Marker‐free motion correction in weight‐bearing cone‐beam CT of the knee joint

Abstract

The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.

Correction of patient motion in cone-beam CT using 3D–2D registration

“…$\mathrm{bold-italicP}{\mathit{M}}_{\mathit{init}}^{\mathit{i}}$ for the third and subsequent projections (through the N th projection) is generated by bi-linear extrapolation of the extrinsic parameters of the previous two projection matrices as described in Ouadah et al (2016) to predict the pose of the current projection. This bilinear extrapolation used to create $\mathrm{bold-italicP}{\mathit{M}}_{\mathit{init}}^{\mathit{i}}$ is also distinct from previous methods for 3D-2D registration of multiple projections, such as (Berger et al 2016). This approach was previously shown to trap spurious outliers and yield realistic, continuous orbits of the C-arm, introducing a slight degree of interdependence between pose estimates for each projection view.…”

“…Using this framework, we aim to correct large-scale rigid motion that occurs during a scan and restore image quality to that of the motion-free image. The method is similar to that of Berger et al (2016) but uses a distinct registration framework with a variety of advantages for interventional imaging, applies the method in the context of head imaging, and demonstrates the applicability of the algorithm in clinical cases. The similarity metric is based on gradient orientation (GO), which is robust against mismatch in image content between the prior 3D image and the current acquisition (demonstrated by De Silva et al 2016).…”

Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion

“…Fiducial-free motion compensation using 2D/3D registration that relied only on the data available in the motion-contaminated scan has been investigated, showing degraded performance compared to the fiducial-based approach (Unberath et al 2015). Better results, approaching or even surpassing the fiducial-based algorithm, were obtained using a 2D/3D registration with a motion-free prior CT or CBCT (Berger et al 2016, Ouadah et al 2016a). However, such prior information is not always available.…”

Motion compensation for cone-beam CT using Fourier consistency conditions

“…For example, an ECG signal will not correlate with skeletal motion while implanting markers for conventional heart imaging does not seem reasonable. Another approach is to perform 2-D/3-D registration of a known volume or an initial reconstruction to the individual projection images (Zeng et al; 2005; Gendrin et al; 2012; Ouadah et al; 2016; Berger et al; 2016). Registration approaches have been investigated extensively for tumor tracking in the field of radiotherapy, where we refer to Rit et al (2013) for a comprehensive review.…”