Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories.
The short-scan Feldkamp David Kress (FDK) method for C-arm CT reconstruction involves a heuristic raybased
weighting scheme to handle data redundancies. This scheme is known to be approximate under general
circumstances and it often creates low frequency image artifacts in regions away from the central axial plane.
Alternative algorithms, such as the one proposed by Defrise and Clack (DC),1 can handle data redundancy in
a theoretically exact manner and thus notably improve image quality. The DC algorithm, however, is computationally
more complex than FDK, as it requires a shift-variant 2D filtering of the data instead of a efficient 1D
filtering. In this paper, a modification of the original DC algorithm is investigated, which applies the efficient
FDK filtering scheme whereever possible and the DC filtering scheme only where it is required. This modification
leads to a more efficient implementation of the DC algorithm, in which filtering effort can be reduced by up to
about 70%, dependent on the specific geometry set-up. This gain in computation speed makes the DC method
even more attractive for use in an interventional environment, where fast and interactive X-ray imaging is a
Misalignment-Correction in C-arm-based flat-detector CT (FD-CT) is a frequently discussed problem. To avoid artifacts
caused by geometrical instabilities, numerous methods for misalignment correction were investigated. Most of them
make use of a foregoing calibration routine, based on scanning a specific phantom. The aim of this study is to develop
and evaluate an online image-content-based calibration technique without using any kind of marker or calibration
phantom. The introduced method is based on a gradient descent method, minimizing an entropy criterion which is used
to optimize the underlying geometry parameters of the acquisition system. It is formed as multistep approach, including a
global, local and projection wise optimization. This enables the elimination of general system misalignments, as well as a
reduction of streak artifacts and the adjustment of patient motion artifacts. Phantom and patient measurements with the
C-arm FD-CT system Artis Zeego (Siemens AG, Healthcare Sector, Forchheim, Germany) were used to validate the
algorithm for realistic applications. It reduced most of the actual misalignment and increased image quality drastically.
Phantom-studies, starting from the standard system geometry without a foregoing calibration showed very good results.
Online-calibration is possible with our approach and therefore, the limitation to predefined scan-protocols is obsolete.
The evaluation of patient datasets brought out the same conclusions and provides the implication of simultaneous patient
Iterative reconstruction methods possess many advantages over analytical reconstruction methods especially if constraints can be used to regularize the reconstruction. However the main problem of iterative reconstruction algorithms is to decide when to stop the iteration.
For the Simultaneous Iterative Reconstruction Technique (SIRT) without constraints we derived a mathematical formula with which the quality of the reconstruction after a given number of iterations can be calculated. The image quality is expressed here by a special filter kernel for a FBP reconstruction which creates images with the same sharpness and noise properties as SIRT.
Further on the formula can be used to analyze the numerical stability of a certain implementation of SIRT.
Experiments show the validity of these "iteration-equivalent"-kernels with respect to sharpness and noise properties of the reconstructed images.
Incomplete data due to the object extent beyond the scanning field of view (SFOV) is a common
problem in computed tomography. In these cases, there are parts of the object to be reconstructed
for which only incomplete projections of less than 180o are available. Applying iterative algorithms
like algebraic reconstruction technique (ART) or simultaneous algebraic reconstruction Technique
(SART) onto the problem of truncated projections can not produce a satisfying solution unless
special constraints are used. To regularize the reconstruction algorithm, we extend iterative
reconstruction algorithms by introducing information regarding the statistics of the attenuation
values of the reconstructed object in terms of the log likelihood function of attenuation values. This
information can be taken from the regions of the image still inside the SFOV but close to the region
where the object exceeds the SFOV. The information can be utilized in an algebraic reconstruction
method by adding a constraint term to the cost function that shall be minimized.
Experiments show that for not severely truncated projections, as they are common for CT
applications, including this information yields good estimates about the object.