Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Abstract

Objectives Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain. Methods MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified. Results It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60–120 ms and TR = 6400 ms. Conclusion Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.

Texture Analysis in Cerebral Gliomas: A Review of the Literature

“…The impact of acquisition parameters on MRTA has been addressed in multiple studies. Ford et al, 69 using a digital 3D phantom, concluded that multiple texture features vary considerably between T1-weighted images (spin-echo, gradient echo, gradient recalled-echo, and inversion recovery) and T1 maps. They also noted that TR/TE variations on T1WI and T2WI affect texture features.…”

Effect of Methionine Diet on Metabolic and Histopathological Changes of Rat Hippocampus

“…Choline as a precursor of membrane metabolism is considered as a 1H MRS marker of membrane density, i.e., phospholipids synthesis and degradation [24]. Additionally, tCho is typically elevated during myelin sheet degradation [17,25] and an increase in tCho relative to tNAA and tCr was correlated with cerebral infarctions, ongoing gliosis, and ischemic and re/de-myelinization processes [19,25].…”

“…Additionally, tCho is typically elevated during myelin sheet degradation [17,25] and an increase in tCho relative to tNAA and tCr was correlated with cerebral infarctions, ongoing gliosis, and ischemic and re/de-myelinization processes [19,25]. It was recently shown that tCho was associated with membrane turnover that was directly related to Hcy removal [24], and a link was revealed between hHcy and ceramide metabolism in AD-type neurodegeneration [17,25,26]. In our experiments, we did not find significant changes in the levels of tCho and tCr in the hippocampus, but the rise in its ratio suggests for the proposed process of hippocampal re/de-remyelination, neuroglial dyshomeostasis, and cell membrane turnover in MDG conditions.…”

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.…”