Small Deformation Inverse Consistent Linear Elastic Research Articles

SU‐F‐J‐84: Comparison of Quantitative Deformable Image Registration Evaluation Tools: Application to Prostate IGART

Purpose:Errors in displacement vector fields (DVFs) generated by Deformable Image Registration (DIR) algorithms can give rise to significant uncertainties in contour propagation and dose accumulation in Image‐Guided Adaptive Radiotherapy (IGART). The purpose of this work is to assess the accuracy of two DIR algorithms using a variety of quality metrics for prostate IGART.Methods:Pelvic CT images were selected from an anonymized database of nineteen prostate patients who underwent 8–12 serial scans during radiotherapy. Prostate, bladder, and rectum were contoured on 34 image‐sets for three patients by the same physician. The planning CT was deformably‐registered to daily CT using three variants of the Small deformation Inverse Consistent Linear Elastic (SICLE) algorithm: Grayscale‐driven (G), Contour‐driven (C, which utilizes segmented structures to drive DIR), combined (G+C); and also grayscale ITK demons (Gd). The accuracy of G, C, G+C SICLE and Gd registrations were evaluated using a new metric Edge Gradient Distance to Agreement (EGDTA) and other commonly‐used metrics such as Pearson Correlation Coefficient (PCC), Dice Similarity Index (DSI) and Hausdorff Distance (HD).Results:C and G+C demonstrated much better performance at organ boundaries, revealing the lowest HD and highest DSI, in prostate, bladder and rectum. G+C demonstrated the lowest mean EGDTA (1.14 mm), which corresponds to highest registration quality, compared to G and C DVFs (1.16 and 2.34 mm). However, demons DIR showed the best overall performance, revealing lowest EGDTA (0.73 mm) and highest PCC (0.85).Conclusion:As expected, both C‐ and C+G SICLE more accurately reproduce manually‐contoured target datasets than G‐SICLE or Gd using HD and DSI metrics. In general, the Gd appears to have difficulty reproducing large daily position and shape changes in the rectum and bladder. However, Gd outperforms SICLE in terms of EGDTA and PCC metrics, possibly at the expense of topological quality of the estimated DVFs.

SU‐E‐J‐151: Dosimetric Evaluation of DIR Mapped Contours for Image Guided Adaptive Radiotherapy with 4D Cone‐Beam CT

Purpose:To estimate dosimetric errors resulting from using contours deformably mapped from planning CT to 4D cone beam CT (CBCT) images for image‐guided adaptive radiotherapy of locally advanced non‐small cell lung cancer (NSCLC).Methods:Ten locally advanced non‐small cell lung cancer (NSCLC) patients underwent one planning 4D fan‐beam CT (4DFBCT) and weekly 4DCBCT scans. Multiple physicians delineated the gross tumor volume (GTV) and normal structures in planning CT images and only GTV in CBCT images. Manual contours were mapped from planning CT to CBCTs using small deformation, inverse consistent linear elastic (SICLE) algorithm for two scans in each patient. Two physicians reviewed and rated the DIR‐mapped (auto) and manual GTV contours as clinically acceptable (CA), clinically acceptable after minor modification (CAMM) and unacceptable (CU). Mapped normal structures were visually inspected and corrected if necessary, and used to override tissue density for dose calculation. CTV (6mm expansion of GTV) and PTV (5mm expansion of CTV) were created. VMAT plans were generated using the DIR‐mapped contours to deliver 66 Gy in 33 fractions with 95% and 100% coverage (V66) to PTV and CTV, respectively. Plan evaluation for V66 was based on manual PTV and CTV contours.Results:Mean PTV V66 was 84% (range 75% – 95%) and mean CTV V66 was 97% (range 93% – 100%) for CAMM scored plans (12 plans); and was 90% (range 80% – 95%) and 99% (range 95% – 100%) for CA scored plans (7 plans). The difference in V66 between CAMM and CA was significant for PTV (p = 0.03) and approached significance for CTV (p = 0.07).Conclusion:The quality of DIR‐mapped contours directly impacted the plan quality for 4DCBCT‐based adaptation. Larger safety margins may be needed when planning with auto contours for IGART with 4DCBCT images.Reseach was supported by NIH P01CA116602.

