\section{Related Work}
In recent years, deep learning has become more common in radiology \cite{choyCurrentApplicationsFuture2018,sabaPresentFutureDeep2019}. Scientists working on radiographs successfully applied Neural Networks to detect fractures \cite{lindseyDeepNeuralNetwork2018,thianConvolutionalNeuralNetworks2019}, classify body parts \cite{agunwaBodyPartImaging2019}, and radiographic views \cite{fangGeneralizedRadiographicView2020a}, to facilitate the work process in radiology. Although our proposed framework also includes radiographic view recognition, these steps are only part of the preprocessing for assessing diagnostic quality. Distinct to \citet{fangGeneralizedRadiographicView2020a}, where only a single step is used for recognition, we use multiple steps containing different networks and resign to recognize laterality.

\citet{essesAutomatedImageQuality2018a} and \cite{wangTwostepAutomatedQuality2020} focus on automated diagnostic quality evaluation of MRT images using Neural Networks. Due to the different modalities of the imaging systems one can not easily transfer the results to radiographs.

Approaches that automatically asses the perceptual quality of radiographs only take technical parameters such as noise and contrast into account and rely on conventional computer vision methods \cite{sameiAutomatedCharacterizationPerceptual2014,willisAutomatedQualityControl2018}.
\citet{takakiCalculatingTargetExposure2020} present a deep learning approach to calculate the target exposure index for chest radiographs based on perceptual quality of small patches. 

To our knowledge, there are no studies considering anatomical features for the diagnostic quality of radiographs in a deep-learning framework.    
 