SU-E-J-185: 4DCT Geometrical Eigenmode Model for Inter-Fraction Evaluation of Tumor Regression and Breathing Pattern Changes

PURPOSE

Evaluate the feasibility of using intra-fractional respiratory dominant eigenmodes of lung deformation as a method to track inter-fractional tumor regression and other physiological changes. Geometrical evaluation by dominant eigenmodes during free-breathing may be implemented as an IGART tool for assessing inter-fractional non-respiratory anatomical variation.

METHODS

Intra-fractional deformable image registration (DIR) is performed on 4DCT scans of lung cancer patients. Principal component analysis is conducted on a set of 10 deformation vector fields for each patient both with and without a lung mask. Leave-one-out cross-validation (LOOCV) is used to assess intra-fractional modeling error among a subset of respiratory phases. The first two dominant eigenmodes of PCA are compared across patients in axial, sagittal, and coronal slices.

RESULTS

Eigenvalues between the first and second dominant respiratory modes decay by a factor of ten for all patients. Intra-fractional LOOCV maximum error is on the order of 1mm inside the lung and 4-6mm for the whole thorax for all patients using the first two dominant eigenmodes. Eigenvalue decay is fastest for patients where diaphragm motion is relatively larger in magnitude. The first two eigenmodes generally demonstrate a strong but smooth superior-inferior motion component with a lesser lateral and anterior-posterior component, while subsequent less dominant eigenmodes produce more random directional eigenvector fields.

CONCLUSION

The two first principal components are consistent amongst patients. Inter-fractional eigenmode comparison may provide a means to track tumor regression and breathing pattern or physiological changes. We expect that patients with more dominant diaphragmatic respiration will allow for more delineated eigenmode classification. Clear dominant eigenmode delineation may allow subsequent less dominant eigenmodes to be inter-fractionally compared as a means to evaluate and compensate for tumor regression.

SU-E-T-476: GPU-Based Monte Carlo Radiotherapy Dose Calculation Using Phase- Space Sources

PURPOSE

To design an efficient method for utilizing phase-space source models in the GPU-based Monte Carlo (MC) dose calculation engine gDPM.

METHODS

In GPU-based MC algorithms, particles are transported in parallel on different threads. Particles of different types and energies can require significantly different execution times. This can cause "thread divergence" and lower efficiency when source particles are read sequentially from a phase-space file. We have developed a strategy for utilizing phase- space files in a GPU compatible manner whereby the particles are grouped into phase-space-lets (PSLs) by type, energy, and location in the phase- space plane. This allows for dose calculations using only particles inside the field opening defined by the secondary collimators. For validation, the gDPM PSL implementation is compared with DOSXYZnrc using a BEAMnrc phase-space source model as input.

RESULTS

Two phase-spaces were generated using a BEAMnrc head model of a 6MV Varian Clinac 21EX, one above the upper jaws used to generate PSLs for gDPM and the other below the lower jaws used for DOSXYZnrc dose calculation. Profiles and depth dose curves for a variety of field sizes were generated in a water phantom. The agreement between gDPM and DOSXYZnrc is within 2% for all field sizes. For the 10 cm × 10 cm field, the calculation times of 650 million histories were 147 CPU hours and 54 GPU seconds for DOSXYZnrc and gDPM, respectively. In addition, we have tested the gDPM PSL implementation for dose calculation in a realistic 7-field IMRT tongue treatment plan. The calculation times were 59 CPU-hours and 66 GPU- seconds for DOSXYZnrc and gDPM for 485 million histories, respectively. Gamma pass rate for the two dose distributions was 99.54% for 3 mm/3% criteria within the 10% isodose.

CONCLUSIONS

Methods for the efficient use of phase-space sources for GPU-based MC dose calculations have been developed.

WE-E-BRB-09: A GPU-Based Monte Carlo QA Tool for IMRT and VMAT

PURPOSE

To develop a GPU-based Monte Carlo (MC) 3D dosimetry quality assurance (QA) tool employing patient geometry and actual delivery information.

METHODS

First, we generate fluence maps at all beam angles from the initial treatment plan. A GPU-based MC dose engine, gDPM, is employed for the secondary dose calculation (SDC) on patient CT. This SDC is used to verify the TPS plan dose (PD) accuracy. Before the 1st treatment fraction, we deliver the treatment plan on a Linac without any phantom setup to obtain machine log files. With the log files, we extract actually delivered fluence maps at all beam angles and perform delivered dose calculation (DDC) using gDPM. The difference between DDC and SDC indicates possible errors in data transferring and machine delivery. Lastly, the comparison between DDC and PD shows the accumulative errors from all the possible sources. Moreover, a web application for this QA tool is developed for clinical use. We have tested this QA tool on 6 patients, 4 VMAT and 2 IMRT patients. We reported mean gamma values and passing rates inside the 20% isodose line; DVH plot and dose difference matrix are also documented.

RESULTS

For all six patients, the gamma passing rates within the 20% isodose line for SDC, DDC and PD comparisons are all higher than 95%. In the DVH plot, the three dose distributions were found to be very close. A typical IMRT or VMAT case takes less than one minute to run the whole QA tool.

CONCLUSIONS

We have developed a GPU-based MC QA tool which can be used for efficient and easy IMRT and VMAT QA.

WE-E-213CD-04: CT to Cone-Beam CT Deformable Registration With Simultaneous Intensity Correction

PURPOSE

To develop and validate a robust CT to cone-beam (CBCT) deformable image registration algorithm that can handle CBCT artifacts and intensity inconsistency, and thus can yield accurate registration results.

METHODS

We propose a new algorithm called Deformation with Intensity Simultaneously Corrected (DISC). DISC distinguishes itself from the original demons by performing an intensity correction procedure on the CBCT image at every iteration step of demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the image intensities inside a patch around this voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities and hence facilitating the registration process. DISC is implemented on computer graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming environment. The performance of DISC has been qualitatively and quantitatively evaluated on a simulated patient case and six head-and- neck cancer patient data.

RESULTS

Visual inspection shows that original demons distorts the tissues after registration, especially in regions which are heavily degraded by artifacts. DISC, on the other hand, can effectively register CT and CBCT image even in regions contaminated by severe artifacts. The intensity corrected CBCT that extracted from the last iteration of DISC is artifact-free and has similar histogram distribution with the deformed CT.

CONCLUSIONS

We have developed a robust CT to CBCT deformable image registration method that properly deals with the CBCT artifacts and intensity inconsistency, and thus yields accurate registration results. This work is supported in part by the University of California Lab Fees Research Program, the Master Research Agreement from Varian Medical Systems, Inc., and the grants from the National Natural Science Foundation of China (No.30970866).