SU-E-J-94: Assessment of Dose Accumulation with Different Deformable Image Registration Algorithms for Image-Guided Radiotherapy of Head and Neck Cancer

Purpose: Many automated Deformable Image Registration (DIR) validation methods are based on comparisons of deformed and observer contours. However, contour-based comparisons are not sufficient to assess the dosimetric accuracy of a DIR algorithm. There is also potential for large dosimetric discrepancy when DVH accumulation is performed with different DIR algorithms. In this work, we compare the accumulated doses obtained with two different DIR algorithms in Head and Neck (H&N) cancer. Methods: Eight H&N patients who had weekly FBCT scans were included. Small Deformation Inverse-Consistent Linear Elastic (SICLE) registration was used was used to estimate the displacement vector fields (DVFs) that simultaneously matched the CT intensity from the source (planning CT) image to weekly FBCT (target) images. In addition to intensity-based matching (SICLE_I), contour-based (SICLE_C) and intensity plus contour-based (SICLE_I+C) matching was also performed. GTV, parotids, larynx and esophagus were used to drive SICLE_C registrations. The SICLE registrations were also compared to ITK Diffeomorphic Demons (ITKDD) registration algorithm. Finally, the accumulated dose was calculated for both adaptive and non-adaptive scenarios and evaluated using dose-volume constraints. Results: For non-adaptive scenario, there was hardly any difference in the accumulated dose for target coverage using SICLE_I, SICLEC, SICLEI+C and ITKDD. Using adaptive replanning, however, PTV1 D98 improved by 1.5% using SICLEI+C. The maximum accumulated dose to the brainstem (D2) was also reduced by 12% by adaptive. On the average, the left parotid D50 also decreased by 32% using adaptive replanning. The maximum dose to the cord did not have a significant change using adaptive replanning-based dose accumulation using all DIR algorithms. Conclusion: Intensity driven DIR algorithms used in this work produced fairly similar accumulated dose results in H&N cases. Overall, the dose accumulation based on adaptive replanning and utilizing intensity + contour driven registration resulted in better target coverage and critical structure sparing.

TU-C-141-04: Evaluation of Clinical Acceptability of DIR Mapped Contours for Adaptive Radiotherapy with 4D Cone-Beam CT

Purpose: To evaluate clinical acceptability of contours generated using deformable image registration of 4D fan beam (4DFBCT) to 4D cone beam (4DCBCT) images for image-guided adaptive radiotherapy (IGART). Methods: Sixteen locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4DFBCT and weekly 4DCBCT scans. 4DFBCT to 4DCBCT registrations were performed using two intensity-driven deformable image registration (DIR) algorithms: 1) small deformation, inverse consistent linear elastic (SICLE) algorithm and 2) Insight Toolkit diffeomorphic demons (DEMONS). Multiple physicians delineated the gross tumor volume in all images (MANUAL). Manual contours were mapped from 4DFBCT to 4DCBCT for two scans in each patient, and were compared with manual contours using Dice similarity coefficient (DSC), average symmetric distance (ASD), false positive (FP), false negative (FN) and percentage volume difference (%VD). A physician was asked to rate DIR generated and manual contours as A) clinically acceptable (CA) B) clinically acceptable after minor modifications (CAMM) and C) clinically unacceptable (CU) using planning CT contour as reference. Reviewing physician was unaware that some of the contours were manual. The results were analyzed with respect to similarity measures, volume and regression characteristics of the tumor. Results: No contours were rated as CU by the physician. The number of contours rated as CA and CAMM was respectively 14 (44%) and 18 (56%) for SICLE, 3 (9%) and 29 (91%) for DEMONS, and 26 (81%) and 6 (19%) for MANUAL. No correlation was observed between physician ratings and DSC, ASD, FP, FN, %VD, tumor shrinkage, day of CBCT scan, and volume of the GTV. Conclusion: DIR generated contours were considered clinically acceptable for IGART, but may require some manual adjustment prior to use. Volume and surface similarity measures did not correlate with physician judgment of clinical acceptability. Supported by NIH Grant P01 CA 116602; This work was supported by a grant (P01CA116602) from the National Cancer Institute. Gary E. Christensen holds a papent related to the SICLE registration algorithm; E. Weiss and J. Williamson has grants from Varian medical systems and Philips Radiation Oncology Systems.

