Correction of Non-Periodic Motion in Computed Tomography

Medical imaging with computed tomography (CT) aims at reconstructing a volumetricimage of the patient’s anatomy from a sequence of planar X-ray projections.The quality of reconstructed images is often degraded by residual patient motion,especially when using slowly rotating C-arm robots or X-ray systems integrated withlinear accelerators for radiotherapy. The data acquisition might take from severalseconds up to one minute on those equipments. In particular, if the patient does notmanage to hold his or her movements during measurements, image resolution canbe impaired by strong motion blur artifacts.This work was dedicated to the development of original techniques aiming atimproving the quality of CT images when X-ray projections contain non-periodicpatient motion. Since the developed methods do not assume any periodicity of themotion model, they can correct artifacts due to unstructured patient motion, such asbreath-hold failure, abdominal contractions, and nervous movements. The proposedsolutions tackle for the first time the problem of motion correction in CT by usingsolely the acquired data.A first approach is to iteratively correct the reconstructed image by first decomposingthe perceived motion in projection space into positive and negative parts,then reconstructing the motion artifacts in image space, and finally, subtracting theartifacts from an initial image of the anatomy. The initial image is reconstructedfrom the acquired data without motion compensation but is nevertheless consideredas a reference for estimating the reconstruction artifacts.An alternative approach consists of an iterative workflow to progressively estimatea dynamic displacement vector field representing the position of image elementsover time. The motion information is then used within a motion-compensated variantof the analytical reconstruction algorithm to improve the image quality locally.An elastic image registration step computes the displacement in projection space,minimizing the difference between measured projections and reference projectionssampled from the image reconstructed in previous iterations. In addition, a motionsegmentation procedure detects in image space the regions which are subject tomotion during acquisition.Promising experimental results are summarized in qualitative figures and quantitativeanalyses. Experiments are based on numerically simulated projections froma mathematical phantom and from a sequence of clinical images obtained from a respiratory-gated acquisition on a fast helical CT scanner. Several elastic image registrationmethods are also evaluated for motion estimation purpose. In supplement,a study comparing various image interpolation and approximation techniques andtheir impact on reconstructed image fidelity is presented.

Keywords:computed tomography, iterative methods, motion segmentation, motion estimation, motion compensation