Correction of patient motion in cone-beam CT using 3D-2D registration

被引:26
|
作者
Ouadah, S. [1 ]
Jacobson, M. [1 ]
Stayman, J. W. [1 ]
Ehtiati, T. [2 ]
Weiss, C. [1 ]
Siewerdsen, J. H. [1 ]
机构
[1] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21205 USA
[2] Siemens Med Solutions USA Inc, Imaging & Therapy Syst, Hoffman Estates, IL 60192 USA
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2017年 / 62卷 / 23期
基金
美国国家卫生研究院;
关键词
3D-2D registration; C-arm; cone-beam CT; image-guided interventions; motion correction; image quality; motion artifact; IMAGE REGISTRATION; COMPENSATION; CALIBRATION; REDUCTION;
D O I
10.1088/1361-6560/aa9254
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (similar to 5-60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D-2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptationevolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5-15 cm translations and 5-15 degrees rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was > 0.995, with significant improvement (p < 0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D-2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.
引用
收藏
页码:8813 / 8831
页数:19
相关论文
共 50 条
  • [21] Translational motion correction algorithm for truncated cone-beam CT using opposite projections
    Gu, Jawook
    Bae, Woong
    Ye, Jong Chul
    [J]. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2017, 25 (06) : 927 - 944
  • [22] Breathing motion in cone-beam CT
    Rit, S.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2019, 133 : S119 - S120
  • [23] Correction of C-arm projection matrices by 3D-2D rigid registration of CT-images using mutual information
    Müller, U
    Bruck, S
    Hesser, J
    Männer, R
    [J]. BIOMEDICAL IMAGE REGISTRATION, 2003, 2717 : 161 - 170
  • [24] A motion correction approach for oral and maxillofacial cone-beam CT imaging
    Sun, Tao
    Jacobs, Reinhilde
    Pauwels, Ruben
    Tijskens, Elisabeth
    Fulton, Roger
    Nuyts, Johan
    [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2021, 66 (12):
  • [25] Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement
    Munbodh, Reshma
    Jaffray, David A.
    Moseley, Douglas J.
    Chen, Zhe
    Knisely, Jonathan P. S.
    Cathier, Pascal
    Duncan, James S.
    [J]. MEDICAL PHYSICS, 2006, 33 (05) : 1398 - 1411
  • [26] Evaluation of the motion and registration accuracy of the Elekta Symmetry 4D cone-beam mode
    Christiansen, Eric
    Neath, Cathy
    Vandermeer, Aaron
    [J]. MEDICAL PHYSICS, 2022, 49 (08) : 5650 - 5650
  • [27] Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images
    Zhang, Yungeng
    Pei, Yuru
    Qin, Haifang
    Guo, Yuke
    Ma, Gengyu
    Xu, Tianmin
    Zha, Hongbin
    [J]. MACHINE LEARNING IN MEDICAL IMAGING: 9TH INTERNATIONAL WORKSHOP, MLMI 2018, 2018, 11046 : 371 - 379
  • [28] Directional Interpolation for Motion Weighted 4D Cone-Beam CT Reconstruction
    Zhang, Hua
    Sonke, Jan-Jakob
    [J]. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2012, PT I, 2012, 7510 : 181 - 188
  • [29] Patient alignment using megavoltage cone-beam CT
    Morin, O.
    Gillis, A.
    Aubin, M.
    Chen, J.
    Mu, G.
    Bocci, K.
    Pouliot, J.
    [J]. MEDICAL PHYSICS, 2006, 33 (06) : 2280 - 2281
  • [30] 3D-2D IMAGE REGISTRATION BY NONLINEAR REGRESSION
    Gouveia, Ana R.
    Metz, Coert
    Freire, Luis
    Klein, Stefan
    [J]. 2012 9TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2012, : 1343 - 1346