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All sources NASA NTRS DOE OSTI

DOE OSTI2015

SU-E-J-122: The CBCT Dose Calculation Using a Patient Specific CBCT Number to Mass Density Conversion Curve Based On a Novel Image Registration and Organ Mapping Method in Head-And-Neck Radiation Therapy

Purpose: To develop a CBCT HU correction method using a patient specific HU to mass density conversion curve based on a novel image registration and organ mapping method for head-and-neck radiation therapy. Methods: There are three steps to generate a patient specific CBCT HU to mass density conversion curve. First, we developed a novel robust image registration method based on sparseness analysis to register the planning CT (PCT) and the CBCT. Second, a novel organ mapping method was developed to transfer the organs at risk (OAR) contours from the PCT to the CBCT and corresponding mean HU values of each OAR were measured in both the PCT and CBCT volumes. Third, a set of PCT and CBCT HU to mass density conversion curves were created based on the mean HU values of OARs and the corresponding mass density of the OAR in the PCT. Then, we compared our proposed conversion curve with the traditional Catphan phantom based CBCT HU to mass density calibration curve. Both curves were input into the treatment planning system (TPS) for dose calculation. Last, the PTV and OAR doses, DVH and dose distributions of CBCT plans are compared to the original treatment plan. Results: One head-and-neck cases which contained a pair of PCT and CBCT was used. The dose differences between the PCT and CBCT plans using the proposed method are −1.33% for the mean PTV, 0.06% for PTV D95%, and −0.56% for the left neck. The dose differences between plans of PCT and CBCT corrected using the CATPhan based method are −4.39% for mean PTV, 4.07% for PTV D95%, and −2.01% for the left neck. Conclusion: The proposed CBCT HU correction method achieves better agreement with the original treatment plan compared to the traditional CATPhan based calibration method.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A Bayesian Measurment Error Model for Misaligned Radiographic Data

An understanding of the inherent variability in micro-computed tomography (micro-CT) data is essential to tasks such as statistical process control and the validation of radiographic simulation tools. The data present unique challenges to variability analysis due to the relatively low resolution of radiographs, and also due to minor variations from run to run which can result in misalignment or magnification changes between repeated measurements of a sample. Positioning changes artificially inflate the variability of the data in ways that mask true physical phenomena. We present a novel Bayesian nonparametric regression model that incorporates both additive and multiplicative measurement error in addition to heteroscedasticity to address this problem. We also use this model to assess the effects of sample thickness and sample position on measurement variability for an aluminum specimen. Supplementary materials for this article are available online.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

CIEL ∗ Ch color map for visualization and analysis of sea ice motion

The International Commission on Illumination (CIE) designed its color space to be perceptually uniform so that a given numerical change in the color code corresponds to perceived change in color. This color encoding is demon- strated to be advantageous in scientific visualization and analysis of vector fields. The specific application is analysis of ice motion in the Arctic where patterns in smooth monthly-averaged ice motion are seen. Furthermore, fractures occurring in the ice cover result in discontinuities in the ice motion. This vector jump in displacement can also be visualized. We then analyze modeled and observed fractures through the use of a metric on the color space, and image amplitude and phase metrics. Amplitude and phase met- rics arise from image registration that is accomplished by sampling images using space filling curves, thus reducing the image registration problem to the more reliable functional alignment problem. We demonstrate this through an exploration of the metrics to compare model runs to an observed ice crack.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Code for multi-shape Gaussian process (GP) fitting with uncertainty quantification (UQ)

This is code associated with the publication “Nonparametric Multi-shape Modeling with Uncertainty Quantification,” authored by Hengrui Luo (Lawrence Berkeley National Laboratory) and Justin Strait (Los Alamos National Laboratory). The code is used to fit multiple-output Gaussian process (GP) models of planar closed curves to collections of ordered point sets, allowing for flexible nonlinear prediction of the underlying curve under dense or sparse point set samplings and with or without noise, as well as tractable uncertainty quantification. To do this, we employ use of a periodic kernel to account for the nonlinear input space of closed curves, and combine with coregionalization models to account for dependence both (i) between curve coordinates, and (ii) between pairs of curves. Functions in the code are capable of fitting these models, as well as performing additional tasks with the fitted curves such as (a) shape registration and alignment, (b) shape averaging, and (c) fitting for curve sub-populations / clusters.

