Reference Publications | Emory School of Medicine

4DCT

Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK, Guerrero T. 2009. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 54 1849-1870. (PMB Featured Article 2009 - Free Access)
Castillo E, Castillo R, Zhang Y, Guerrero T. 2009. Compressible image registration for thoracic computed tomography images. Journal of Medical and Biological Engineering 29 222-233. (JMBE Featured Article 2009 - Free Access)
Gu X, Pan H, Liang Y, Castillo R, Yang D, Choi D, Castiilo E, Majumdar A, Guerrero T, Jiang SB. 2009. Implementation and evaluation of various demons deformable image registration algorithms on a GPU. Phys Med Biol 55 207-219.
Castillo E, Castillo R, Martinez J, Shenoy M, Guerrero T. 2009. Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 55 305-327.
Castillo E, Castillo R, White B, Rojo J, Guerrero T. 2012. Least median of squares filtering of locally optimal point matches for compressible flow image registration. Phys Med Biol 57 4827-4833.
Hoog Antink CB, Singh T, Singla P, Podgorsak M. 2013. Evaluation of advanced Lukas-Kanade optical flow on thoracic 4D-CT. J Clin Monit Comput 27 (2).
Hermann S and Werner R. TV-L1-based 3D Medical Image Registration with the Census Cost Function. 6th Pacific-Rim Symposium on Image and Video Technology 2013, Guanajuato, Mexico.
Hermann S and Werner R. High Accuracy Optical Flow for 3D Medical Image Registration using the Census Cost Function. 6th Pacific-Rim Symposium on Image and Video Technology 2013, Guanajuato, Mexico.
Muenzing SEA, van Ginneken B, Pluim JPW. Knowledge Driven Regularization of the Deformation Field for PDE Based Non-Rigid Registration Algorithms. in: Medical Image Analysis for the Clinic - A Grand Challenge. CreateSpace Independent Publishing Platform, Aug. 2010. (n.b.: floating points used for positional error calculation)
Rühaak J, Heldmann S, Kipshagen T, Fischer B. Highly Accurate Fast Lung CT Registration. SPIE Medical Imaging 2013: Image Processing, Lake Buena Vista, Florida, USA. February 2013.
Polzin T, Rühaak J, Werner R, Strehlow J, Heldmann S, Handels H, Modersitzki J. Combining Automatic Landmark Detection and Variational Methods for Lung CT Registration. Proc. Fifth International MICCAI Workshop on Pulmonary Image Analysis (PIA 2013), Nagoya, Japan. September 2013.
Hermann S. Evaluation of Scan-Line Optimization for 3D Medical Image Registration. IEEE Conference on Computer Vision and Pattern Recognition 2014, Columbus, Ohio, USA. June 2014.
König L and Rühaak J. A fast and accurate parallel algorithm for non-linear image registration using normalized gradient fields. IEEE International Symposium on Biomedical Imaging 2014, Beijing, China. April 2014.
Vishnevskiy V, Gass T, Szekely G, Goksel O. Total Variation Regularization of Displacements in Parametric Image Registration. In: Abdominal Imaging. Computational and Clinical Applications. Lecture Notes in Computer Science. Vol. 8676, pp: 211-220. 2014.
Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration. IEEE Transactions on Medical Imaging. DOI 10.1109/TMI.2016.2610583.
Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration. IEEE Transactions on Medical Imaging. DOI 10.1109/TMI.2016.2610583. (Revision: https://github.com/visva89/pTVreg/blob/master/DIRandCOPD.md)

COPD

Castillo R, Castillo E, Fuentes D, Ahmad M, Wood AM, Ludwig MS, Guerrero T. 2013. A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive. Phys Med Biol 58 2861-2877.
Rühaak J, Heldmann S, Kipshagen T, Fischer B. Highly Accurate Fast Lung CT Registration. SPIE Medical Imaging 2013: Image Processing, Lake Buena Vista, Florida, USA. February 2013.
Polzin T, Rühaak J, Werner R, Strehlow J, Heldmann S, Handels H, Modersitzki J. Combining Automatic Landmark Detection and Variational Methods for Lung CT Registration. Proc. Fifth International MICCAI Workshop on Pulmonary Image Analysis (PIA 2013), Nagoya, Japan. September 2013.
Castillo E, Castillo R, Fuentes D, Guerrero T. 2014. Computing Global Minimizers to a Constrained B-spline Image Registration Problem from Optimal L1 Perturbations to Block Match Data. Medical Physics 41 (4).
Hermann S. Evaluation of Scan-Line Optimization for 3D Medical Image Registration. IEEE Conference on Computer Vision and Pattern Recognition 2014, Columbus, Ohio, USA. June 2014.
Heinrich MP, Handels H, Simpson IJA. Estimating Large Lung Motion in COPD Patients by Symmetric Regularised Correspondence Fields. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2015. LNCS Springer 2015.
Polzin T, Niethammer M, Heinrich MP, Handels H, Modersitzki J. Memory Efficient LDDMM for Lung CT. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2016. LNCS Springer 2016.
Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration. IEEE Transactions on Medical Imaging. DOI 10.1109/TMI.2016.2610583.
Rühaak, J., Polzin, T., Heldmann, S., Simpson, I. J. A., Handels, H., Modersitzki, J., und Heinrich, M. P. Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration. IEEE Transactions on Medical Imaging 36 (8), 2017.
Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration. IEEE Transactions on Medical Imaging. DOI 10.1109/TMI.2016.2610583. (Revision: https://github.com/visva89/pTVreg/blob/master/DIRandCOPD.md)