MRI and CT images were registered with image fusion on the Variseed system. The prostate was contoured on the MR imageswhile seeds were identified on CT using Variseed. The prostate was divided into elements of approximately 0.03 cc and an FEMalgorithm was used to model the displacement of each element over the three time points. The averaged fractional magnitudesof displacement (�DP) in the medial-lateral (ML), anterior-posterior (AP), and caudal-cranial (CC) direction about the centerof mass (COM) of prostate were calculated. These quantities represent the average change in the prostate dimension along thethree axes. The sequential changes of the average distance of seeds from the COM of prostate (�DS) on the three principal axeswere also determined. They correspond to the changes in dimensions of the seed cluster over time. Correlations were performedbetween �DP and �DS for intra-prostatic seeds and repeated for all seeds.

Results: The prostate volume decreased by 8% and 13 % from Day 1 to 8 and from Day 8 to 30 respectively. �DP on the threeprincipal axes were between -0.9 to -2% for Day 1 to 8 and -4 to -6.5 % for Day 8 to 30. �DS was found to be between -0.98to 0.23 mm for Day 1 to 8 and between -0.56 to 0.26 mm for Day 8 to 30. For Day 1 to 8, there was a significant correlationbetween �DP and �DS of intra-prostatic seeds in the ML (r�0.80, p � 0.001) and CC (r�0.77, p�0.001) direction. Thecorrelations became weaker but still significant when all seeds were considered with r�0.47 (p�0.04) and r�0.48 (p�0.04)correspondingly. Correlations in the AP direction as well as for all directions for Day 8 to 30 were not significant.

Conclusions: There is a strong linear relationship between prostate edema and intra-prostatic seed displacement in the ML andCC direction from Day 1 to Day 8. The decrease in the correlation coefficients for Day 8 to 30 suggests that the implanted seedswere not as compliant to prostate deformation during this later period. The results of non-significant correlations when all seedswere considered may indicate that extracapsular seeds do not move linearly with prostate edema. These results of prostatedeformation and seed movement post implantation should be taken into account in edema modeling for the calculation of totaldose.

Author Disclosure: I.W.T. Yeung, None; S. Chen, None; Y. Cho, None; D. Taussky, None; A. Beiki-Ardakani, None; J. Crook,None.

2787 Comparison of 2D With 3D Image Alignment Techniques for Head and Neck

E. L. Sebastian, L. H. Kim, J. Wloch, Q. Wu, D. Yan

William Beaumont Hospital, Royal Oak, MI

Purpose/Objective(s): Studies have shown that in head and neck IMRT treatment plans, dose errors are more sensitive tosystematic set-up errors than random errors. For example, results from one study show that for simultaneous integrated boostIMRT head and neck treatments, when the systematic error ��3 mm, dose errors to the target were greater than 3% for 50%of the plans. Corrections based on 2D and 3D image registrations have been performed on patients at our institution withdiffering results in error prediction between the two techniques. These differences suggested that systematic errors of up to 3mm could not be reliably detected. The purpose of this study is to determine the cause of those differences.

Materials/Methods: Studies were made to quantify four users’ ability to detect known shifts and rotations using a homemadehigh contrast cylindrical phantom with BBs and a Rando head phantom. Both 2D and 3D image alignment methods were used.The 2D method involved creating a template based on bony anatomy from an orthogonal set of digitally reconstructedradiographs (DRRs) from the planning CT. The template was aligned to corresponding megavoltage (MV) electronic portalimages (EPIs). The 3D method involved acquiring conebeam CT (CBCT) images, importing into Pinnacle®, and manuallyaligning with the planning CT. Elekta’s XVI automatic alignment system was also used. Known translational and rotationalshifts were made on both phantoms and images acquired for both methods. Alignment comparisons were made between 2D and3D as well as between different users. Alignment corrections acquired on patients treated in the head and neck regions andaligned using the same methods were also reviewed and compared with the phantom data.