MO-F-BRA-02: Evaluation of 4D CT to 4D Cone-Beam CT Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy.

To evaluate two deformable image registration (DIR) algorithms for the purpose of contour mapping to support image guided adaptive radiotherapy (IGART) with 4D cone beam CT (4DCBCT). Eleven locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4D fan- beam CT (4DFBCT) and seven weekly 4DCBCT scans. Gross tumor volume (GTV) and carina were delineated by a physician in all 4D images. For day to day registration, the end of inspiration 4DFBCT phase was deformably registered to the corresponding phase in each 4DCBCT image. For phase to phase registration, the end of inspiration phase from each 4D image was registered to end of expiration phase. The delineated contours were warped using the resulting transforms and compared to the manual contours through Dice similarity coefficient (DSC), false positive and false negative indices, and, for carina, target registration error (TRE). Two DIR algorithms were tested: 1) small deformation, inverse consistent linear elastic (SICLE) algorithm and 2) Insight Toolkit diffeomorphic demons (DEMONS). For day to day registrations, the mean DSC was 0.59 ± 0.16 after rigid registration, 0.72 ± 0.13 with SICLE and to 0.66 ± 0.18 with DEMONS. SICLE and DEMONS reduced TRE to 4.1 ± 2.1 mm and 5.8 ± 3.7 mm respectively, from 6.2 ± 3.5 mm; and reduced false positive index to 0.27 and 0.26 respectively from 0.46. Registration with the cone beam as the fixed image resulted in higher DSC than with the fan beam as fixed (p < 0.001). SICLE and DEMONS increased the DSC on average by 10.0% and 8.0% and reduced TRE by 2.8 mm and 2.9 mm respectively for phase to phase DIR. DIR achieved more congruent mapping of target structures to delineations than rigid registration alone, although DIR performance varied with algorithm and patient. This work was supported by National Cancer Institute Grant No. P01 CA 116602.

WE-G-BRCD-09: Assessment of Quantitative Differences Between Different Deformable Image Registration Algorithms for Image Guided Radiotherapy of Head and Neck Cancer Patients.

DIR algorithms differ by physical principles employed in their design which in turn determines complexity of the transformation that the algorithm allows. Indiscriminate application of different algorithms without regard for their limitations may lead to significant discrepancies in research results and in clinical procedures. We compare two different algorithms in Head and Neck (H&N) patients to assess what aspects of DIR use are most sensitive to differences between algorithms. H&N patients are enrolled in a protocol which acquires weekly FBCT and daily double CBCT. Each FBCT study is contoured by the same physician. Two DIR algorithms are compared: Small Deformation Inverse-Consistent Linear Elastic (SICLE), and the ITK Diffeomorphic Demons (ITKDD) as implemented in the ITK package. Both algorithms employ significantly different physical principles in their design and consequently impose different restrictions on the complexity of transformation they allow. We compare Jacobian Volume Histograms (JVH), Spatial Discrepancy Volume Histograms (SDVH), BED and physical dose accumulation results and resulting plan evaluation indices. Analysis of mean Jacobian shows that both algorithms are able to detect changes in structure volumes, though they differ quantitatively from one another and from the ground truth as established by the analysis of changes in contours. Width of Jacobian distributions is very different indicating that Jacobian should not be used as a measure of volume change at a voxel level without independent validation. Analysis of SDVHs shows that dose lookup points implied by both algorithms are separated by 5mm - 10mm over approximately 30% of most volumes. These differences translate into clearly visible though not very significant differences in BED and dose accumulation. We further observe that physical dose accumulation in external beam H&N patients is a good proxy for direct BED accumulation. Different DIR algorithms may have to be applied selectively in different areas of treatment planning. Acknowledgments: Supported by NIH Grant P01 CA11602.

