A novel technology for estimating both the pose and the joint flexion from a single musculoskeletal X-ray image is presented for automatic quality assessment of patient positioning. The method is based on convolutional neural networks and does not require pose or flexion labels of the X-ray images for the training phase. The task is split into two steps: (i) detection of relevant bone contours in the X-ray by a feature-detection network and (ii) regression of the pose and flexion parameters by a pose-estimation network based upon the detected contours. This separation enables the pose-estimation network to be trained using synthetic contours, which are generated via projections of an articulated 3D model of the target anatomy. It is demonstrated that the use of data-augmentation techniques during training of the pose-estimation network significantly contributes to the robustness of the algorithm. Feasibility of the approach is illustrated using lateral ankle X-ray exams. Validation was performed using X-rays of an anthropomorphic phantom of the foot-ankle joint, imaged in various controlled positions. Reference pose parameters were established by an expert using an interactive tool to align the articulated 3D joint model with the phantom image. Errors in pose estimation are in the range of 2 degrees per pose angle and at the level of the expert performance. Using the rigid foot phantom the flexion parameter was constant, but the overall results indicate accurate estimation also of this parameter.
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.
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