A deformable phantom for 4D radiotherapy verification: design and image registration evaluation.

Motion of thoracic tumors with respiration presents a challenge for three-dimensional (3D) conformal radiation therapy treatment. Validation of techniques aimed at measuring and minimizing the effects of respiratory motion requires a realistic deformable phantom for use as a gold standard. The purpose of this study was to develop and study the characteristics of a reproducible, tissue equivalent, deformable lung phantom. The phantom consists of a Lucite cylinder filled with water containing a latex balloon stuffed with dampened natural sponges. The balloon is attached to a piston that mimics the human diaphragm. Nylon wires and Lucite beads, emulating vascular and bronchial bifurcations, were uniformly glued at various locations throughout the sponges. The phantom is capable of simulating programmed irregular breathing patterns with varying periods and amplitudes. A tissue equivalent tumor, suitable for holding radiochromic film for dose measurements was embedded in the sponge. To assess phantom motion, eight 3D computed tomography data sets of the static phantom were acquired for eight equally spaced positions of the piston. The 3D trajectories of 12 manually chosen point landmarks and the tumor center-of-mass were studied. Motion reproducibility tests of the deformed phantom were established on seven repeat scans of three different states of compression. Deformable image registration (DIR) of the extreme breathing phases was performed. The accuracy of the DIR was evaluated by visual inspection of image overlays and quantified by the distance-to-agreement (DTA) of manually chosen point landmarks and triangulated surfaces obtained from 3D contoured structures. In initial tests of the phantom, a 20-mm excursion of the piston resulted in deformations of the balloon of 20 mm superior-inferior, 4 mm anterior-posterior, and 5 mm left-right. The change in the phantom mean lung density ranged from 0.24 (0.12 SD) g/cm3 at peak exhale to 0.19 (0.12 SD) g/cm3 at peak inhale. The SI displacement of the landmarks varied between 94% and 3% of the piston excursion for positions closer and farther away from the piston, respectively. The reproducibility of the phantom deformation was within the image resolution (0.7 x 0.7 x 1.25 mm3). Vector average registration accuracy based on point landmarks was found to be 0.5 (0.4 SD) mm. The tumor and lung mean 3D DTA obtained from triangulated surfaces were 0.4 (0.1 SD) mm and 1.0 (0.8 SD) mm, respectively. This phantom is capable of reproducibly emulating the physically realistic lung features and deformations and has a wide range of potential applications, including four-dimensional (4D) imaging, evaluation of deformable registration accuracy, 4D planning and dose delivery.

[1]  J. Wong,et al.  The use of active breathing control (ABC) to reduce margin for breathing motion. , 1999, International journal of radiation oncology, biology, physics.

[2]  C Thieke,et al.  An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy , 2006, Physics in medicine and biology.

[3]  Rojano Kashani,et al.  Technical note: a deformable phantom for dynamic modeling in radiation therapy. , 2007, Medical physics.

[4]  C. Ling,et al.  Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images. , 2006, Medical physics.

[5]  C. Ling,et al.  Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. , 2002, Medical physics.

[6]  A. Boyer,et al.  Radiation dose reduction in four-dimensional computed tomography. , 2005, Medical physics.

[7]  R. Mohan,et al.  Motion adaptive x-ray therapy: a feasibility study , 2001, Physics in medicine and biology.

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

[9]  P. J. Keall,et al.  Potential radiotherapy improvements with respiratory gating , 2009, Australasian Physics & Engineering Sciences in Medicine.

[10]  David J. Hawkes,et al.  Voxel Similarity Measures for 3D Serial MR Brain Image Registration , 2000, IEEE Trans. Medical Imaging.

[11]  Geoffrey G. Zhang,et al.  Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method. , 2004, Physics in medicine and biology.

[12]  P. Keall 4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[13]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[14]  Cedric X. Yu,et al.  Gated CT imaging using a free-breathing respiration signal from flow-volume spirometry. , 2005, Medical physics.

[15]  Steve B. Jiang,et al.  An experimental investigation on intra-fractional organ motion effects in lung IMRT treatments. , 2003, Physics in medicine and biology.

[16]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[17]  Radhe Mohan,et al.  Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. , 2005, Medical physics.

[18]  D. Louis Collins,et al.  Animal: Validation and Applications of Nonlinear Registration-Based Segmentation , 1997, Int. J. Pattern Recognit. Artif. Intell..

[19]  K. Lam,et al.  Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing. , 1996, International journal of radiation oncology, biology, physics.

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

[21]  Rojano Kashani,et al.  Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms. , 2007, Medical physics.

[22]  K. Brock,et al.  Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.

[23]  L Dong,et al.  Quantification of accuracy of the automated nonlinear image matching and anatomical labeling (ANIMAL) nonlinear registration algorithm for 4D CT images of lung. , 2007, Medical physics.

[24]  Steve Webb,et al.  Quantifying the effect of respiratory motion on lung tumour dosimetry with the aid of a breathing phantom with deforming lungs , 2006, Physics in medicine and biology.

[25]  B Norrlinger,et al.  A novel four-dimensional radiotherapy method for lung cancer: imaging, treatment planning and delivery , 2006, Physics in medicine and biology.

[26]  M van Herk,et al.  A general methodology for three-dimensional analysis of variation in target volume delineation. , 1999, Medical physics.

[27]  Eike Rietzel,et al.  Deformable registration of 4D computed tomography data. , 2006, Medical physics.

[28]  Tinsu Pan,et al.  Four-dimensional computed tomography: image formation and clinical protocol. , 2005, Medical physics.