SU-E-J-46: Evaluation of Inter-Fraction Deformable Registration of 4DCT Scans: Direct vs. Composed Registration

Purpose: To evaluate the direct versus composed registration in inter‐fraction deformable image registration of 4D CT scans of thoracic cancer patients. Methods: Lungcancer patients treated under an institutional IRB protocol were selected for this study. Each patient had a planning and subsequent weekly 4D CTimages that were acquired at ten discrete respiratory phases during their treatment course. GTV, lungs,heart, cord, and esophagus were manually delineated for all ten phases on each image set. A Small Deformation Inverse Consistent Linear Elastic (SICLE) non‐rigid‐registration algorithm was utilized for the generation of displacement vector fields (DVFs). The end of inhalation phase images from the weekly on‐treatment 4DCTs were deformably‐registered to the end of inhalation phase image from the planning 4DCT using two different registration permutations. In the ‘Composed’ method, each end of inhalation phase image was registered to the subsequent weekly image, and the resulting DVFs were composed or ‘chained’ to create the mapping from any weekly image to the planning image. For evaluation, contours on different image sets were mapped back to the planning 4D CT. Deformation maps generated by “Direct” versus “Composed” registration methods were evaluated using “volume histograms of the vector difference length for each structure”, “Jacobian”, “Dice similarity coefficient (DSC)” and Center of Mass (COM). Results: The average spatial discrepancy for all structures was less then 1mm using “direct” vs. “composed” registration. The differences in the average Jacobian for all structures obtained with “direct” vs. “composed” DVFs were also negligible. Differences between deformably‐mapped GTV, lung and esophagus contours obtained with “direct” and “composed” registration DVFs were <0.5mm. Conclusions: The evaluation of the “direct” versus “composed” registration of weekly 4D CT scans showed that there are very small differences in “Direct” vs. “Composed” registration DVFs although there are numerically noticeable differences. Supported by NIHgrant‐P01CA11602

SU‐GG‐J‐47: Verification of a Deformable Image Registration Algorithm for Head and Neck Cancer Therapy

Introduction: H&amp;N cancer patients may require frequent CT imaging for re‐planning due to significant anatomical changes to improve dosimetric accuracy. A challenging task is manual delineation of target(s) and normal structures on each image sets acquired on different days. The aim of this study was to verify a Small Deformation Inverse Consistent Linear Elastic (SICLE) deformable image registration for its ability to deformably‐map contours on H&amp;N image sets by comparing them to physician‐drawn contours. Methods and Materials: Six patients with H&amp;N cancer who had frequent CT imaging were selected. GTVs, parotids, cord and brainstem were physican‐drawn on both source and target image sets. Physician‐drawn contours on source image set were deformed by the DVFs to produce deformably‐mapped contours and dose on target images. Differences between the physician‐drawn and deformably‐mapped contours were compared using: “Differences in volumes(Vdiff)”, “differences in center of mass(COMdiff)”, “volume overlap index(VOI)”, “3D‐perpendicular‐distance‐maps”. To evaluate the clinical usefulness, plans were generated based on the physician‐drawn and deformably‐mapped contours. Furthermore, the dose from source image was deformed to target images using the DVFs. Plans were compared for their coverage of physician‐drawn contours. Results: While GTV and NodalGTV VOIs ranged from 0.57 to 0.72 and 0.57 to 0.79 respectively, for parotids, brainstem and cord, it varied from 0.42 to 0.82. Up to 1.7cm and 2.9cm discrepancies were observed in esophagus and cord COM in z direction. Visual comparison of physician‐drawn and deformably‐mapped GTVcontours showed a reasonably well agreement. Dosimetric impact of contour differences was not very significant with &lt;5% differences in PTV D95. Conclusion: SICLE algorithm can produce high quality contours on subsequent H&amp;N image sets. However, manual modification of deformably‐mapped contours may be necessary. Deformably‐mapped contours can be used as a QA tool for inspecting manually‐drawn contours, in addition to reducing or even eliminating the manual contouring workload.

