D Michalski

FOUR-DIMENSIONAL COMPUTED TOMOGRAPHY-BASED INTERFRACTIONAL REPRODUCIBILITY STUDY OF LUNG TUMOR INTRAFRACTIONAL MOTION

PURPOSE To evaluate the interfractional reproducibility of respiration-induced lung tumors motion, defined by their centroids and the intrafractional target motion range. METHODS AND MATERIALS Twentythree pairs of four-dimensional/computed tomography scans were acquired for 22 patients. Gross tumor volumes were contoured, Clinical target volumes (CTVs) were generated. Geometric data for CTVs and lung volumes were extracted. The motion tracks of CTV centroids, and CTV edges along the cranio-caudal, anterior-posterior, and lateral directions were evaluated. The Pearson correlation coefficient for motion tracks along the cranio-caudal direction was determined for the entire respiratory cycle and for five phases about the end of expiration. RESULTS The largest motion extent was along the cranio-caudal direction. The intrafractional motion extent for five CTVs was <0.5 cm, the largest motion range was 3.59 cm. Three CTVs with respiration-induced displacement >0.5 cm did not exhibit the similarity of motion, and for 16 CTVs with motion >0.5 cm the correlation coefficient was >0.8. The lung volumes in corresponding phases for cases that demonstrated CTVs motion similarity were reproducible. No correlation between tumor size and mobility was found. CONCLUSION Target motion reproducibility seems to be present in 87% of cases in our dataset. Three cases with dissimilar motion indicate that it is advisable to verify target motion during treatment. The adaptive adjustment to compensate the possible interfractional shifts in a target position should be incorporated as a routine policy for lung cancer radiotherapy.

SU-E-J-261: Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix

PURPOSE To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover. METHODS 20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared. RESULTS Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same is for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans. CONCLUSION Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied. Funding provided by Varian Medical Systems via Investigator Initiated Research Project.

The use of strain tensor to estimate thoracic tumors deformation

PURPOSE Respiration-induced kinematics of thoracic tumors suggests a simple analogy with elasticity, where a strain tensor is used to characterize the volume of interests. The application of the biomechanical framework allows for the objective determination of tumor characteristics. METHODS Four-dimensional computed tomography provides the snapshots of the patients anatomy at the end of inspiration and expiration. Image registration was used to obtain the displacement vector fields and deformation fields, which allows one for the determination of the strain tensor. Its departure from the identity matrix gauges the departure of the medium from rigidity. The tensorial characteristic of each GTV voxel was determined and averaged. To this end, the standard Euclidean matrix norm as well as the Log-Euclidean norm were employed. Tensorial anisotropy was gauged with the fractional anisotropy measure which is based on the normalized variance of the tensors eigenvalues. Anisotropy was also evaluated with the geodesic distance in the Log-Euclidean framework of a given strain tensor to its closest isotropic counterpart. RESULTS The averaged strain tensor was determined for each of the 15 retrospectively analyzed thoracic GTVs. The amplitude of GTV motion varied from 0.64 to 4.21 with the average of 1.20 cm. The GTV size ranged from 5.16 to 149.99 cc with the average of 43.19 cc. The tensorial analysis shows that deformation is inconsiderable and that the tensorial anisotropy is small. The Log-Euclidean distance of averaged strain tensors from the identity matrix ranged from 0.06 to 0.31 with the average of 0.19. The Frobenius distance from the identity matrix is similar and ranged from 0.06 to 0.35 with the average of 0.21. Their fractional anisotropy ranged from 0.02 to 0.12 with the average of 0.07. Their geodesic anisotropy ranged from 0.03 to 0.16 with the average of 0.09. These values also indicate insignificant deformation. CONCLUSIONS The tensorial framework allows for direct measurements of tissue deformation. It goes beyond the evaluation of deformation via comparison of shapes. It is an independent and objective determination of tissue properties. This methodology can be used to determine possible changes in lung properties due to radiation therapy and possible toxicities.

SU‐E‐T‐220: A Web‐Based Research System for Outcome Analysis of NSCLC Treated with SABR

PURPOSE To establish a web-based software system, an electronic patient record (ePR), to consolidate and evaluate clinical data, dose delivery and treatment outcomes for non small cell lung cancer (NSCLC) patients treated with hypofractionated stereotactic ablative radiation therapy (SABR) across institutions. METHODS The new trend of information technology in medical imaging and informatics is towards the development of an electronic patient record (ePR), in which all health and medical information of each patient are organized under the patients name and identification number. The system has been developed using the Wamp Server, a package of Apache web server, PHP and MySQL database to facilitate patient data input and management, and evaluation of patient clinical data and dose delivery across institution using web technology. The data of each patient to be recorded in the database include pre-treatment clinical data, treatment plan in DICOM-RT format and follow-up data. The pre-treatment data include demographics data, pathology condition, cancer staging. The follow-up data include the survival status, local tumor control condition and toxicity. The clinical data are entered to the system through the web page while the treatment plan data will be imported from the treatment planning system (TPS) using DICOM communication. RESULTS The collection of data of NSCLC patients treated with SABR stored in the ePR is always accessible and can be retrieved and processed in the future. The core of the ePR is the database which integrates all patient data in one location. CONCLUSIONS The web-based DICOM RT ePR system utilizes the current state-of-the-art medical informatics approach to investigate the combination and consolidation of patient data and outcome results. This will allow clinically-driven data mining for dose distributions and resulting treatment outcome in connection with biological modeling of the treatment parameters to quantify the efficacy of SABR in treating NSCLC patients.

