Deviations from the MR acquisition guidelines could lead to images with serious quality concerns such as incompletely imaged anatomies, which might require re-examinations and could result in missed pathologies. In this paper, we propose a deep learning method to automatically estimate the coverage of the target anatomy and to predict the extent of an anatomy outside the present field-of-view (FOV). For this purpose, we employed a 3D fully-convolutional neural network operating at multiple resolution levels. The proposed solution could be employed to propose a correct FOV setting in case of organ-coverage issues while patient is on the table and could be incorporated as a retrospective tool for quality monitoring and staff training. Our method was evaluated for four abdominal organs - liver, spleen, and left and right kidneys - in 40 magnetic resonance (MR) images from the publicly available Combined Healthy Abdominal Organ Segmentation (CHAOS) dataset. We obtained median extent-detection errors of 5.5-7.3mm or 3-4 voxels in the superior or inferior position in a dataset with average anatomical clippings of 24.8-43.6mm for four partially missing organs in the given FOV.
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.
In order to alleviate the risk of radiation induced cataract in patients undergoing head CT examinations, the guidelines published by the American Association of Physicists in Medicine (AAPM) link the optimal scan angle to particular anatomic landmarks in the skull. In this paper, we investigated the use of a foveal fully-convolutional neural network (F-Net) for the segmentation-based detection of three head CT landmarks, with the final objective of an automatic scan quality control. Three individual networks were trained using ground-truth (GT) from three different readers to investigate the detection accuracy compared to each reader. The experiments were performed using 119 head CT scans and the three-fold cross-validation set up. For the evaluation, two performance measures were employed: the Euclidean distance between the detected landmarks and GT, and the distance of the detected landmarks to the plane generated from the GT landmark positions. For three readers, the median values of the Euclidean and point-to-plane distance obtained using F-Net were in the range of 1.3 - 2.8 mm and 0.3 - 0.8 mm, respectively. The presented method outperformed a previously published approach using image registration and achieved results comparable to the inter-observer variability between three readers. Further improvements were achieved by training a similar network which combined GT information from all readers.
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.
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