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The aim of this study was to provide insights into myocardial adaptation over time in myocyte injury caused by acute myocarditis with preserved ejection fraction. The effect of myocardial injury, as defined by the presence of late gadolinium enhancement (LGE), on the change of left ventricular (LV) segmental strain parameters was evaluated in a longitudinal analysis. Patients with a first episode of acute myocarditis were enrolled retrospectively. Peak radial (PRS), longitudinal (PLS) and circumferential (PCS) LV segmental strain values at baseline and at follow-up were computed using feature tracking cine cardiac magnetic resonance imaging. The change of segmental strain values in LGE positive (LGE+) and LGE negative (LGE−) segments was compared over a course of 89 ± 20 days. In 24 patients, 100 LGE+ segments and 284 LGE− segments were analysed. Between LGE+ and LGE− segments, significant differences were found for the change of segmental PCS (p < 0.001) and segmental PRS (p = 0.006). LGE + segments showed an increase in contractility, indicating recovery, and LGE− segments showed a decrease in contractility, indicating normalisation after a hypercontractile state or impairment of an initially normal contracting segment. No significant difference between LGE+ and LGE− segments was found for the change in segmental PLS. In the course of acute myocarditis with preserved ejection fraction, regional myocardial function adapts inversely in segments with and without LGE. As these effects seem to counterbalance each other, global functional parameters might be of limited use in monitoring functional recovery of these patients.

OBJECTIVE: To retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT). METHODS: Sixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated. RESULTS: Values for some radiomics features were significantly lower (p < 0.05) and those of other radiomics features were significantly higher (p = 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity). CONCLUSION: The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis. KEY POINTS: Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of

We compared a fast, single breath-hold three dimensional LGE sequence (3D LGE) with an established two dimensional multi breath-hold sequence (2D LGE) and evaluated image quality and the amount of myocardial fibrosis in patients with acute and chronic myocarditis. 3D LGE and 2D LGE (both spatial resolution 1.5 × 1.5 mm$^{2}$, slice-thickness 8 mm, field of view 350 × 350 mm$^{2}$) were acquired in 25 patients with acute myocarditis (mean age 40 ± 18 years, 7 female) and 27 patients with chronic myocarditis (mean age 44 ± 22 years, 9 female) on a 1.5 T MR system. Image quality was evaluated by two independent, blinded readers using a 5-point Likert scale. Total myocardial mass, fibrotic mass and total fibrotic tissue percentage were quantified for both sequences in both groups. There was no significant difference in image quality between 3D und 2D acquisitions in patients with acute (p = 0.8) and chronic (p = 0.5) myocarditis. No significant differences between 3D and 2D acquisitions could be shown for myocardial mass (acute p = 0.2; chronic p = 0.3), fibrous tissue mass (acute p = 0.7; chronic p = 0.1) and total fibrous percentage (acute p = 0.4 and chronic p = 0.2). Inter-observer agreement was substantial to almost perfect. Acquisition time was significantly shorter for 3D LGE (24 ± 5 s) as compared to 2D LGE (350 ± 58 s, p < 0.001). In patients with acute and chronic myocarditis 3D LGE imaging shows equal diagnostic quality compared to standard 2D LGE imaging but with significantly reduced acquisition time.

ObjectivesTo assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine.Materials and methodsA web-based questionnaire was designed using SurveyMonkey, and was sent out to students at three major medical schools. It consisted of various sections aiming to evaluate the students' prior knowledge of AI in radiology and beyond, as well as their attitude towards AI in radiology specifically and in medicine in general. Respondents' anonymity was ensured.ResultsA total of 263 students (166 female, 94 male, median age 23 years) responded to the questionnaire. Around 52% were aware of the ongoing discussion about AI in radiology and 68% stated that they were unaware of the technologies involved. Respondents agreed that AI could potentially detect pathologies in radiological examinations (83%) but felt that AI would not be able to establish a definite diagnosis (56%). The majority agreed that AI will revolutionise and improve radiology (77% and 86%), while disagreeing with statements that human radiologists will be replaced (83%). Over two-thirds agreed on the need for AI to be included in medical training (71%). In sub-group analyses male and tech-savvy respondents were more confident on the benefits of AI and less fearful of these technologies.ConclusionContrary to anecdotes published in the media, undergraduate medical students do not worry that AI will replace human radiologists, and are aware of the potential applications and implications of AI on radiology and medicine. Radiology should take the lead in educating students about these emerging technologies.Key Points center dot Medical students are aware of the potential applications and implications of AI in radiology and medicine in general.center dot Medical students do not worry that the human radiologist or physician will be replaced.center dot Artificial intelligence should be included in medical training.

Cardiovascular involvement is a well-known feature of inflammatory rheumatic diseases, although often clinically silent, so early cardiovascular involvement may remain unrecognised. Thus, increased awareness and improved insights into the pathomechanisms of heart disease in the context of inflammatory rheumatic disease has led to an emerging role of cardiovascular magnetic resonance (CMR) as an accurate and non-invasive diagnostic test for detection of early (as well as late) cardiovascular involvement in inflammatory rheumatic disease. The present article will review the current potential as well as the limitations of established and emerging, qualitative and quantitative CMR techniques in the setting of inflammatory rheumatic disease and shed some light onto current developments in the field.

Myocarditis is known as the chameleon of cardiac diseases. The symptoms and the course of disease vary greatly so that it is often challenging to establish a diagnosis. Early and accurate diagnosis is of utmost importance, since myocarditis is one of the leading causes of sudden cardiac death in young adults and represents an important precursor to dilated cardiomyopathy. Due to the constraints of the routinely used diagnostic approach, including clinical history and examination, laboratory testing, and electrocardiogram, different imaging modalities have emerged over the last decades as contributors to the noninvasive diagnosis of myocarditis. With this interdisciplinary review we would like to present the current state-of-the-art imaging of myocarditis across all available imaging modalities (i.e., echocardiography, cardiac magnetic resonance, cardiac computed tomography, and nuclear medicine). Furthermore, we present novel imaging techniques that might become useful in the near future for easier and more accurate diagnosis of this highly relevant disease. Key Points: Different imaging modalities are increasingly used in the diagnostic workup of myocarditis Several emerging imaging techniques are currently on the way to becoming part of the clinical routine This review summarizes the diagnostic value of echocardiography, CMR, CT, and nuclear medicine imaging There is special focus on the possibilities and challenges of novel imaging tools within the different modalities. Citation Format: Baessler B, Schmidt M, Lucke C et al. Modern Imaging of Myocarditis: Possibilities and Challenges. Fortschr Rontgenstr 2016; 188: 915-925