SU‐E‐J‐161: Biomechanical Framework for Thoracic Tumors Characteristics

PURPOSE Respiration-induced kinematics of thoracic tumors suggests a simple analogy with elasticity, where a strain is used to characterize the volume of interests. The application of the biomechanical framework allows for the objective determination of tumor characteristics. METHODS The deformation of a given tissue element can be determined if its displacement is known. The latter can be obtained from 4DCT scans using image registration of the end of inhalation and exhalation CT volumes. The averaged right Cauchy-Green strain tensor was determined for each of the 15 retrospectively analyzed thoracic GTVs. The departure of the strain tensor from the identity matrix gauges the departure of the medium from rigidity. The averaging was carried out in Log-Euclidean framework. The fractional and geodesic anisotropy factors were determined for the tensor. RESULTS The amplitude of GTV motion varied from 0.64 to 4.21 with the average of 1.2cm. The GTV size ranged from 5.16 to 149.99cc with the average of 43.19cc. The tumor deformation is inconsiderable and the tensorial anisotropy is small. The Log-Euclidean distance of averaged strain tensors from the identity matrix ranged from 0.06 to 0.31 with the average of 0.19. The Frobenius distance from the identity matrix is similar and ranged from 0.06 to 0.35 with the average of 0.21. Their fractional anisotropy ranged from 0.02 to 0.12 with the average of 0.07. Their geodesic anisotropy ranged from 0.03 to 0.16 with the average of 0.09. These values also indicate insignificant deformation. CONCLUSION The biomechanical framework allows for the quantitative description of the tissue or anatomical regions of interest. Such regional characterization of volume of interests can be used in the objective evaluation of changes of the anatomy during the treatment or after the treatment. It might be used for correlation of outcome studies with objective characterization of changes within biomechanical framework. These objective characteristics do not rely on human interpretation. The measured changes might have predictive characteristics for the therapeutic success of the treatment.

SU-E-J-34: Tensor-Based Measure of Tumor Deformation

Purpose: To present a tensorial based method for evaluation of tumor deformation. Method and Materials:Image registration provides a displacement field for each voxel and as such provides also a deformation field. The Jacobian of the deformation field reveals the volume changes. However even with Jacobian =1, a voxel might deform. We use the Green‐St Venan strain tensor to quantify the tumor deformation. The tensor is obtained with the in‐house implemented elastic image registration. The Gaussian Pyramid of registered CT volumes allows for speedup of the registration as well as for addressing larger displacement. We use retrospectively ten cases of lungcancer patients for whom a pre‐tretamnt 4 DCT was obtained. The use of the tensor allows for factoring out rotation and translation. This in turns allows for the measurement of the pure deformation. Results: Only three GTVs were observed to deform between phase 50 and phase 0. The relative maximal shape change as quantified by the average ratio of the tensorial elipsoid radii was 30%. The average tumor motion for the cases was 1.6 cm. Conclusion: The method allows for improved measurement of intra fractional tumor deformation as contrasted with methods based on contour or volume comparison. It can be adopted to the measurement of the inter fractional changes in tumor shape and size if relevant CT scans are available.

SU‐E‐J‐27: 4DCT‐Derived Treatment Planning Scan with Improved Quality

Purpose: To improve the quality of the treatment planningCT volume for cancer occurring in upper abdomen. Method and Materials:Delineation of tumors in liver or pancreas critically depends on the image quality due to rather weak radio‐opacity difference between the tumor and surrounding tissue. Since for cases with tumor motion > 0.5cm, phase 50% scan is used for treatment planning the quality of this image is worse than its helical equivalent. Thus in order to improve Signal to Noise ratio (SNR) and Contrast to Noise ratio (CNR) of the phase 50 scan a synchronized averaging of the entire 4DCT data set is applied to create a composite CT volume equivalent to phase 50 scan. Four dimensional CT scans of ten patients with liver and pancreas cancer were used retrospectively in this study. In‐house implementation of Demons algorithm allowed for adding the deformed CT phases to phase 50% scan. Results: Improved SNR for all cases was observed. Average improvement of SNR for all cases in the region of interest was by a factor of 2.8. The scan also look better for a visual inspection. Conclusion: Synchronized averaging of the 4 DCT scan can be used to obtain better quality treatment planning scans. However possible artefacts in 4DCT phases might preclude effective use of the entire set of CT phases.

