\section{Introduction}
As one of the most frequently used imaging modalities, radiographs are of significant importance for diagnosis and treatment planning.
For these tasks, a diagnostic-adequate image quality is mandatory.

Currently, radiographers have to decide if the quality of the radiograph suffices for the diagnosis or if the imaging process must be repeated. To not be able to immediately judge the diagnostic quality correctly can result in various disadvantages including unnecessary radiation exposure. 
Reasons for misjudging image quality as sufficient may be time pressure, inexperience or overtiredness, in which case the treating radiologist has to schedule a new examination resulting in additional effort.
In the worst case, the radiographer would take a second radiograph although the first one was sufficient and thereby re-expose the patient to radiation.
To prevent these errors and to establish a quality control mechanism, an automated quality assessment can help.

While there is extensive research assessing radiograph quality based on technical factors such as contrast and noise \cite{essesAutomatedImageQuality2018a, takakiCalculatingTargetExposure2020, wangTwostepAutomatedQuality2020}, these parameters are less important with digital radiography.
A more important quality criterion, is the alignment of the body part to be X-rayed relative to the X-ray machine. 
A radiograph may be of perfect technical quality but can nevertheless be worthless for diagnostic purposes if relevant anatomical structures are not visible due to misalignment.
To our knowledge, previous approaches do not assess the quality of a radiograph based on this criterion.

In this paper, we propose a framework based on classification and segmentation Neural Networks, which assesses the diagnostic quality of ankle radiographs based on anatomical features. Furthermore, we test the framework on a new dataset containing radiographs of ankles, with 950 radiographs in two different radiographic views (\textit{anterior posterior} and \textit{lateral}), all labeled by four radiologists.
Using this framework, radiographers will be able to immediately get a first quality assessment of the taken radiographs without relying on a radiologist. Besides reducing the described judgment errors, the framework can be used as a quality control mechanism to detect causes for low quality radiographs. 
