Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts

•This paper presents a novel geodesic density registration (GDR) algorithm to remove motion artifacts in 4DCT pulmonary imaging that occur during breathing.•The GDR algorithm uses binary artifact masks to exclude artifact regions from the regression and accommodates image intensity change associated with breathing by using a tissue density deformation action.•This paper provides experimental evidence that not every artifact needs to be masked for the GDR approach to reduce the effects of motion artifacts. Thus, artifact masks can be quickly identified by hand.•We demonstrate that the GDR method is able to remove artifacts in treatment planning 4DCT images in regions with and without artifact masks. [Display omitted] Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.