Strait, Justin↗

DOE OSTI2016

SU-G-TeP2-13: Patient-Specific Reduction of Range Uncertainties in Proton Therapy by Proton Radiography with a Multi-Layer Ionization Chamber

Purpose: The conversion from Hounsfield units (HU) to stopping powers is a major source of range uncertainty in proton therapy (PT). Our contribution shows how proton radiographs (PR) acquired with a multi-layer ionization chamber in a PT center can be used for accurate patient positioning and subsequently for patient-specific optimization of the conversion from HU to stopping powers. Methods: A multi-layer ionization chamber was used to measure the integral depth-dose (IDD) of 220 MeV pencil beam spots passing through several anthropomorphic phantoms. The whole area of interest was imaged by repositioning the couch and by acquiring a 45×45 mm{sup 2} frame for each position. A rigid registration algorithm was implemented to correct the positioning error between the proton radiographs and the planning CT. After registration, the stopping power map obtained from the planning CT with the calibration curve of the treatment planning system was used together with the water equivalent thickness gained from two proton radiographs to generate a phantom-specific stopping power map. Results: Our results show that it is possible to make a registration with submillimeter accuracy from proton radiography obtained by sending beamlets separated by more than 1 mm. This was made possible by the complex shape of the IDD due to the presence of lateral heterogeneities along the path of the beam. Submillimeter positioning was still possible with a 5 mm spot spacing. Phantom specific stopping power maps obtained by minimizing the range error were cross-verified by the acquisition of an additional proton radiography where the phantom was positioned in a random but known manner. Conclusion: Our results indicate that a CT-PR registration algorithm together with range-error based optimization can be used to produce a patient-specific stopping power map. Sylvain Deffet reports financial funding of its PhD thesis by Ion Beam Applications (IBA) during the confines of the study and outside the submitted work. Francois Vander Stappen reports being employed by Ion Beam Applications (IBA) during the confines of the study and outside the submitted work.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1980

Jig for assembling large composite panels

Layup of composite panels as large as 15 by 60 ft is greatly facilitated by simple mechanism. Jig consists of flat, detachable table, and curved laminating-plate joined by rack and pinion to insure accurate registration. Vacuum holds thin plastic film to laminating-plate. Preimpregnated composite sheet is applied to plate, which is then lowered face down onto table. Release of vacuum leaves layer and film and table. Film is peeled off, and steps are repeated for next layer of laminate.

Watts, J. T.↗

DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗

DOE OSTI2015

SU-E-T-291: Dosimetric Accuracy of Multitarget Single Isocenter Radiosurgery

Purpose: To evaluate the accuracy of single-isocenter multiple-target VMAT radiosurgery (SIMT-VMAT-SRS) by analysis of pre-treatment verification measurements. Methods: Our QA procedure used a phantom having a coronal plane for EDR2 film and a 0.125 cm3 ionization chamber. Film measurements were obtained for the largest and smallest targets for each plan. An ionization chamber measurement (ICM) was obtained for sufficiently large targets. Films were converted to dose using a patient-specific calibration curve and compared to treatment planning system calculations. Alignment error was estimated using image registration. The gamma index was calculated for 3%/3 and 3%/1 mm criteria. The median dose in the target region and, for plans having an ICM, the average dose in the central 5 mm was calculated. Results: The average equivalent target diameter of the 48 targets was 15 mm (3–43 mm). Twenty of the 24 plans had an ICM for the plan corresponding to the largest target (diameter 11–43 mm) with a mean ratio of chamber reading to expected dose (ED) and the mean ratio of film to ED (averaged over the central 5 mm) was 1.001 (0.025 SD) and 1.000 (0.029 SD), respectively. For all plans, the mean film to ED (from the median dose in the target region) was 0.997 (0.027 SD). The mean registration vector was (0.15,0.29) mm, with an average magnitude of 0.96 mm. Before (after) registration, the average fraction of pixels having gamma < 1 was 99.3% (99.6%) and 89.1% (97.6%) for 3%/3mm and 3%/1mm, respectively. Conclusion: Our results demonstrate dosimetric accuracy of SIMT-VMAT-SRS for targets as small as 3 mm. Film dosimetry provides accurate assessment of the absolute dose delivered to targets too small for an ionization chamber measurement; however, the relatively large registration vector indicates that image-guidance should replace laser-based setup for patient-specific evaluation of geometric accuracy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

47 OTHER INSTRUMENTATION↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

image registration↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

stair-step artifact↗

NASA NTRS2014

The Rise and Fall of the Type Ib Supernova iPTF13bvn Not a Massive Wolf-Rayet Star

