Image Quality Assessments Medha Juneja 1 , Mechthild Bode-Hofmann 2 , Khay Sun Haong 3 , Steffen Meißner 5 , Viola Merkel 4 , Johannes Vogt 1 , Nobert Wilke 3 , Anja Wolff 1 , Thomas Hartkens 1 1 Technology Lab, medneo GmbH, Berlin 2 Ernst von Bergmann Poliklinik Potsdam 3 Radiologisches Zentrum H¨ ochstadt / N¨ urnberg / F¨ urth 4 Radiologische Praxis Merkel 5 Sana Gesundheitszentren Berlin-Brandenburg GmbH medha.juneja@medneo.com Abstract. Deep learning with Convolutional Neural Networks (CNN) requires large number of training and test data sets which involves usu- ally time-consuming visual inspection of medical image data. Recently, crowdsourcing methods have been proposed to gain such large training sets from untrained observers. In this paper, we propose to establish a lightweight method within the daily routine of radiologists in order to collect simple image quality annotations on a large scale. In multiple di- agnostic centres, we analyse the acceptance rate of the radiologists and whether a substantial total number of professional annotations can be acquired to be used for deep learning later. Using a simple control panel with three buttons, 6 radiologists in 5 imaging centres assessed the im- age quality within their daily routine. Altogether, 1527 DICOM image studies (MR, CT, and X-ray) have been subjectively assessed in the first 70 days which demonstrates that a considerable number of training data sets can be collected with such a method in short time. The acceptance rate of the radiologists indicates that more data sets could be acquired if corresponding incentives are introduced as discussed in the paper. Since the proposed method is incorporated in the daily routine of radiologists, it can be easily scaled to even more number of professional observers. 1 Introduction Quality control of radiological images has been an intense field of research in the last 25 years [1], because it is essential for excluding problematic acquisitions and avoiding bias in subsequent reading of the images. Also, many image pro- cessing and analysis techniques rely on a constant image quality and can result in erroneous conclusion if the image quality is not sufficient. Automatic image quality control enables technicians at the modality to verify the image quality directly after acquisition and makes it possible to repeat the acquisition while the patient is still in the scanner. Quantitative image quality