Christopher Syben

JOINT calibration and motion estimation in weight-bearing cone-beam CT of the knee joint using fiducial markers

Recently, C-arm cone-beam CT systems have been used to acquire knee joints under weight-bearing conditions. For this purpose, the C-arm acquires images on a horizontal trajectory around the standing patient, who shows involuntary motion. The current state-of-the-art reconstruction approach estimates motion based on fiducial markers attached to the knee. A drawback is that this method requires calibration prior to each scan, since the horizontal trajectory is not reproducible. In this work, we propose a novel method, which does not need a calibration scan. For comparison, we extended the state-of-the-art method with an iterative scheme and we further introduce a closed-form solution of the compensated projection matrices. For evaluation, a numerical phantom and clinical data are used. The novel approach and the extended state-of-the-art method achieve a reduction of the reprojection error of 94% for the phantom data. The improvement for the clinical data ranged between 10% and 80%, which is followed by the visual impression. Therefore, the novel approach and the extended state-of-the-art method achieve superior results compared to the state-of-the-art method.

Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees.

Patient motion is one of the major challenges in cone-beam computed tomography (CBCT) scans acquired under weight-bearing conditions, since it leads to severe artifacts in reconstructions. In knee imaging, a state-of-the-art approach to compensate for patient motion uses fiducial markers attached to the skin. However, marker placement is a tedious and time consuming procedure for both, the physician and the patient. In this manuscript we investigate the use of anatomical landmarks in an attempt to replace externally attached fiducial markers. To this end, we devise a method to automatically detect anatomical landmarks in projection domain X-ray images irrespective of the viewing direction. To overcome the need for annotation of every X-ray image and to assure consistent annotation across images from the same subject, annotations and projection images are generated from 3D CT data. Twelve landmarks are annotated in supine CBCT reconstructions of the knee joint and then propagated to synthetically generated projection images. Then, a sequential Convolutional Neuronal Network is trained to predict the desired landmarks in projection images. The network is evaluated on synthetic images and real clinical data. On synthetic data promising results are achieved with a mean prediction error of \(8.4 \pm 8.2\) pixel. The network generalizes to real clinical data without the need of re-training. However, practical issues, such as the second leg entering the field of view, limit the performance of the method at this stage. Nevertheless, our results are promising and encourage further investigations on the use of anatomical landmarks for motion management.

Towards In-Vivo X-Ray Nanoscopy

Novel X-Ray Microscopy (XRM) systems allow to study the internal structure of a specimen on nanoscale. A possible use of this non-destructive technology is motivated in the medical research area. In-Vivo investigation of medication over a period of time and its effects on perfusion and bony structure might lead to a better understanding of drug mechanisms and diseases like Osteoporosis and could lead to new approaches to their treatment. The first step towards in-vivo XRM imaging is to investigate the suitability of recent XRM systems for this task and subsequently to determine the system parameters. In this context, the impact of mice motion on the image quality is studied in this work. This paper aims to simulate the effects of breathing motion and muscle relaxation of the mice on the reconstructed images, which already effects the projection images. We therefore assume a mouse’s respiration motion pattern, which happens four time during a single projection acquisitions, and the muscle relaxation movement due to anesthesia and simulate its impacts on image quality. Additionally, we show that a frame rate of at least 16 fps is needed to capture in-vivo movements in order to apply state-of-the-art motion correction methods.

Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke

In case of an ischemic stroke, identifying and removing blood clots is crucial for a successful recovery. We present a novel method to automatically detect vascular occlusion in non-enhanced computed tomography (NECT) images. Possible hyperdense thrombus candidates are extracted by thresholding and connected component clustering. A set of different features is computed to describe the objects, and a Random Forest classifier is applied to predict them. Thrombus classification yields 98.7% sensitivity with 6.7 false positives per volume, and 91.1% sensitivity with 2.7 false positives per volume. The classifier assigns a clot probability ≥ 90% for every thrombus with a volume larger than 100 mm3 or with a length above 23 mm, and can be used as a reliable method to detect blood clots. CCS Concepts •Computing methodologies → Classification and regression trees; •Applied computing → Health care information systems;

Self-Calibration and Simultaneous Motion Estimation for C-Arm CT Using Fiducial

C-arm cone-beam CT systems have an increasing popularity in the clinical environment due to their highly flexible scan trajectories.Recent work used these systems to acquire images of the knee joint under weight-bearing conditions. During the scan, the patient is in a standing or in a squatting position and is likely to show involuntary motion, which corrupts image reconstruction. The state-of-the-art fully automatic motion compensation relies on fiducial markers for motion estimation. Due to the not reproducible horizontal trajectory, the system has to be calibrated with a calibration phantom before or after each scan. In this work we present a method to incorporate a self-calibration into the existing motion compensation framework without the need of prior geometric calibration. Quantitative and qualitative evaluations on a numerical phantom as well as clinical data, show superior results compared to the current state-of-the-art method. Moreover, the clinical workflow is improved, as a dedicated system calibration for weight-bearing acquisitions is no longer required.