Context. We investigate iPTF13bvn, a core-collapse (CC) supernova (SN) in the nearby spiral galaxy NGC 5806. This object was discovered by the intermediate Palomar Transient Factory (iPTF) very close to the estimated explosion date and was classified as a stripped-envelope CC SN, likely of Type Ib. Furthermore, a possible progenitor detection in pre-explosion Hubble Space Telescope (HST) images was reported, making this the only SN Ib with such an identification. Based on the luminosity and color of the progenitor candidate, as well as on early-time spectra and photometry of the SN, it was argued that the progenitor candidate is consistent with a single, massive Wolf-Rayet (WR) star. Aims. We aim to confirm the progenitor detection, to robustly classify the SN using additional spectroscopy, and to investigate if our follow-up photometric and spectroscopic data on iPTF13bvn are consistent with a single-star WR progenitor scenario. Methods. We present a large set of observational data, consisting of multi-band light curves (UBVRI, g′r′i′z′) and optical spectra. We perform standard spectral line analysis to track the evolution of the SN ejecta. We also construct a bolometric light curve and perform hydrodynamical calculations to model this light curve to constrain the synthesized radioactive nickel mass and the total ejecta mass of the SN. Late-time photometry is analyzed to constrain the amount of oxygen. Furthermore, image registration of pre- and post-explosion HST images is performed. Results. Our HST astrometry confirms the location of the progenitor candidate of iPTF13bvn, and follow-up spectra securely classify this as a SN Ib. We use our hydrodynamical model to fit the observed bolometric light curve, estimating the total ejecta mass to be 1.9 solar mass and the radioactive nickel mass to be 0.05 solar mass. The model fit requires the nickel synthesized in the explosion to be highly mixed out in the ejecta. We also find that the late-time nebular r′-band luminosity is not consistent with predictions based on the expected oxygen nucleosynthesis in very massive stars. Conclusions. We find that our bolometric light curve of iPTF13bvn is not consistent with the previously proposed single massive WR-star progenitor scenario. The total ejecta mass and, in particular, the late-time oxygen emission are both significantly lower than what would be expected from a single WR progenitor with a main-sequence mass of at least 30 solar mass.

massive↗

DOE OSTI2015

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Sci-Thur PM - Colourful Interactions: Highlights 06: Patient-specific calibration of cone-beam computed tomographic (CBCT) image sets for on-line dose tracking and plan assessment

Purpose: To develop a radiotherapy dose tracking and plan evaluation technique using cone-beam computed tomography (CBCT) images. Methods: We developed a patient-specific method of calibrating CBCT image sets for dose calculation. The planning CT was first registered with the CBCT using deformable image registration (DIR). A scatter plot was generated between the CT numbers of the planning CT and CBCT for each slice. The CBCT calibration curve was obtained by least-square fitting of the data, and applied to each CBCT slice. The calibrated CBCT was then merged with original planning CT to extend the small field of view of CBCT. Finally, the treatment plan was copied to the merged CT for dose tracking and plan evaluation. The proposed patient-specific calibration method was also compared to two methods proposed in literature. To evaluate the accuracy of each technique, 15 head-and-neck patients requiring plan adaptation were arbitrarily selected from our institution. The original plan was calculated on each method’s data set, including a second planning CT acquired within 48 hours of the CBCT (serving as gold standard). Clinically relevant dose metrics and 3D gamma analysis of dose distributions were compared between the different techniques. Results: Compared to the gold standard of using planning CTs, the patient-specific CBCT calibration method was shown to provide promising results with gamma pass rates above 95% and average dose metric agreement within 2.5%. Conclusions: The patient-specific CBCT calibration method could potentially be used for on-line dose tracking and plan evaluation, without requiring a re-planning CT session.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Validation of a deformable image registration technique for cone beam CT-based dose verification