TU‐B‐204B‐05: Quantitative Evaluation and Optimization of an Algorithm for Non‐Rigidly Registering Serial CT Images to the Planning CT during Prostate‐Cancer Radiation Therapy

Purpose: To identify the optimal parameters for a non‐rigid registration algorithm and to assess its accuracy for deformably registering serial prostate cancer CT images acquired during radiation therapy. Such registrations are needed to map organ segmentations from the planning CT onto daily replanning CTs and for cumulative dose reconstruction. Method and Material: Data sets were provided by the Netherlands Cancer Institute and consisted of multislice CT images of 17 prostate cancer patients each scanned 10–12 times during radiation therapy. One physician contoured bladder, rectum, and prostate on all images. The small‐deformation, inverse‐consistent linear‐elastic (SICLE) registration algorithm (TMI 20: 568, 2001) was used to estimate the deformation vector field (DVF) that simultaneously matched CT intensity (intensity matching, IM) and the three segmented regions (contour matching, or CM). IM was used to find DVFs mapping the first image (source) to each of the other subsequent images (target images) for all patients. IM + CM and CM was assessed for a pair of images for each of 10 patients. Endpoints included comparing deformed source contours to manually drawn target contours via the Dice similarity Index (DSI) and Pearson Correlation Coefficient (CC). CC and difference images (deformed source‐target) were used to quantify IM accuracy. Results: IM+CM vs. IM increased mean DSI from 0.78 to 0.95, 0.84 to 0.92, and 0.77 to 0.90 for rectum, prostate, and bladder, respectively, with CC improvements of 14%, 9%, 12%. Compared to CM, IM+CM produced better soft‐tissue registration outside contoured regions, with minimal IM loss relative to IM alone. Conclusion: IM registrations failed to produce acceptable registrations in about 1/3 of the cases, with accurate rectal deformation proving to be especially challenging. Simultaneously contour and intensity matching gave the best overall registration accuracy. Supported by Grant P01 CA 116602.

SU-FF-J-170: Impact of 4D Cone Beam CT View-Aliasing Artifact On Nonrigid Registration Accuracy

Purpose: To evaluate the effect of 4D cone beam CT view‐aliasing artifact on nonrigid registration accuracy. Method and Materials: End exhalation (EE) and end inhalation (EI) volumes from a 4D multi‐slice CTimage (4DCT) of a research subject were registered using a small deformation inverse consistent linear elastic (SICLE) registration algorithm to produce a reference displacement vector field (DVF) between the two images. Artificial cone beam CT projections were generated for the EE and EI volumes at several different angular sampling patterns, mimicking 4D cone beam CT (4DCBCT) acquisition in a free‐breathing patient. The projections were reconstructed into EE and EI 4DCBCT phase images containing view‐aliasing artifact due to angular undersampling, but without CBCT scatter and beam hardening artifacts. The EE and EI 4DCBCT images were registered using the SICLE algorithm to produce a test DVF. Test DVFs were generated at a range of angular frequencies from 0.17 projections per degree to 1.83 projections per degree (full angular sampling on a commercial cone beam CT system). The norm of the voxel by voxel vector difference between the test DVF and reference DVF within the patient's delineated lung volume was calculated. Results: The mean absolute error was significantly associated with the projection angular frequency (p < 0.05, exponential regression, R2=0.996). The median error was below 0.2 cm and 90% of the error was below 0.3 cm for the lowest frequency evaluated here of 60 projections per phase. However, above a sampling frequency of 0.33 projections per degree (120 projections per phase), 90% of the error was less than 0.1 cm in relation to the 4DCT registration. Conclusion: Using at least 120 projections per phase, view‐aliasing artifact had a minimal effect on the registration accuracy for the SICLE algorithm due to inherent elastic and smoothing constraints in the algorithm.