SU‐GG‐I‐126: An Adaptivie Regularization for the Demons Algorithm

Purpose: An examination of the regularization methods of the deformation vector field obtained with the “Demons” algorithm. The application of the adaptive smoothing for incremental variability of the similarity between the source and target image and its effect on the general characteristics of the transformation map. Method and Materials: The synthetic images are used for the numerical experiments. An adaptive iterative smoothing of the deformation field computed with the “Demons” algorithm is examined. The transformation map is examined with respect to the degree of the dissimilarity between the matched images and adaptive filtering . The standard Gaussian filtering with varying standard deviations (σ) is used for the adaptive smoothing. The magnitudes if the deformation vectors and the smoothness characteristics of the deformation maps are examined. Results: The deformation map reflecting the degree of the dissimilarity between the source and the target image gains truthfulness after application of the adaptive regularization. The real magnitude of the deformation between registered objects affects the effectiveness of the filtering. Since this value is not known an arbitrary a priori selection of the Gaussian filter is never optimal. The larger the filters σ the smaller the magnitude of the deformation vectors is obtained. An inverse trend is observed for the magnitude of the maps standard deviation. The convergence rate of the algorithm is affected by the selection of the given σ. The mean squared sum of intensity differences measures the images similarity. Conclusion: The application of the adaptive regularization of the deformation field reflects the varying scales of the real deformation and how the algorithm is parametrically allowed to accommodate these differences during the iteration. This idea might be extended to anisotropic adaptive filtering to accommodate inhomogeneity of the real deformation at a given resolution scale.

SU‐GG‐I‐133: The Analysis of the Transformation Map Construction for Optic Flow Based Image Registrations

Purpose: A formal analysis and computational examples of the deformation field calculation for optic‐flow‐based image registration algorithms is presented. The addition and composition of the deformation field updates are compared for different types of medium deformation. Method and Materials: A robust family of image registration algorithms is based on the optic flow method. They are iterative and use the conceptual temporal and spatial gradients of the registered images for the determination of the deformation between the objects. This transformation map is obtained iteratively from the current one by: i)the addition and ii)the composition with the update field. These methods are formally different and interplay with image gradients. This effect of the interplay is examined with respect to the type of the deformation. Synthetic and patient images are used in an experiment. Results: The inter‐relation of the deformation field and the gradient forces of the algorithms provides the main gauge of the effectiveness of the iterative process of the determination of the deformation field. The disparity in the alignment of the gradient and the field decreases the efficiency of the iterative process for the additive update. The translational differences between the moving and static image do not affect either methods. The variable deformation is properly addressed by the composition in the iterative process. Conclusion: This analysis can be generalized for any image registration algorithms, which uses the multiple pass approach for the image update in an iterative scheme. The obvious temporal load of the field composition can be mitigated by the use of linear interpolations. For multigrid‐based image registrations, the low resolution steps do can use B‐spline interpolations without sacrificing performance. The improved effectiveness of the deformation field buildup is an important issue for the creation of physically veritable diffeomorphic transformations.

SU-GG-T-539: Quantitative Analysis of Dosimetric Effects Due to Irregular Respiratory Motion in Stereotactic Body Radiotherapy of Lung Cancer

Purpose: A dosimetric analysis of respiratory motion irregularities is presented for stereotactic body radiotherapy(SBRT) of lungcancer. The changes in target dosimetric coverage is quantified in terms of random and systematic variations in the respiratory motion and correlated with average tumor motion. Method and Materials:Treatment plans and respiratory motion data of 12 lungcancer patients treated under SBRT protocols are retrospectively analyzed. In addition to actual respiratory motion data, a set of simulated motion data is also generated demonstrating irregularities in form of random fluctuations as well as systematic variations. Delineated clinical target volumes (CTV) of patients are subjected to the motion pattern while the target voxels are individually tracked in three dimensions. These trajectories are finally used to map the dose accumulated in the voxels of CTV in its own frame‐of‐reference enabling dosimetric evaluation. Results: The reduction in target minimum dose (Dmin) due to irregular respiratory motion was −1.1% (s=0.7%) for mean target motion of 1.0 cm and −2.6% (s=1.4%) for 2.0 cm. Changes in tumor control probability (TCP) associated with these reductions were negligible. When investigated separately, random fluctuations in the respiratory motion accounted for no change in Dmin (s=0.2%) for 1.0 cm target motion and only −0.8% (s=0.4%) for 2.0 cm. However, systematic variations in the respiratory motion such as a change in the average target motion between the simulation and treatment sessions was observed to play a greater role, with −1.6% and −3.7% changes in Dmin for 1.0 and 2.0 cm target motions respectively. Conclusions: Dosimetric impact of irregular respiratory motion was observed to be minimal in the use of SBRT for lungcancertreatments. When individual types of uncertainties are evaluated, the systematic shifts in breathing pattern were observed to have larger effect than commonly observed random breathing fluctuations.