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Title: SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

Abstract

Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then, AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: Formore » 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ- , and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.« less

Authors:
; ;  [1];  [2];  [3]
  1. University of California San Francisco, San Francisco, CA (United States)
  2. UF Health Cancer Center at Orlando Health, Orlando, FL (United States)
  3. University of Texas Health Science Center at San Antonio, San Antonio, TX (United States)
Publication Date:
OSTI Identifier:
22494110
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ACCURACY; ALGORITHMS; COMPUTER CODES; COMPUTERIZED TOMOGRAPHY; DATASETS; DEFORMATION; ERRORS; IMAGES; PATIENTS; PHANTOMS; RADIATION DOSE DISTRIBUTIONS; RADIATION DOSES

Citation Formats

Kim, H, Chen, J, Pouliot, J, Pukala, J, and Kirby, N. SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy. United States: N. p., 2015. Web. doi:10.1118/1.4924179.
Kim, H, Chen, J, Pouliot, J, Pukala, J, & Kirby, N. SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy. United States. doi:10.1118/1.4924179.
Kim, H, Chen, J, Pouliot, J, Pukala, J, and Kirby, N. Mon . "SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy". United States. doi:10.1118/1.4924179.
@article{osti_22494110,
title = {SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy},
author = {Kim, H and Chen, J and Pouliot, J and Pukala, J and Kirby, N},
abstractNote = {Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then, AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: For 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ- , and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.},
doi = {10.1118/1.4924179},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: To evaluate the performance variations in commercial deformable image registration (DIR) tools for adaptive radiation therapy. Methods: Representative plans from three different anatomical sites, prostate, head-and-neck (HN) and cranial spinal irradiation (CSI) with L-spine boost, were included. Computerized deformed CT images were first generated using virtual DIR QA software (ImSimQA) for each case. The corresponding transformations served as the “reference”. Three commercial software packages MIMVista v5.5 and MIMMaestro v6.0, VelocityAI v2.6.2, and OnQ rts v2.1.15 were tested. The warped contours and doses were compared with the “reference” and among each other. Results: The performance in transferring contours was comparablemore » among all three tools with an average DICE coefficient of 0.81 for all the organs. However, the performance of dose warping accuracy appeared to rely on the evaluation end points. Volume based DVH comparisons were not sensitive enough to illustrate all the detailed variations while isodose assessment on a slice-by-slice basis could be tedious. Point-based evaluation was over-sensitive by having up to 30% hot/cold-spot differences. If adapting the 3mm/3% gamma analysis into the evaluation of dose warping, all three algorithms presented a reasonable level of equivalency. One algorithm had over 10% of the voxels not meeting this criterion for the HN case while another showed disagreement for the CSI case. Conclusion: Overall, our results demonstrated that evaluation based only on the performance of contour transformation could not guarantee the accuracy in dose warping. However, the performance of dose warping accuracy relied on the evaluation methodologies. Nevertheless, as more DIR tools are available for clinical use, the performance could vary at certain degrees. A standard quality assurance criterion with clinical meaning should be established for DIR QA, similar to the gamma index concept, in the near future.« less
  • Purpose: To evaluate geometric and dosimetric uncertainties of CT-CBCT deformable image registration (DIR) algorithms using digital phantoms generated from real patients. Methods: We selected ten H&N cancer patients with adaptive IMRT. For each patient, a planning CT (CT1), a replanning CT (CT2), and a pretreatment CBCT (CBCT1) were used as the basis for digital phantom creation. Manually adjusted meshes were created for selected ROIs (e.g. PTVs, brainstem, spinal cord, mandible, and parotids) on CT1 and CT2. The mesh vertices were input into a thin-plate spline algorithm to generate a reference displacement vector field (DVF). The reference DVF was applied tomore » CBCT1 to create a simulated mid-treatment CBCT (CBCT2). The CT-CBCT digital phantom consisted of CT1 and CBCT2, which were linked by the reference DVF. Three DIR algorithms (Demons, B-Spline, and intensity-based) were applied to these ten digital phantoms. The images, ROIs, and volumetric doses were mapped from CT1 to CBCT2 using the DVFs computed by these three DIRs and compared to those mapped using the reference DVF. Results: The average Dice coefficients for selected ROIs were from 0.83 to 0.94 for Demons, from 0.82 to 0.95 for B-Spline, and from 0.67 to 0.89 for intensity-based DIR. The average Hausdorff distances for selected ROIs were from 2.4 to 6.2 mm for Demons, from 1.8 to 5.9 mm for B-Spline, and from 2.8 to 11.2 mm for intensity-based DIR. The average absolute dose errors for selected ROIs were from 0.7 to 2.1 Gy for Demons, from 0.7 to 2.9 Gy for B- Spline, and from 1.3 to 4.5 Gy for intensity-based DIR. Conclusion: Using clinically realistic CT-CBCT digital phantoms, Demons and B-Spline were shown to have similar geometric and dosimetric uncertainties while intensity-based DIR had the worst uncertainties. CT-CBCT DIR has the potential to provide accurate CBCT-based dose verification for H&N adaptive radiotherapy. Z Shen: None; K Bzdusek: an employee of Philips Healthcare; S Koyfman: None; P Xia: received research grants from Philips Healthcare and Siemens Healthcare.« less