MO‐FF‐A3‐03: Quantitative Assessment of Automatic Anatomy Segmentation of 4D CT Images Using a Deformable Image Registration Algorithm

Purpose: Quantify the accuracy of Small Deformation Inverse Consistent Linear Elastic (SICLE) deformable image registration to auto‐segment contours on 4D thoracic CT images. Method and Materials: A process of automatic contouring based on SICLE deformable image registration of respiratory CT phases from 4DCT scans was integrated into our image‐guided‐radiotherapy‐computing‐framework. The gross tumor volumes (GTVs), right and left lungs, cord and esophagus were manually‐drawn by the same physician for all ten respiratory phases. Registrations were performed using three intensity windows: 0–900 (lung only), 900–1300 (remaining tissue excluding lung) and 0–1300 (whole image). For each respiratory phase, the differences between the manually‐drawn and deformably‐mapped contours for each phase were quantified using fractional volume differences (FVDiff), differences in center of mass coordinates (COMdiff) and volume overlap index (VOI) and 3D maps of perpendicular distance between manually‐drawn and deformably‐mapped boundaries. The results were also compared against the previously‐implemented viscous fluid‐based registration. Results: The VOI for GTV and heart varied from 0.85 to 0.90, from 0.9 to 0.97, respectively. As expected, the smallest FVDiff were found be for the lungs (&lt;0.027). The SICLE (0–900) and viscous fluid methods yielded similar GTV COM in superior‐inferior direction. COMdiff were largest for the esophagus (∼0.36 cm) since the esophagus is difficult to visualize on CT, especially in the lower thorax. Overall, visual comparison of GTV manual and auto contours showed good agreement. However, large discrepancies (up to 2cm) were found at the most inferior portion of the lung. This disagreement was also very evident from 3D maps of perpendicular distances between manually‐drawn and deformably‐mapped contours. Conclusion: Automatically‐generated contours using SICLE algorithm can produce high quality contours on subsequent respiratory CT phases. Deformable image registration may reduce or even eliminate the manual contouring workload and can be used for manually‐drawn contour quality assurance.Supported by NIHgrant‐P01CA11602

SU‐FF‐J‐171: Quantification of Ventilation Imaging From Clinical 4DCT Datasets for Selective Avoidance IMRT in Non‐Small Cell Lung Cancer

Purpose: To determine a clinically appropriate ventilation level for selective avoidance IMRT using clinical 4D CT data sets and a small‐deformation inverse‐consistent linear‐elastic (SICLE) registration algorithm Materials/Methods: Using SICLE, the maximum inhalation and expiration phases of 4D CT datasets from 6 NSCLC patients were registered. After registration, smoothing was applied to the input images to account for registration error. A correction was applied to the voxels in the inhalation image to account for lung tissue mass discrepancy due to lung inflation. The ventilation was calculated voxel by voxel as a local volume change, DV/Vex = 1000 (HUin − HUex)/(HUex(1000 + HUin)), where HUex and HUin are the Hounsfield units associated with the expiration and inhalation images, respectively. After this, an additional smoothing filter and a cumulative distribution function were applied to aid segmentation and normalize the data between 0% and 100%. Ventilation volumes were quantified as a function of normalized ventilation level, which can be used as a threshold for delineation of ventilation subregions. Results: Using the inhalation image registered to the expiration image, consistent ventilation images were calculated. The ventilation levels of 90%, 80%, 70%, 60%, and 50% were studied as it was determined that ventilation levels below 50% would not be beneficial as this region would include almost the entirety of the lungs. An exponential relationship between ventilation level and ventilationcompetent volume at a given percentage ventilation was observed. Quantified ventilation data and charts demonstrating these relationships were generated. Conclusions: In selective avoidance IMRT for NSCLC, it is critical to determine the optimal ventilation level to segment within a normal lung region, which would then be used as an avoidance structure in functional image guided IMRT. Our study shows that a 70% ventilation level is promising to be implemented into selective avoidance IMRT techniques for NSCLC.

