Development and evaluation of an articulated registration algorithm for human skeleton registration

Accurate registration over multiple scans is necessary to assess treatment response of bone diseases (e.g. metastatic bone lesions). This study aimed to develop and evaluate an articulated registration algorithm for the whole-body skeleton registration in human patients. In articulated registration, whole-body skeletons are registered by auto-segmenting into individual bones using atlas-based segmentation, and then rigidly aligning them. Sixteen patients (weight = 80-117 kg, height = 168-191 cm) with advanced prostate cancer underwent the pre- and mid-treatment PET/CT scans over a course of cancer therapy. Skeletons were extracted from the CT images by thresholding (HU>150). Skeletons were registered using the articulated, rigid, and deformable registration algorithms to account for position and postural variability between scans. The inter-observers agreement in the atlas creation, the agreement between the manually and atlas-based segmented bones, and the registration performances of all three registration algorithms were all assessed using the Dice similarity index-DSIobserved, DSIatlas, and DSIregister. Hausdorff distance (dHausdorff) of the registered skeletons was also used for registration evaluation. Nearly negligible inter-observers variability was found in the bone atlases creation as the DSIobserver was 96 ± 2%. Atlas-based and manual segmented bones were in excellent agreement with DSIatlas of 90 ± 3%. Articulated (DSIregsiter = 75 ± 2%, dHausdorff = 0.37 ± 0.08 cm) and deformable registration algorithms (DSIregister = 77 ± 3%, dHausdorff = 0.34 ± 0.08 cm) considerably outperformed the rigid registration algorithm (DSIregsiter = 59 ± 9%, dHausdorff = 0.69 ± 0.20 cm) in the skeleton registration as the rigid registration algorithm failed to capture the skeleton flexibility in the joints. Despite superior skeleton registration performance, deformable registration algorithm failed to preserve the local rigidity of bones as over 60% of the skeletons were deformed. Articulated registration is superior to rigid and deformable registrations by capturing global flexibility while preserving local rigidity inherent in skeleton registration. Therefore, articulated registration can be employed to accurately register the whole-body human skeletons, and it enables the treatment response assessment of various bone diseases.

[1]  Ludvig Paul Muren,et al.  Propagation of target and organ at risk contours in radiotherapy of prostate cancer using deformable image registration , 2010, Acta oncologica.

[2]  C. Whyne,et al.  Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method. , 2007, Medical physics.

[3]  S. Nekolla,et al.  Reproducibility and accuracy of non-invasive measurement of infarct size in mice with high-resolution PET/CT , 2012, Journal of Nuclear Cardiology.

[4]  Deshan Yang,et al.  A fast inverse consistent deformable image registration method based on symmetric optical flow computation , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[5]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.

[6]  Benoît Macq,et al.  Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  Stephen T. C. Wong,et al.  Computer-assisted quantitative evaluation of therapeutic responses for lymphoma using serial PET/CT imaging. , 2010, Academic radiology.

[8]  Dong-Gyu Sim,et al.  Object matching algorithms using robust Hausdorff distance measures , 1999, IEEE Trans. Image Process..

[9]  A. Lomax,et al.  Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer , 2012, Radiation oncology.

[10]  Valerie Duay,et al.  Non-rigid registration algorithm with spatially varying stiffness properties , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[11]  I. Buvat,et al.  A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology , 2012, Physics in medicine and biology.

[12]  M. Staring,et al.  A rigidity penalty term for nonrigid registration. , 2007, Medical physics.

[13]  Stewart Gaede,et al.  An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Albert M. Vossepoel,et al.  FULLY AUTOMATED WHOLE-BODY REGISTRATION IN MICE USING AN ARTICULATED SKELETON ATLAS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[15]  J. Lagendijk,et al.  Contour propagation in MRI-guided radiotherapy treatment of cervical cancer: the accuracy of rigid, non-rigid and semi-automatic registrations , 2009, Physics in medicine and biology.

[16]  Brendan McCane,et al.  On Benchmarking Optical Flow , 2001, Comput. Vis. Image Underst..

