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

Abstract

Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm – 0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure Similarity Index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.

Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

“…For even higher motion amplitudes as in the high motion datasets we were only able to reach an SSIM of 0.7. Motion as described by Sisniega et al generates double contours which our algorithm is only able to correct for small motion amplitudes (SSIM > 0.9 only for motion amplitudes of up to 3 mm). However even for the high motion group an SSIM value of 0.98 could be obtained if the ground truth was used for initial motion estimation.…”

“…We generated a total of 9 random walk motion fields applied to 3 different CT volumes resulting in a total of 27 synthetic random walk motion datasets. Similar to Sisniega et al we created additional datasets where the patient is stationary for the first 129 images, then translates between 1 and 10 mm into a new position during the next 86 images and stays stationary for the remaining images. Thus, we obtained 20 additional piecewise linear motion datasets.…”

“…It has been suggested that a high percentage (>80%) of the patients move more than 0.5 mm during the scan, with movements more than one millimeter occurring in 70% of all cases . Thereby, even movements of the patient in the low single‐digit millimeter range have a negative influence on the quality of the volumetric reconstruction . In addition to active patient movements, similar problems can also occur due to normal organ activity (heartbeat) or respiratory movements .…”

“…Additionally the authors focused on minimizing the linear equation system while we use a more general approach by using image similarity measurements as a way of estimating motions. Sisniega et al developed another technique for motion compensation minimizing the blur of a selected ROI . The authors state that they only simulated piecewise linear motions of the patient to evaluate their synthetic data.…”

Head motion correction based on filtered backprojection for x‐ray CT imaging

“…We then perform MC reconstruction using the rebinned data, by integrating the rigid motion into the preweighting, filtering, and backprojection processes of the FDK algorithm. The proposed MC reconstruction is thereby different from previous FBP‐based motion compensation approaches where motion compensation is performed only in the backprojection process, and provides improved image quality.…”