  • Purpose: Several commercial software packages have been recently released that allow the user to apply deformable registration algorithms (DRA) for image fusion and dose propagation. Although the idea of anatomically tracking the daily patient dose in the context of adaptive radiotherapy or merely adding the dose from prior treatment to the current one is very intuitive, the accuracy and applicability of such algorithms needs to be investigated as it remains somewhat subjective. In our study, we used true anatomical data where we introduced changes in the density, volume and location of segmented structures to test the DRA for its sensitivitymore » and accuracy. Methods: The CT scan of a prostate patient was selected for this study. The CT images were first segmented to define structure such as the PTV, bladder, rectum, intestines and pelvic bone anatomy. To perform our study, we introduced anatomical changes in the reference patient image set in three different ways: (i) we kept the segmented volumes constant and changed the density of rectum and bladder in increments of 5% (ii) we changed the volume of rectum and bladder in increments of 5% and (iii) we kept the segmented volumes constant but changed their location by moving their COM in increments of 3mm. Using the Velocity software, we evaluated the accuracy of the DRA for each incremental change in all three scenarios. Results: The DRA performs reasonably well when the differential density difference against the background is more than 5%. For the volume change study, the DRA results became unreliable for relative volume changes greater than 10%. Finally for the location study, the DRA performance was acceptable for shifts below 9mm. Conclusion: Site specific and patient specific QA for DRA is an important step to evaluate such algorithms prior to their use for dose propagation.« less
  • Purpose: To allow a reliable deformable image registration (DIR) method for dose calculation in radiation therapy. This work proposes a performance assessment of a morphological segmentation algorithm that generates a deformation field from lung surface displacements with 4DCT datasets. Methods: From the 4DCT scans of 15 selected patients, the deep exhale phase of the breathing cycle is identified as the reference scan. Varian TPS EclipseTM is used to draw lung contours, which are given as input to the morphological segmentation algorithm. Voxelized contours are smoothed by a Gaussian filter and then transformed into a surface mesh representation. Such mesh ismore » adapted by rigid and elastic deformations to match each subsequent lung volumes. The segmentation efficiency is assessed by comparing the segmented lung contour and the TPS contour considering two volume metrics, defined as Volumetric Overlap Error (VOE) [%] and Relative Volume Difference (RVD) [%] and three surface metrics, defined as Average Symmetric Surface Distance (ASSD) [mm], Root Mean Square Symmetric Surface Distance (RMSSD) [mm] and Maximum Symmetric Surface Distance (MSSD) [mm]. Then, the surface deformation between two breathing phases is determined by the displacement of corresponding vertices in each deformed surface. The lung surface deformation is linearly propagated in the lung volume to generate 3D deformation fields for each breathing phase. Results: The metrics were averaged over the 15 patients and calculated with the same segmentation parameters. The volume metrics obtained are a VOE of 5.2% and a RVD of 2.6%. The surface metrics computed are an ASSD of 0.5 mm, a RMSSD of 0.8 mm and a MSSD of 6.9 mm. Conclusion: This study shows that the morphological segmentation algorithm can provide an automatic method to capture an organ motion from 4DCT scans and translate it into a volume deformation grid needed by DIR method for dose distribution combination.« less
  • Purpose: To estimate the accumulated dose to targets and organs at risk (OAR) for head and neck (H'N) radiotherapy using 3 deformable image registration (DIR) algorithms. Methods: Five H'N patients, who had daily CBCTs taken during the course of treatment, were retrospectively studied. All plans had 5 mm CTV-to-PTV expansions. To overcome the small field of view (FOV) limitations and HU uncertainties of CBCTs, CT images were deformably registered using a parameter-optimized B-spline DIR algorithm (Elastix, elastix.isi.uu.nl) and resampled onto each CBCT with a 4 cm uniform FOV expansion. The dose of the day was calculated on these resampled CTmore » images. Calculated daily dose matrices were warped and accumulated to the planning CT using 3 DIR algorithms; SmartAdapt (Eclipse/Varian), Velocity (Velocity Medical Solutions), and Elastix. Dosimetric indices for targets and OARs were determined from the DVHs and compared with corresponding planned quantities. Results: The cumulative dose deviation was less than 2%, on average, for PTVs from the corresponding plan dose, for all algorithms/patients. However, the parotids show as much as a 37% deviation from the intended dose, possibly due to significant patient weight loss during the first 3 weeks of treatment (15.3 lbs in this case). The mean(±SD) cumulative dose deviations of the 5 patients estimated using the 3 algorithms (SmartAdapt, Velocity, and Elastix) were (0.8±0.9%, 0.5±0.9%, 0.6±1.3%) for PTVs, (1.6±1.9%, 1.4±2.0%, 1.7±1.9%) for GTVs, (10.4±12.1%, 10.7±10.6%, 6.5±10.1%) for parotid glands, and (4.5±4.6%, 3.4±5.7%, 3.9±5.7%) for mucosa, respectively. The differences among the three DIR algorithms in the estimated cumulative mean doses (1SD (in Gy)) were: 0.1 for PTVs, 0.1 for GTVs, 1.9 for parotid glands, and 0.4 for mucosa. Conclusion: Results of this study are suggestive that more frequent plan adaptation for organs, such as the parotid glands, might be beneficial during the course of H'N RT. This study was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA.« less