Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.

[1]  Alain Trouvé,et al.  Diffeomorphisms Groups and Pattern Matching in Image Analysis , 1998, International Journal of Computer Vision.

[2]  Darryl D. Holm,et al.  Geometry of Image Registration: The Diffeomorphism Group and Momentum Maps , 2013, 1306.6854.

[3]  Nicholas Ayache,et al.  Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach , 2008, MICCAI.

[4]  Laurent Risser,et al.  An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration , 2014, Medical Image Anal..

[5]  R. Werner,et al.  4D CT image artifacts affect local control in SBRT of lung and liver metastases. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[6]  Marleen de Bruijne,et al.  Mass preserving image registration for lung CT , 2012, Medical Image Anal..

[7]  Alain Trouvé,et al.  Geodesic Shooting for Computational Anatomy , 2006, Journal of Mathematical Imaging and Vision.

[8]  Geoffrey G. Zhang,et al.  IMRT treatment plans and functional planning with functional lung imaging from 4D-CT for thoracic cancer patients , 2013, Radiation Oncology.

[9]  Peter A Balter,et al.  Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, using ventilation images for functional avoidance. , 2007, International journal of radiation oncology, biology, physics.

[10]  B. Jalaludin,et al.  A model for decision making for the use of radiotherapy in lung cancer. , 2003, The Lancet. Oncology.

[11]  Wei Shao,et al.  Detecting Out-of-Phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer , 2018, RAMBO+BIA+TIA@MICCAI.

[12]  Wei Shao,et al.  Sensitivity analysis of Jacobian determinant used in treatment planning for lung cancer , 2018, Medical Imaging.

[13]  Maximilian Diehn,et al.  Reducing 4D CT artifacts using optimized sorting based on anatomic similarity. , 2011, Medical physics.

[14]  Eric A. Hoffman,et al.  Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation , 2008, Medical Image Anal..

[15]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[16]  Wei Shao,et al.  N-Phase Local Expansion Ratio for Characterizing Out-of-Phase Lung Ventilation , 2020, IEEE Transactions on Medical Imaging.

[17]  Paul J Keall,et al.  Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients. , 2008, International journal of radiation oncology, biology, physics.

[18]  J M Wild,et al.  Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance. , 2016, Clinical oncology (Royal College of Radiologists (Great Britain)).

[20]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[21]  Simon Rit,et al.  Motion artifact detection in four-dimensional computed tomography images , 2014 .

[22]  Martin Bauer,et al.  Diffeomorphic Density Matching by Optimal Information Transport , 2015, SIAM J. Imaging Sci..

[23]  Martin Szegedi,et al.  4D CT image reconstruction with diffeomorphic motion model , 2012, Medical Image Anal..

[24]  Alex ChiChung Kot,et al.  A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation , 2014, IEEE Signal Processing Letters.

[25]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[26]  P. Fletcher.  Geodesic Regression on Riemannian Manifolds , 2011 .

[27]  Emanuel Todorov,et al.  Optimal Control Theory , 2006 .

[28]  Thomas Guerrero,et al.  The numerical stability of transformation-based CT ventilation , 2017, International Journal of Computer Assisted Radiology and Surgery.

[29]  Jing Cai,et al.  The VAMPIRE challenge: A multi‐institutional validation study of CT ventilation imaging , 2019, Medical physics.

[30]  Darryl D. Holm,et al.  Geometric Mechanics and Symmetry: From Finite to Infinite Dimensions , 2009 .

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  Thomas Guerrero,et al.  Regional Lung Function Profiles of Stage I and III Lung Cancer Patients: An Evaluation for Functional Avoidance Radiation Therapy. , 2016, International journal of radiation oncology, biology, physics.

[33]  François-Xavier Vialard,et al.  Geodesic Regression for Image Time-Series , 2011, MICCAI.

[34]  François-Xavier Vialard,et al.  Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions , 2013, Medical Image Anal..

[35]  S. Kabus,et al.  The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[36]  Gary E. Christensen,et al.  Quantifying ventilation change due to radiation therapy using 4DCT Jacobian calculations , 2018, Medical physics.

[37]  P. Thomas Fletcher,et al.  A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[38]  Josien P. W. Pluim,et al.  Semi-automatic Reference Standard Construction for Quantitative Evaluation of Lung CT Registration , 2008, MICCAI.

[39]  Gary E. Christensen,et al.  A Measure for Characterizing Sliding on Lung Boundaries , 2013, Annals of Biomedical Engineering.

[40]  Guido Gerig,et al.  Geodesic shape regression with multiple geometries and sparse parameters , 2017, Medical Image Anal..

[41]  Polina Golland,et al.  Fast Geodesic Regression for Population-Based Image Analysis , 2017, MICCAI.

[42]  Gary E. Christensen,et al.  Tissue volume and vesselness measure preserving nonrigid registration of lung CT images , 2010, Medical Imaging.

[43]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[44]  P. Wolfe.  Convergence Conditions for Ascent Methods. II , 1969 .

[45]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[46]  J. Ehrhardt,et al.  An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. , 2007, Medical physics.

[47]  Paul Dupuis,et al.  Variational problems on ows of di eomorphisms for image matching , 1998 .

[48]  Indrani Bhattacharya,et al.  CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis , 2020, MICCAI.

[49]  Lei Dong,et al.  Modeling respiratory motion for reducing motion artifacts in 4D CT images. , 2013, Medical physics.

[50]  Tomas Kron,et al.  Ga-68 MAA Perfusion 4D-PET/CT Scanning Allows for Functional Lung Avoidance Using Conformal Radiation Therapy Planning , 2016, Technology in cancer research & treatment.