temporal parameters (ie, scan duration and temporal resolution) could significantly modify the ability to discriminate benign from malignant prostate or breast lesions, but they were based on average values of DCE‐MRI indices or morphology of the time–intensity curves.…”

“…Indeed, each step of the radiomics process can introduce variability independently from the intrinsic heterogeneity of the tumor; for instance: MRI field strength, manufacturers, coils, acquisition parameters, segmentation, voxel-size resampling, normalization techniques, or gray-level discretization. [20][21][22][23] Previous studies have demonstrated that temporal parameters (ie, scan duration and temporal resolution) could significantly modify the ability to discriminate benign from malignant prostate or breast lesions, [24][25][26] but they were based on average values of DCE-MRI indices or morphology of the time-intensity curves. Only one study has focused on the stability of texture features extracted from computed tomography (CT) perfusion maps identifying an influence of temporal resolution.…”