Variance-Based Iterative Image Reconstruction from Few Views in Limited-Angle C-Arm Computed Tomography

C-arm cone-beam computed tomography offers CT-like 3D imaging capabilities, but with the additional advantage of being appropriate for interventional suites. Due to the limitations of the data acquisition system, projections are oft acquired in a short scan angular range, resulting in significant artifacts, if conventional analytic formulas are applied. Furthermore, the presence of high-density objects, like metal parts, induces streak-like artifacts, which can obscure relevant anatomy. We present a new algorithm to reduce such artifacts and enhance the quality of reconstructed 3D volume. We make use of the variance of estimated voxel values over all projections to decrease the ground artifact level. The proposed algorithm is less sensitive to data truncation, and does not require explicit estimation of missing data. The number of required images is very low (up to 56 projections), which have several benefits, like significant reduction of patient dose and shortening of the acquisition time. The performance of the proposed method is demonstrated based on simulations and phantom data

On-the-Fly Geometrical Calibration Fine-Tuning of a Mobile C-Arm CBCT System : Geometrische Online-Nachoptimierung der Kalibrierung eines mobilen CBCT-C-Bogens

C-arm cone-beam computed tomography (CBCT) offers CT-like 3D imaging capabilities while being appropriate for interventional suites. 3D images are reconstructed based on 2D projections gathered during the rotation of the C-arm around the object under investigation. It is mandatory to provide accurate geometrical projection parameters for each acquired 2D image, otherwise significant CT artifacts may be induced in the reconstructed 3D volume. Usually, a pre-calibration (offline calibration) using an X-ray phantom is preformed under the assumption that the C-arm motion is reproducible. Thereby, stochastic misalignments due to the open design of mobile C-arm CBCT systems are not considered. In this article we introduce a novel online calibration algorithm to compensate stochastic mechanical inaccuracies. The performance of the proposed method is demonstrated on simulated and real data. Intraoperative 3D-Röntgenbildgebung ist zum Standard in der modernen Chirurgie geworden. Mithilfe von mobilen C-Bögen können 3D Computertomographie-ähnliche Bilder intraoperativ aufgenommen werden. Während der Rotation des C-Bogens um den Patienten werden 2D-Projektionen akquiriert, die zur Rekonstruktion der Volumendaten verwendet werden. Präzise geometrische Projektionsparameter (Position der Röntgenquelle, Detektor-Lage und -Orientierung) werden für jedes Projektionsbild benötigt, da sonst bei der Rekonstruktion Artefakte entstehen können. Aufgrund der mechanischen Stabilität des C-Bogens wird eine Offline-Kalibrierung üblicherweise durchgeführt, damit die Projekionsparameter ermittelt werden können. Es wird dabei angenommen, dass Abweichungen von der Idealgeometrie reproduzierbar sind; stochastische Abweichungen (z. B. Vibrationen bei der Rotation) werden dabei nicht berücksichtigt. In dieser Arbeit präsentieren wir eine neue Methode zur geometrischen Online-Kalibrierung, die zusätzlich die stochastisch-mechanischen Abweichungen kompensiert. Die Qualität der Kalibrierung wird anhand von Simulations- und experimentellen Daten demonstriert

Particle Path Segmentation: a Fast, Accurate, and Robust Method for Localization of Spherical Markers in Cone-beam CT Projections

Spherical fiducial markers are widely used for point-based registration of intra-operative 2D X-ray projections to preoperative 3D computed tomography images. The automatic localization of marker centers on X-rays speeds up the registration process and reduces the human error. However, a sub-pixel accurate localization of said centers is at present a challenging task, especially when fiducials overlap dense anatomical structures on the radiographs. In this paper, we propose a new method for automated and accurate detection of centers of fiducial markers in 2D projections, even in presence of structure overlapping. Several experiments confirm the high accuracy and robustness of the proposed algorithm achieving a localization error - mean (std) - equal to 0.059 (0.062) mm