European radiology
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To qualitatively and quantitatively compare the image quality between single-shot echo-planar (SS-EPI) and multi-shot echo-planar (IMS-EPI) diffusion-weighted imaging (DWI) in female pelvis METHODS: This was a prospective study involving 80 females who underwent 3.0T pelvic magnetic resonance imaging (MRI). SS-EPI and IMS-EPI DWI were acquired with 3 b values (0, 400, 800 s/mm2). Two independent reviewers assessed the overall image quality, artifacts, sharpness, and lesion conspicuity based on a 5-point Likert scale. Regions of interest (ROI) were placed on the endometrium and the gluteus muscles to quantify the signal intensities and apparent diffusion coefficient (ADC). Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion were quantified on both sequences. Inter-rater agreement was assessed using κ statistics and Kendall test. Qualitative scores were compared using Wilcoxon signed-rank test and quantitative parameters were compared with paired t test and Bland-Altman analysis. ⋯ • IMS-EPI showed better image quality than SS-EPI. • IMS-EPI showed lower geometric distortion without affecting ADC compared with SS-EPI. • The SNR and CNR of IMS-EPI decreased due to post-processing limitations.
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Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. ⋯ • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.
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To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. ⋯ • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.
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To determine whether diffusion- and perfusion-weighted MRI-based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) METHODS: Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning-based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28). ⋯ • The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.
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The objectives were to survey MR safety incidents in Sweden during a 12-month period, to assess severity scores, and to evaluate the confidence of MR personnel in incident-reporting mechanisms. ⋯ • Safety incidents in clinical MR environments still result in human injury and material damage. • Severity level of MR incidents-assessed using Swedish national risk assessment recommendations-is high. • Confidence of MR personnel in incident-reporting mechanisms is high, but reflects a false sense of security, as a high proportion of staff is unaware of reported incidents in the same workplace.