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When looking at the sequence of cone-beam x-ray projection images of an object, humans can intuitively make some assumptions about the shape and size of the scanned object judging by its motion field during the acquisition. This works even in highly truncated scenarios by intuitively extrapolating the observed part of the motion field to the occluded outer regions. Based on this observation, we present a method that allows to infer knowledge about the shape and size of the object under investigation even far outside of the scan field of view of a truncated cone-beam x-ray acquisition. By making use of the optical flow vectors computed from a sequence of projection images, the correspondences between points of interest in consecutive projection frames are established and triangulated back to locate their projective origins in 3D space. In a previous work we have shown as a proof of concept that this can be used as an estimation of the patient's maximum extent. Here, we add further epipolar constraints to the general method which allows for obtaining more precise structural and shape information about the patient outside of the commonly reconstructable scan field of view. Results show that from severely truncated clinical projection data of the human skull, this method enables to locate parts of the anterior skull far outside of the scan field of view and therefore can be used to e.g. optimize the fit of an extrapolation, to initialize iterative reconstruction methods or even for deep learning-based methods.

The use of C-Arm-based cone-beam computed tomography (CBCT) plays an increasing role in interventions, especially for guidance and therapy control. The slow speed and the high dose limit the use of CBCT to research and prevent its widespread application in clinical routine. Acquiring less data using greater angular step or limited angular range is an obvious way to overcome these issues. However, images reconstructed from such datasets using standard reconstruction algorithms are deteriorated with severe artifacts. In this work, we investigate the use of a new nullspace-constrained modification scheme for sparse-view and limited-angle intra-operative CT image reconstruction. This scheme allows to perform fast unconstrained ART reconstruction, and, based on prior knowledge regarding the object to be reconstructed, some modifications restricted to the nullspace of the system can be easily applied as a post-processing step. Within this scheme, we enforce sparsity by integrating geometric prior information regarding the interventional tool itself, besides a high-quality pre-operative CT image. The presented method was compared to the compressed sensing-based algorithms NIHT and PrIDICT. Performance was evaluated qualitatively and quantitatively. This new scheme is shown to be promising for low-dose intra-operative image reconstruction. Compared to PrIDICT and NIHT, it shows higher reconstruction accuracy and demonstrates the ability to precisely visualize the instrument’s position even when only 15 projection views are acquired over a full angular range. It demonstrates an accurate reconstruction with a high degree of robustness against data incompleteness and sparsity level over-estimation.

A typical incomplete data problem arising in cone-beam computed tomography (CBCT) occurs when an object is either too large to be projected onto the detector or is deliberately only projected in parts. This problem is called truncation. Tomographic images reconstructed from truncated projection data can be severely impaired by image artifacts depending on the degree of truncation. A typical strategy to counter this is to extend the projection data by some smooth extrapolation. In order to accurately approximate the shape of the scanned object outside of the volume of interest (VOI), we previously presented a method which fits an extrapolation model to the truncated data by minimizing an error function based on the Grangeat consistency condition (GCC). In this work we propose a method of reducing the complexity of the extrapolation by making use of the 0th image moments of the truncated projection data.