Cardiac motion correction based on partial angle reconstructed images in x-ray CT.

PURPOSE Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. METHODS A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. RESULTS In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. CONCLUSIONS The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

[1]  K. Stierstorfer,et al.  Weighted FBP--a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch. , 2004, Physics in medicine and biology.

[2]  Nicholas Ayache,et al.  3D tomographic reconstruction of coronary arteries using a precomputed 4D motion field. , 2004, Physics in medicine and biology.

[3]  Maria Iatrou,et al.  Nonrigid registration-based coronary artery motion correction for cardiac computed tomography. , 2012, Medical physics.

[4]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[5]  Pierre Grangeat,et al.  Theoretical framework for a dynamic cone-beam reconstruction algorithm based on a dynamic particle model. , 2002, Physics in medicine and biology.

[6]  M Grass,et al.  Aperture weighted cardiac reconstruction for cone-beam CT , 2006, Physics in medicine and biology.

[7]  Katsuyuki Taguchi,et al.  A fully four-dimensional, iterative motion estimation and compensation method for cardiac CT. , 2012, Medical physics.

[8]  K. Stierstorfer,et al.  Evaluation of a novel CT image reconstruction algorithm with enhanced temporal resolution , 2011, Medical Imaging.

[9]  Michael Grass,et al.  Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography , 2006, IEEE Transactions on Medical Imaging.

[10]  W P Segars,et al.  Realistic CT simulation using the 4D XCAT phantom. , 2008, Medical physics.

[11]  Christian Hofmann,et al.  TRI-PICCS in single source and dual source CT , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[12]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[13]  Christopher Rohkohl,et al.  Improving best-phase image quality in cardiac CT by motion correction with MAM optimization. , 2013, Medical physics.

[14]  T Nielsen,et al.  Cardiac cone-beam CT volume reconstruction using ART. , 2005, Medical physics.

[15]  Michael Grass,et al.  Fully automatic nonrigid registration-based local motion estimation for motion-corrected iterative cardiac CT reconstruction. , 2010, Medical physics.

[16]  M. Budoff,et al.  Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Indi , 2008, Journal of the American College of Cardiology.

[17]  Jie Tang,et al.  Temporal resolution improvement in cardiac CT using PICCS (TRI-PICCS): performance studies. , 2010, Medical physics.

[18]  J. Hsieh,et al.  A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT—helical scanning , 2006, Physics in medicine and biology.

[19]  K. Stierstorfer,et al.  Image reconstruction and image quality evaluation for a dual source CT scanner. , 2003, Medical physics.

[20]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[21]  M Grass,et al.  Motion-compensated iterative cone-beam CT image reconstruction with adapted blobs as basis functions , 2008, Physics in medicine and biology.