SU-FF-J-138: A Statistical Analysis of Lung Tumor Regression and Interfraction Geometric Variation

Purpose: To evaluate interfraction geometric variation and tumor regression in thoracic cancer using an active breath hold technique. Method and Materials: Weekly helical CT images were acquired during planning and treatment for six research subjects using an active breath hold technique at end of normal inspiration. A small deformation inverse consistent linear elastic (SICLE) algorithm was used to nonrigidly register each weekly image to the image acquired during planning. The resulting displacement vector fields (DVF) were used to track internal volume points and delineated surface points for the gross tumor volume (GTV) and ipsi- and contralateral lungs throughout the treatment course. The anatomical and pathological variation was analyzed through reduction of the vector field distribution using principal component analysis (PCA). Statistical models were generated independently for each patient using PCA. Principal eigenmodes were analyzed for trends related to volume regression, shifts, or combinations of both. Results: For all patients, three to five eigenmodes (mean: 4.2) accounted for at least 98% of the variation for the GTV only model. The mean residual error for a model constructed with these principal eigenmodes was 0.35 +/− 0.14 mm (range: 0.00 to 1.1 mm) over all subjects. The dominant eigenmode represented primarily shape change, attributed to volume regression of the GTV. The secondary eigenmodes represented rigid shifts in tumor position (within the lung) week to week and higher order combined effects. Conclusion: Isotropic tumor volume regression and tumor position shift in the lung were separated into components using PCA. Analysis of the dominant component will enable selection of the appropriate image-guided intervention to account for the variation represented by the model.

SU‐GG‐J‐193: Using Small‐Deformation Linear‐Elastic Registration to Quantifying Ventilation‐Competent Lung Imaging From Clinical 4DCT Datasets: Toward Selective Avoidance IMRT for Locally Advanced Non‐Small‐Cell Lung Cancer

Purpose: To verify the feasibility of a small‐deformation inverse‐consistent linear‐elastic (SICLE) registration algorithm in quantifying ventilation‐competent sublung images from 4D CT datasets. Materials/Method: SICLE, a deformable image registration algorithm developed at the University of Iowa, was utilized to register the 100% inhalation and 100% exhalation phases of the lung which were extracted from clinical 4D CT datasets. The SICLE method performs inverse consistent image registration in which the forward and reverse transformations are estimated jointly while minimizing the inverse consistency error. After deformable registration, ventilation was calculated using the equation on voxels of the inhalation and exhalation images, where HU is in Hounsfield Units and ΔV/Vex is the change in regional volume divided by the local volume. Results: SICLE was found to be efficient and consistent in deformable image registering lung CT image datasets with different phases. 3D ventilation images were quantified and given in coronal, transverse, and sagittal view. Quantitative ventilation in a given region of interest was also determined. Ventilation‐competent sublung regions were constructed with different functionality levels of 90, 70, 50, and 30%. Conclusion: Utilizing a small‐deformation inverse‐consistent linear‐elastic registration algorithm, it was feasible to quantify ventilation competent subregions from clinical 4D CT datasets. Given the availability of 4D CT technology, this study opens a pathway to integrate functional lung information into radiotherapy for locally advanced non‐small‐cell lung cancer.