Purpose: As radiation therapy evolves toward more adaptive techniques, image guidance plays an increasingly important role, not only in patient setup but also in monitoring the delivered dose and adapting the treatment to patient changes. This study aimed to validate a method for evaluation of delivered intensity modulated radiotherapy (IMRT) dose based on multimodal deformable image registration (DIR) for prostate treatments. Methods: A pelvic phantom was scanned with CT and cone-beam computed tomography (CBCT). Both images were digitally deformed using two realistic patient-based deformation fields. The original CT was then registered to the deformed CBCT resulting in a secondary deformed CT. The registration quality was assessed as the ability of the DIR method to recover the artificially induced deformations. The primary and secondary deformed CT images as well as vector fields were compared to evaluate the efficacy of the registration method and it’s suitability to be used for dose calculation. PLASTIMATCH, a free and open source software was used for deformable image registration. A B-spline algorithm with optimized parameters was used to achieve the best registration quality. Geometric image evaluation was performed through voxel-based Hounsfield unit (HU) and vector field comparison. For dosimetric evaluation, IMRT treatment plans were created and optimized on the original CT image and recomputed on the two warped images to be compared. The dose volume histograms were compared for the warped structures that were identical in both warped images. This procedure was repeated for the phantom with full, half full, and empty bladder. Results: The results indicated mean HU differences of up to 120 between registered and ground-truth deformed CT images. However, when the CBCT intensities were calibrated using a region of interest (ROI)-based calibration curve, these differences were reduced by up to 60%. Similarly, the mean differences in average vector field lengths decreased from 10.1 to 2.5 mm when CBCT was calibrated prior to registration. The results showed no dependence on the level of bladder filling. In comparison with the dose calculated on the primary deformed CT, differences in mean dose averaged over all organs were 0.2% and 3.9% for dose calculated on the secondary deformed CT with and without CBCT calibration, respectively, and 0.5% for dose calculated directly on the calibrated CBCT, for the full-bladder scenario. Gamma analysis for the distance to agreement of 2 mm and 2% of prescribed dose indicated a pass rate of 100% for both cases involving calibrated CBCT and on average 86% without CBCT calibration. Conclusions: Using deformable registration on the planning CT images to evaluate the IMRT dose based on daily CBCTs was found feasible. The proposed method will provide an accurate dose distribution using planning CT and pretreatment CBCT data, avoiding the additional uncertainties introduced by CBCT inhomogeneity and artifacts. This is a necessary initial step toward future image-guided adaptive radiotherapy of the prostate.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TU-F-12A-03: Using 18F-FDG-PET-CT and Deformable Registration During Head-And-Neck Cancer (HNC) Intensity Modulated Radiotherapy (IMRT) to Predict Treatment Response

Purpose: To evaluate the effect of deformable vs. rigid registration of pre-treatment 18F-FDG-PET-CT to intra-treatment 18F-FDG-PET-CT on different standardized uptake value (SUV) parameters and investigate which parameters correlate best with post-treatment response in patients undergoing IMRT for HNC. Methods: Pre-treatment and intra-treatment PET-CT (after 20Gy) scans were acquired, in addition to a 12 week post-treatment PET-CT to assess treatment response. Primary and lymph node gross tumor volumes (GTV-PRI and GTV-LN) were contoured on the pre-treatment CT. These contours were then mapped to intra-treatment PET images via rigid and deformable registration. Absolute changes from pre- to intra-treatment scans for rigid and deformable registration were extracted for the following parameters: SUV-MAX, SUV-MEAN, SUV-20%, SUV-40%, and SUV-60% (SUV-X% is the minimum SUV to the highest-intensity X% volume). Results: Thirty-eight patients were evaluated, with 27 available for classification as complete or incomplete response (CR/ICR). The pre-treatment average tumor volumes for the patients were 24.05cm{sup 3} for GTV-PRI and 23.4cm{sup 3} for GTV-LN. For GTV-PRI, there was no statistically significant difference between rigid vs. deformable registration across all ΔSUV parameters. For GTV-LN contours, all parameters were significantly different except for ΔSUV-MAX. For deformably-registered GTV-PRI, changes in the following metrics were significantly different for CR vs. ICR: SUV-MEAN(p=0.003), SUV-20%(p=0.02), SUV-40%(p=0.02), and SUV-60%(p=0.008). The following cutoff values separated CR from ICR with high sensitivity and specificity: ΔSUV-MEAN=1.49, ΔSUV-20%=2.39, ΔSUV-40%=1.80 and ΔSUV-60%=1.31. Corresponding areas under the Receiver Operating Characteristics curve were 0.90, 0.81, 0.81, and 0.85, respectively. Conclusion: Rigidly and deformably registered contours yielded statistically similar SUV parameters for GTV-PRI, but not GTV-LN. This implies that neither registration should be solely relied upon for nodal GTVs. Of the four SUV parameters found to be predictive of CR vs. ICR, SUV-MEAN was the strongest. Preliminary results show promise for using intra-treatment 18F-FDG-PET-CT with deformable registration to predict treatment response.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Registration of clinical volumes to beams-eye-view images for real-time tracking

Purpose: The authors combine the registration of 2D beam’s eye view (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.

60 APPLIED LIFE SCIENCES↗