An iterative projection‐based motion estimation and compensation scheme for head x‐ray CT. Issue 10 (28th September 2016)

• Record Type: Journal Article
• Authors: Sun, Tao; Kim, Jung‐Ha; Fulton, Roger; Nuyts, Johan
• Is Part Of: Medical physics. Volume 43:Issue 10(2016)
• Page Start: 5705
• Page End: 5716
• Publication Date: 2016-09-28
• Subjects: computerised tomography -- image resolution -- image restoration -- iterative methods -- mean square error methods -- medical image processing -- motion compensation -- motion estimation -- phantoms; Computed tomography -- Reconstruction -- General statistical methods -- Spatial resolution; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image -- Analysis of motion; computed tomography -- iterative reconstruction -- rigid motion -- motion estimation -- motion compensation; Medical image reconstruction -- Computed tomography -- Image reconstruction -- Motion compensation -- Motion estimation -- Movement sensors -- Image scanners -- Medical image artifacts -- Kinematics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4963218
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Although current computed tomography (CT) systems can scan the head in a very short time, patient motion sometimes still induces artifacts. If motion occurs, one has to repeat the scan; to avoid motion, sedation or anesthesia is sometimes applied. Methods: The authors propose a method to iteratively estimate and compensate this motion during the reconstruction. In every iteration, the rigid motion was estimated view‐by‐view and then used to update the system matrix. A multiresolution scheme was used to speed up the convergence of this joint estimation of the image and the motion of the subject. A final iterative reconstruction was performed with the last motion estimate. Results: The method was evaluated on simulations, patient scans, and a phantom study. The quality of the reconstructed images was improved substantially after the compensation. In simulation and phantom studies, root‐mean‐square error was reduced and mean structural similarity was increased. In the patient studies, most of the motion blurring in the reconstructed images disappeared after the compensation. Conclusions: The proposed method effectively eliminated motion‐induced artifacts in head CT scans. Since only measured raw data are needed for the motion estimation and compensation, the proposed method can be applied retrospectively to clinical helical CT scans affected by motion.