title: Dose reduction for cardiac CT using a registration-based approach.
abstract: Two reasons for the recent rise in radiation exposure from CT are increases in its clinical applicability and the desire to maintain high SNR while acquiring smaller voxels. To address this emerging dose problem, several strategies for reducing patient exposure have already been proposed. One method employed in cardiac imaging is ECG-driven modulation of the tube current between 100% at one time point in the cardiac cycle and a reduced fraction at the remaining phases. In this paper, we describe how images obtained during such acquisition can be used to reconstruct 4D data of consistent high quality throughout the cardiac cycle. In our approach, we assume that the middiastole (MD) phase is imaged with full dose. The MD image is then independently registered to lower dose images (lower SNR) at other frames, resulting in a set of transformations. Finally, the transformations are used to warp the MD frame through the cardiac cycle to generate the full 4D image. In addition, the transformations may be interpolated to increase the temporal sampling or to generate images at arbitrary time points. Our approach was validated using various data obtained with simulated and scanner-implemented dose modulation. We determined that as little as 10% of the total dose was required to reproduce full quality images with a 1 mm spatial error and an error in intensity values on the order of the image noise. Thus, our technique offers considerable dose reductions compared to standard imaging protocols, with minimal effects on the quality of the final data.
authors.affiliation: Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, P.O. Box 5015, N6A 5K8, London, Ontario, Canada.
authors.forename: Marcin
authors.initials: M
authors.lastname: Wierzbicki
authors.forename: Gérard M
authors.initials: GM
authors.lastname: Guiraudon
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Jones
authors.forename: Terry
authors.initials: T
authors.lastname: Peters
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001185:Artificial Intelligence; D006321:Heart / Q000000981:diagnostic imaging*; D021621:Imaging, Three-Dimensional / Q000379:methods; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods; D011829:Radiation Dosage; D011835:Radiation Protection / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine; D014057:Tomography, X-Ray Computed / Q000379:methods*