The quality of chest radiographs is a practical issue because deviations from quality standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic and reproducible assessment of the most important quality figures on every acquisition can enable a radiology department to measure, maintain, and improve quality rates on an everyday basis. A method is proposed here to automatically quantify the quality according to the aspects of (i) collimation, (ii) patient rotation, and (iii) inhalation state of a chest PA radiograph by localizing a number of anatomical features and calculating some quality figures in accordance with international standards. The anatomical features related to these quality aspects are robustly detected by a combination of three convolutional neural networks and two probabilistic anatomical atlases. An error analysis demonstrates the accuracy and robustness of the method. The implementation proposed here works in real time (less than a second) on a CPU without any GPU support.
The purpose of this paper is the investigation of automatic evaluation of the quality of patient positioning and Field of View (FoV) in head CT scans. Studies have shown elevated risk of radiation-induced cataract in patients undergoing head CT examinations. The American Association of Physicists in Medicine (AAPM) published a protocol for head CT scans including requirements linking the optimal scan angle to anatomic landmarks in the skull. To help sensitizing staff for the need of correct patient positioning, a software-based tool detecting nonoptimal patient positioning was developed. Our experiments were conducted on 209 head CT exams acquired at the University Medical Center Hamburg Eppendorf (UKE). All of these examinations were done on the same Philips iCT scanner. Each exam contains a 3D volume with an in-plane voxel spacing of 0.44mm x 0.44mm and a slice distance of 1mm. As ground truth anatomic landmarks on the skull were annotated independently by three different readers. We applied an atlas registration technique to map CT scans to a probabilistic anatomical atlas. For a new CT scan, previously defined model landmarks were mapped back to the CT volume when registering it to the atlas thus labelling new head CT scans. From the location of the detected landmarks we derive the deviation of the actual head angulation and scan length from the optimal values. Furthermore, the presence of the eye-lenses in the FoV is predicted. The median error of the estimated landmark positions measured as distance to the plane generated from the ground truth landmark positions is below 1mm and comparable to the interobserver variability. A classifier for the prediction of the presence of the eye-lenses in the FoV from the estimated landmark locations achieves a κ value of 0.74. Furthermore there is moderate agreement of the estimated deviations of optimal head tilt and scan length with an expert’s rating.