[17]  L. Dai,et al.  Small-Animal PET/CT Assessment of Bone Microdamage in Ovariectomized Rats , 2011, The Journal of Nuclear Medicine.

[18]  Robert Jeraj,et al.  Early assessment of treatment response in patients with AML using [(18)F]FLT PET imaging. , 2011, Leukemia research.

[19]  Joseph O. Deasy,et al.  Technical Note: DIRART – A software suite for deformable image registration and adaptive radiotherapy research , 2010 .

[20]  T. Mackie,et al.  Fast free-form deformable registration via calculus of variations , 2004, Physics in medicine and biology.

[21]  W. Bautz,et al.  Clinical evaluation of bone-subtraction CT angiography (BSCTA) in head and neck imaging , 2006, European Radiology.

[22]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[23]  M. Spivak A comprehensive introduction to differential geometry , 1979 .

[24]  Boudewijn P. F. Lelieveldt,et al.  Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications , 2010, Molecular Imaging and Biology.

[25]  Boudewijn P. F. Lelieveldt,et al.  Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data , 2010, Medical Image Anal..

[26]  Jane Higgins,et al.  Comparison of spine, carina, and tumor as registration landmarks for volumetric image-guided lung radiotherapy. , 2009, International journal of radiation oncology, biology, physics.

[27]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  R Calandrino,et al.  An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy , 2011, Physics in medicine and biology.

[29]  K. Brock,et al.  Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy. , 2009, Medical physics.

[30]  Damini Dey,et al.  Automated 3-dimensional registration of stand-alone (18)F-FDG whole-body PET with CT. , 2003, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[31]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[32]  R. Jeraj,et al.  Use of articulated registration for response assessment of individual metastatic bone lesions , 2014, Physics in medicine and biology.

[33]  H. Venema,et al.  CT angiography of the circle of Willis and intracranial internal carotid arteries: maximum intensity projection with matched mask bone elimination-feasibility study. , 2001, Radiology.

[34]  Charl P. Botha,et al.  Articulated Planar Reformation for Change Visualization in Small Animal Imaging , 2010, IEEE Transactions on Visualization and Computer Graphics.

[35]  Dinggang Shen,et al.  ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images , 2008, IEEE Transactions on Medical Imaging.

[36]  M. Staring,et al.  Nonrigid registration with tissue-dependent filtering of the deformation field , 2007, Physics in medicine and biology.

[37]  Boudewijn P. F. Lelieveldt,et al.  Automated Bone Volume and Thickness Measurements in Small Animal Whole-Body MicroCT Data , 2011, Molecular Imaging and Biology.

[38]  Lars Edenbrandt,et al.  Semi-automatic analysis of standard uptake values in serial PET/CT studies in patients with lung cancer and lymphoma , 2012, BMC Medical Imaging.

[39]  H. Venema,et al.  Multisection CT venography of the dural sinuses and cerebral veins by using matched mask bone elimination. , 2004, AJNR. American journal of neuroradiology.

[40]  Juha Koikkalainen,et al.  Fast and robust multi-atlas segmentation of brain magnetic resonance images , 2010, NeuroImage.

[41]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[42]  Anand Rangarajan,et al.  A new point matching algorithm for non-rigid registration , 2003, Comput. Vis. Image Underst..

[43]  Cristian Lorenz,et al.  Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics. , 2011, Medical physics.

[44]  Measuring the "unmeasurable": assessment of bone marrow response to therapy using FDG-PET in patients with lymphoma. , 2010, Academic radiology.

[45]  Todd E Peterson,et al.  Longitudinal live animal micro-CT allows for quantitative analysis of tumor-induced bone destruction. , 2011, Bone.

[46]  Pei Wang,et al.  The Application Research of Optical Flow Method in Target Volume Motion Image Registration , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[47]  Xia Li,et al.  Automatic nonrigid registration of whole body CT mice images. , 2008, Medical physics.

[48]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[49]  Cristian Lorenz,et al.  Reproducibility of four-dimensional computed tomography-based lung ventilation imaging. , 2012, Academic radiology.

[50]  Iddo Wernick,et al.  Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes. , 2011, International journal of radiation oncology, biology, physics.