Motion compensated iterative reconstruction of a ...

Motion compensated iterative CT reconstruction of a cardiac region of interest using elastic image registration A.A. Isola1,2, M. Grass1, T. Köhler1 and W.J. Niessen2,3 1Philips

Technologie GmbH Forschungslaboratorien, Röntgenstrasse 24-26, 22335 Hamburg, Germany Imaging Group Rotterdam, Erasmus MC, University Medical Center Rotterdam, The Netherlands 3Department of Applied Sciences, Delft University of Technology, The Netherlands 2Biomedical

1. Purpose Coronary artery disease (CAD) is the leading cause of mortality in the world. Cardiac CT can be performed for noninvasive diagnosis of CAD, but the heart pulsation continues to be a limiting factor. ECG-gated CT reconstruction methods [1,2] yield excellent results, but these are limited in their temporal resolution due to the mechanical movement of the gantry, and lead to residual motion blurring artifacts (MBA). If the motion of the cardiac region of interest (ROI) during the acquisition time can be recovered, motion compensated (MC) gated reconstructions can be applied to remove the residual MBA [3,4]. A method for MC iterative CT reconstruction of a cardiac ROI is presented. Given a precomputed gated 4D ROI data set, a fully automatic elastic image registration is applied to recover the motion vector field (MVF) of the ROI from a chosen cardiac reference phase to a number of phases within the RR interval. The method is applied to clinical data at a strong cardiac motion phase. Comparing the method to standard gated iterative reconstruction results shows that motion compensation strongly improved the image quality.

2. Methods Iterative reconstruction algorithms require the calculation of forward projections through the image volume, which are compared with the actual measurements to update the intermediate image volume via back projection. This procedure is repeated, leading to an iterative reconstruction algorithm. In this work, an iterative aperture weighted ordered subset maximum likelihood (AWOSML) approach [5,6] was applied. To recover the ROI sinogram from the raw measurements the method proposed by Ziegler et al. [6] was used. For the image representation, Kaiser-Bessel basis functions (also called blobs) [7] are used. Due to the divergence of the estimated cardiac MVF, a change in the volumes of the blobs cannot be neglected. Hence, an efficient blob adaptation by changing the blob-size and its footprint on the detector depending on the neighboring blobs is performed [3]. To perform a MC iterative reconstruction, a MVF of the chosen ROI has to be determined. For cardiac CT, several methods to estimate the heart’s motion vector field (MVF) have been proposed in literature [8,9]. Elastic (or non-rigid) image registration techniques (EIR) [10,11] have been used extensively in medical imaging. In this paper, EIR is applied to recover the MVF of the chosen cardiac ROI. The basic EIR technique can be summarized as follows. Given a pair of images fr and ft, which can be called reference and test, the main task of the EIR is to find a correspondence function g such that fw(x)=ft(g(x))≈ fr, where fw is the warped test image. The EIR method presented here works directly with all voxel-intensity values of the images being registered. The correspondence between the discrete and continuous versions of the images and of the deformation field g is established using cubic B-Splines interpolation [12,13] and the optimization is based on a gradient descent method. Gradient-based registration methods typically attempt to find the best deformation g that minimizes a similarity criterion. In this work, the sum of squared difference (SSD) between the images fr and ft was used as criterion. Moreover, to avoid irregular and non-invertible solution, a topology-preserving penalty function [14] was added to the similarity criterion. The Marquardt-Levemberg optimization method is applied to minimize the criterion (Fig.1). Finally, to increase the speed and efficiency of the EIR algorithm a multi-resolution scheme is implemented [15]. In this contribution, a 4D ROI data set is reconstructed at equidistant phase points throughout the entire cardiac cycle. Hence, EIRs are performed between the volume reconstructed at the chosen reference phase and all other volumes reconstructed at different phases within the R-R interval. The deformation fields obtained as output of the registration correspond to the ROI’s MVFs from the reference phase to all other cardiac phases.

Fig.1 The flowchart of the EIR algorithm. First, given a couple of input images, a multiresolution approach is applied and a set of gradually reduced versions of the original images is created. Hence, a B-spline interpolator is used to determine the continuous version of the discrete input images and of the deformation field g. Finally, an iterative optimization process is performed to determine the optimal B-spline coefficients ck of the deformation function g that minimize the similarity criterion E.

Due to the severe MBA present on reconstructions at phases of fast cardiac motion, it’s advantageous to apply the EIR only between images related to slow motion phases to avoid matching errors. Therefore, to fill this MVF estimation gap, in these phases the MVFs will be determined by cubic spline interpolation in the temporal domain. The MVFs estimated at slow motion phases are used as knots.

3. Results The MC iterative ROI reconstruction method is applied to clinical cases. The data sets are acquired on a Brilliance 40(64) CT scanner (Philips Healthcare). To study the effect of motion compensation, the MC gated AWOSML method is compared with a non-compensated gated AWOSML method. The reference phase is selected in a cardiac phase of fast motion (50% RR) with a fixed gating window width of 40% RR. The RCA is chosen as ROI with a radius of 25 mm. The 4D ROI images are generated by an AWCR [16] method at phase points within the range from 30 to 70% RR in steps of 5% RR with a fixed gating window width of 22% RR. This gating window width is the smallest possible for the given scanner parameters and heart rate [17]. The MVFs are determined by these AWCR images from phase to phase by EIR and motion field interpolation as described in the previous section. Two multiresolution levels are used, and for both levels the deformation field knots spacing is every 20 blobs. The iterative reconstructions are performed using 15 subsets, each one filled with 500 projections. The order of the subsets is determined randomly. A simple cubic grid of blobs is used (0.5 mm3). Reconstruction results for a clinical dataset after 15 iterations are shown in Fig.2. Image quality of gated reconstructions strongly depends on the chosen cardiac phase. For example, in the case of gated AWOSML reconstructions in a phase of fast cardiac motion, the reconstructed images are strongly blurred (Fig.2). While, MC gated AWOSML reconstructions lead to sharp images where the residual MBA is almost removed (Fig.2). Moreover, even secondary vessels (e.g. marginal acute branches) are recovered and visible.

Fig.2 The axial, coronal and sagittal images and the 3D volume rendering of a human RCA. In order, the gated AWOSML (left column) and the MC gated AWOSML reconstructed images (right column) are shown. The aorta (a), the RCA vessel and its first marginal acute branch (b), its second marginal acute branch (c), the RCA’s interventricular posterior branch (d), the outlet of the RCA from the aorta (e), a coronal view of the RCA (f), a sagittal view of the RCA and its first marginal acute branch (h), the RCA’s right conal branch (i), and the 3D volume rendering of the RCA (j) are shown. (at 50% RR, ROI’s radius=25 mm, Level=0 HU, Window=700 HU)

4. Conclusions A MC iterative reconstruction method of a cardiac ROI for CT has been proposed. It was applied successfully to clinical cases acquired in a helical acquisition mode with a parallel ECG recording. The MVF was derived by elastic image registration on a set of precomputed 3D gated-reconstructed images. Using the estimated MVF with the MC gated AWOSML method, sharp RCA images are achieved and their quality outperforms the quality of the gated AWOSML reconstructions.

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