Results: The differences between the 2D and 3D manual registrations for the cylinder phantom were (rt/lt, ant/post, sup/inf inmm) -0.5 � 0.9, 0.1 � 1.0 and -0.5 � 0.7, and for the head phantom, -2.0 � 0.6, 1.3 � 0.6 and 1.1 � 1.9. Results from thepatient data, 0.8 � 2.4, 0.3 � 2.1, -1.3 � 1.6, exhibited larger standard deviations. The absolute accuracy of both methods ofmanual registration depended upon the user and their experience. Variablility of automatic registration was smaller, but inisolated cases this method yielded obviously incorrect results.

Conclusions: It was found that factors contributing to image quality of the planning CT, DRR, EPI, and CBCT affected theability of users to detect shifts. Each user was affected differently by changes in the image quality resulting in a differentsystematic error for each user and each technique. 3D manual registration is not necessarily more accurate or reproducible than2D manual registration depending on the user. Under optimal conditions, which can vary from user to user, manual registrationcan perform comparably to automatic registration.

Author Disclosure: E.L. Sebastian, None; L.H. Kim, None; J. Wloch, None; Q. Wu, None; D. Yan, None.

2788 Toward Magnetic Resonance (MR) Simulation: A Knowledge-Guided Active-Model Method of SkullSegmentation for Generating MR Digitally Reconstructed Radiographs (DRRs)

Z. Y. Shan1, C. Hua1, Q. Ji1, X. Ying1, C. Parra2, W. E. Reddick1, M. J. Krasin1, L. E. Kun1, T. E. Merchant1

1St Jude Children’s Research Hospital, Memphis, TN, 2University of Memphis, Memphis, TN

Purpose/Objective(s): Advantages of MR-only simulation (MR sim) are that it reduces radiation exposure to pediatric patients,removes the uncertainty of registration of computed tomography (CT) and MR images of soft tissue tumors, and can performbiological MR imaging in treatment position at the time of simulation. However, its implementation is hindered by the lowintensity of skull signals and poor differentiation between bone and air on MR images. Generating a high-quality MR DRRdirectly from raw MR images to show bony structures for verifying treatment position is challenging. Therefore, we proposea knowledge-guided active-model method to extract skulls from MR datasets.

Materials/Methods: We constructed a population-based skull template by spatially aligning and averaging the CT datasets of10 children’s heads and segmenting them with bone intensity thresholds. The template was further developed into a

S650 I. J. Radiation Oncology ● Biology ● Physics Volume 66, Number 3, Supplement, 2006

3-dimensional triangular mesh model with inner and outer layers based on the surface curvature of the skull. The triangularmesh model was transformed to an individual MR dataset using the same transformation matrix obtained by registering theaveraged CT images to the individual MR dataset using an affine registration based on normalized mutual information. Thevertex of each triangle in the template was then actively adjusted, using the Greedy algorithm, to a position of minimumenthalpy (the sum of the external force required to pull each vertex to the edge of the patient’s skull and the internal forcerequired to maintain the continuity of the adjusted template). The converged mesh structure between the inner and outer surfaceswas then filled with a CT Hounsfield number of 1000 before being written back to the DICOM image files.

Results: Segmentation of a typical pediatric patient’s skull took 4 hours on an SGI Origin 300 computer without optimizationof computation algorithms. The extracted cranium including orbital bones, the base of skull, and upper jaw are clearly visible.The MR DRR was displayed using the Philips ACQSIM software originally designed for CT datasets without modification.Anatomic landmarks routinely used to verify patient setup are easily identifiable. The similarity of skulls extracted from CT andMR images of 10 additional patients is being investigated.

Conclusions: We developed a knowledge-guided method to segment skulls on MR images, thus allowing simulation by MRto be verified by kilovoltage or megavoltage X-ray imaging at the time of treatment. The ability to extract bony structures fromMR images will also allow correction of heterogeneity in MR-based treatment plans and thereby increase the accuracy of dosecalculations.

Author Disclosure: Z.Y. Shan, None; C. Hua, None; Q. Ji, None; X. Ying, None; C. Parra, None; W.E. Reddick, None; M.J.Krasin, None; L.E. Kun, None; T.E. Merchant, None.

