Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐CBCT) using groupwise deformable registration

PURPOSE To demonstrate the feasibility of using a purely data-driven, a posteriori respiratory motion modeling and reconstruction compensation method to improve 4D-CBCT image quality under clinically relevant image acquisition conditions. METHODS Evaluated workflows that utilized a combination of groupwise deformable image registration and motion-compensated image reconstruction algorithms. Groupwise registration is an approach that simultaneously registers all temporal frames of a 4D image to a common reference instead of one at a time so as to minimize the influence of any individual time point on the global smoothness or accuracy of the resulting deformation model. Four-dimensional cone-beam CT (4D-CBCT) Feldkamp-Davis-Kress (FDK) reconstructions were registered to either iteratively computed mean respiratory phase (mean-frame) or preselected respiratory phase (fixed-frame) reference images to model respiratory motion. The resulting 4D transformations were used to deform projection data during the FDK backprojection operation to create motion-compensated reconstructions. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to a mobile tissue boundary and was used to evaluate image quality in regions susceptible to motion artifacts. Image quality improvement was assessed for 19 clinical cases by evaluating mitigation of view aliasing artifacts, TIS, image noise reduction, and contrast for implanted fiducial markers. RESULTS Average (standard deviation) diaphragm TIS recovery relative to initial 4D-CBCT reconstructions was observed to be 87% (46%) using fixed-frame registration alone; 87% (47%) using fixed frame with motion-compensated reconstruction; 101% (68%) using mean-frame registration alone; and 99% (65%) using mean frame with motion-compensated reconstruction. Noise was reduced in sampled soft tissue ROIs by 58% for both fixed-frame registration and registration with motion compensation and by 57% and 58% on average for the corresponding mean-frame methods, respectively. Average improvement in local CNR was observed to be respectively 93% and 98% for fixed-frame registration and registration with motion compensation methods and 116% and 111% for the corresponding mean-frame methods. CONCLUSION Data-driven groupwise registration and motion-compensated reconstruction offer a feasible means of improving the quality of 4D-CBCT images acquired under clinical conditions. The addition of motion compensation reconstruction after groupwise registration visibly reduced the impact of view aliasing artifacts for the clinical image datasets studied.