Fiducial marker‐based correction for involuntary motion in weight‐bearing C‐arm CT scanning of knees. Part I. Numerical model‐based optimization

Abstract

Purpose: Human subjects in standing positions are apt to show much more involuntary motion than in supine positions. The authors aimed to simulate a complicated realistic lower body movement using the four-dimensional (4D) digital extended cardiac-torso (XCAT) phantom. The authors also investigated fiducial marker-based motion compensation methods in two-dimensional (2D) and threedimensional (3D) space. The level of involuntary movement-induced artifacts and image quality improvement were investigated after applying each method. Methods: An optical tracking system with eight cameras and seven retroreflective markers enabled us to track involuntary motion of the lower body of nine healthy subjects holding a squat position at 60• of flexion. The XCAT-based knee model was developed using the 4D XCAT phantom and the optical tracking data acquired at 120 Hz. The authors divided the lower body in the XCAT into six parts and applied unique affine transforms to each so that the motion (6 degrees of freedom) could be synchronized with the optical markers' location at each time frame. The control points of the XCAT were tessellated into triangles and 248 projection images were created based on intersections of each ray and monochromatic absorption. The tracking data sets with the largest motion (Subject 2) and the smallest motion (Subject 5) among the nine data sets were used to animate the XCAT knee model. The authors defined eight skin control points well distributed around the knees as pseudofiducial markers which functioned as a reference in motion correction. Motion compensation was done in the following ways: (1) simple projection shifting in 2D, (2) deformable projection warping in 2D, and (3) rigid body warping in 3D. Graphics hardware accelerated filtered backprojection was implemented and combined with the three correction methods in order to speed up the simulation process. Correction fidelity was evaluated as a function of number of markers used (4-12) and marker distribution in three scenarios. Results: Average optical-based translational motion for the nine subjects was 2.14 mm (±0.69 mm) and 2.29 mm (±0.63 mm) for the right and left knee, respectively. In the representative central slices of Subject 2, the authors observed 20.30%, 18.30%, and 22.02% improvements in the structural similarity (SSIM) index with 2D shifting, 2D warping, and 3D warping, respectively. The performance of 2D warping improved as the number of markers increased up to 12 while 2D shifting and 3D warping were insensitive to the number of markers used. The minimum required number of markers for 2D shifting, 2D warping, and 3D warping was 4-6, 12, and 8, respectively. An even distribution of markers over the entire field of view provided robust performance for all three correction methods. Conclusions:The authors were able to simulate subject-specific realistic knee movement in weightbearing positions. This study indicates that involuntary motion can seriously degrade the image quality. The proposed three methods were evaluated with the...

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment

“…Given minor deviations in knee flexion, it is not unreasonable to assume that considering only a rigid body motion could work for correcting the involuntary motion in the lower body; this was confirmed by the results, which showed that 2D shifting and 3D warping performed well for the numerical data 16 and experimental data in vivo even when used to analyze the datasets with the largest motion. We could further reduce the residual errors by addressing deformable motion using motion vector fields estimated by significant features in the projection images.…”

“…As a practical way for 3D warping to address knee flexion, we could transform the lower leg and upper leg individually in 3D and apply both affine transformations to the region close to the knee joint center after weighting the two transformations based on proximity to the joint center, as we did for the XCAT model [see Ref. 16 in Part I].…”

“…Moreover, optical tracking of involuntary motion of the lower body showed that knee flexion deviation was minor. 16 In the optical tracking study, knee flexion deviation at the right and left knee of nine subjects squatting at approximately 60…”

“…Recently, dedicated CBCT systems for musculoskeletal extremities have been developed and are capable of imaging the lower extremities under weight-bearing conditions. 14,15 In the numerical model-based optimization 16 (hereafter referred to as "Part I") of this study, the complicated involuntary lower body movement of patients in standing positions was simulated using the XCAT knee model. From the numerical simulation results in Part I, it was seen that a subjects' involuntary lower body motion generates considerable motion artifacts in volume images in vivo.…”

Dynamic detector offsets for field of view extension in C‐arm computed tomography with application to weight‐bearing imaging

“…1). 10,15 Currently, these trajectories and their reconstruction methods are designed for circular FOVs, but many anatomical structures may be better described by a noncircular boundary, e.g., an ellipse.…”