2789 Registration of 4D CBCT and 4D CT for Extracranial Stereotactic Treatments

E. Elder1, E. Schreibmann1, T. Li2, T. Fox1, L. Xing2, J. Crocker1, J. Landry1

1Emory University School of Medicine, Atlanta, GA, 2Stanford University School of Medicine, Stanford, CA

Purpose/Objective(s): A strategy was recently reported to acquire high quality phase-resolved (4D) cone-beam CT (CBCT)images based on phase-binning of the CBCT projection data, generating artifact-free 4D CBCT that include the respiratorymotion. The 4D CBCT data acquired prior to the treatment can be used to recalculate and verify the delivered dose to the patienton that treatment day for extracranial SRS. For all these applications, the 4D CBCT image has to be registered to the planning4D CT images. In this work we report the details of a voxel-based automated deformable registration algorithm devised tocorrelate spatio-temporal the 4D CBCT image to the planning 4D CT.

Materials/Methods: Under the developed image registration method, all bins comprising the CT or CBCT images areregistered simultaneously. First, a rigid alignment is described by a shift and a scaling in both spatial and temporal axes tocompensate for differences in the number of bins and align the first bin in the two 4D images on the time axis. Then thedeformations produced by different breathing pattern are compensated by using a deformable registration based on a hybridapproach combining the advantages of a B-Spline and a level set fluid flow methods. The B-Spline model is responsible forcorrecting bulk deformations and is stable to the noise in the CBCT images. The level set method then updates the B-Splineresults to the fine anatomical details.

Results: The 4D CBCT images of a pancreatic cancer patient were acquired using the on-board CBCT imaging system usedin this work was the On-Board Imaging (OBI) system (kV source/detector arms) of a TrilogyTM treatment system (VarianMedical Systems, Palo Alto, CA). Registration using a grid of 10 nodes per spatial and 5 nodes pet temporal dimensions fora series composed of 9 binned phases for the 4D CT and 4 phases for the 4D CBCT was feasible on a standard desktopcomputer. After the registration, the CBCT image was resampled to 9 bins corresponding to the number of phases in the CTimage. A mean accuracy of 2.5 mm is achieved in all phases, as assessed by checkerboards comparison the CT and CBCTimages. Interpolation between time bins ensured a smooth description of the respiratory motion.

Conclusions: The 4D registration increases localization accuracy in CBCT-based setup for extra-cranial SRS because (a) abetter match is possible since the respiratory motion is observed in both images and (b) artifacts caused by intra-scanning organmotion are eliminated from the 4D CBCT image.

Author Disclosure: E. Elder, None; E. Schreibmann, None; T. Li, None; T. Fox, None; L. Xing, None; J. Crocker, None; J.Landry, None.

2790 Auto Contour Mapping in CBCT for Adaptive Therapy Treatment Planning

P. Peng, M. Chao, Q. Le, T. Li, A. Hsu, T. A. Pawlicki, L. Xing

Stanford University School of Medicine, Stanford, CA

Purpose/Objective(s): In order to incorporate on-board cone beam CT (CBCT) data to adaptively modify the patient treatmentplan or even generate online treatment plan in the future, organ segmentation on CBCT is indispensable. We implemented anapproach to automatically map the manually delineated contours on the planning CT to CBCT image sets using a controlvolume mapping technique.

Materials/Methods: Two head and neck and two prostate cancer patients were selected. For each patient, a planning CT and3�5 CBCTs were acquired using Varian Trilogy. The tumor target and relevant organs (such as brainstem, spinal cord, parotidgland for the head and neck cases, and rectum and ladder for the prostate cases) were manually segmented on the planning CTby a physician in a Varian Eclipse treatment planning system. Along the segmented contours a series of small control volumes(� 0.5cm) were auto-placed and collectively mapped to CBCT images using rigid transformation. To optimize the mapping asimple mean squares function was used as the auto-mapping metric and an iterative optimization algorithm was employed tooptimize the function. The positions of the mapped control volumes were then fine-tuned to accommodate any possibledeformations. To accomplish this, an energy function consisting of two terms was introduced to determine the final locationsof the control volumes. The first term is the intensity similarity between the control volumes from the planning CT and the

S651Proceedings of the 48th Annual ASTRO Meeting