Congenital anomalies of the aortic arch include diverse subgroups of malformations that may be clinically silent or may present with severe respiratory or esophageal symptoms especially when associated with complete vascular rings. These anomalies may be isolated or may be associated with other congenital heart diseases. Volume rendered computed tomography (CT) and magnetic resonance angiography (MRA) help in preoperative surgical planning by providing information about the complex relationship of aortic arch and its branches to the trachea and esophagus. Three dimensional capabilities of both computed tomography angiography (CTA) and MRA are helpful in determining evidence of tracheal or esophageal compression or other high-risk features in patients with a complete vascular ring.

SUMMARY: Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging.

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

Radiologists with a basic understanding of neural crest development can identify many seemingly unrelated diseases as neurocristopathies and thus be positioned to have important roles in the diagno... The neural crest is an important transient structure that develops during embryogenesis in vertebrates. Neural crest cells are multipotent progenitor cells that migrate and develop into a diverse r...