Volume 35, Issue 6, June 2008
Index of content:
- Joint Imaging/Therapy: Scientific Session: Room 351
- Functional Imaging and Small Animal IGRT
35(2008); http://dx.doi.org/10.1118/1.2962857View Description Hide Description
Purpose: To design a conformal small animal micro radiation therapy (microRT) instrument, consisting of a cone beam microCT subsystem for submillimeter low dose structural imaging and image guided radiotherapy and an orthovoltage conformal micro irradiator with high radiationdose rate for high throughput conformal irradiation. Method and Materials: The microCT subsystem is based on an 80kVp micro‐focus x‐ray source with 75×75 um2 focal spot and a flat panel amorphous silicon detector with 1024×1024 pixels. The microRT subsystem design utilizes a 320kVp orthovoltage source with dual focus spots (0.4×0.4 mm2 at 800W and 1×1 mm2 at 1800 W). The orthovoltage beam is collimated using two orthogonal jaws and exchangeable apertures. The resolution of the cone‐beam micro CT and the therapy beam spatial precision was determined using a numerical model. The microCT radiationdose, the orthovoltage source spectral output, and dose rate were evaluated using a mouse digital phantom (MOBY) and a pencil beam algorithm. Results: microCT reconstructed tomographic data with a resolution of 125 μm is achievable using 128 projections and a maximum radiationdose of 2cGy. Automatic animal positioning and handling is performed within a precision of 100 μm. The treatment beam can be aimed at different latitude and longitude angles in steps of 2 arc min. and translated at 50μm steps (x,y,z). The beam cross section can be modulated with submillimeter precision using steps of 50μm. A radiationdose rate of 40 Gy/min is delivered when the system is operated at an average half‐value layer of 4.6 mm Cu and a maximum beam penumbra of 0.35 mm. Conclusion: We designed and numerically evaluated the performance of a microRT system integrated with a microCT for conformal radiotherapy of murine animal models.
This work supported in part by NIH grant CA108677.
TH‐C‐351‐02: Registration Based Automatic Segmentation and Wall Motion Analysis for 4D Cardiac Micro‐CT in Mice35(2008); http://dx.doi.org/10.1118/1.2962858View Description Hide Description
Purpose: We present an automated method for segmentation and tracking of hearth anatomy using 4D cardiac micro‐CT datasets in mice. Cardiac motion is determined and analyzed using the deformation field obtained after non‐rigidly deforming a template to all the time frames of the 4D dataset. Method and Materials: 4D micro‐CT image sets were acquired with a temporal resolution of 10 ms and 100 microns spatial resolution in four mice using the Duke in‐house developed micro‐CT scanner and cardio‐respiratory gating. Ten cardiac phases were used in the data acquisition. The images of the heart at different phases were registered using a BSpline deformable model. The contour points on the diastolic phase were automatically mapped to the corresponding points on the images of other phases following the mapping relation established by the deformable model. The method's stability to noise and artifacts in the input images was assessed using the 4D virtual mouse Moby phantom. Results: The deformable model was capable of accommodating significant variability of cardiac motion over time and across different individuals. The template was warped to the first phase of the 4D dataset with an accuracy of 0.95, 0.96 and for the left ventricle, myocardium and right ventricle as compared by the Hausdorff measure to manual segmentation. In between phases, worst segmentation accuracy was 0.93, 0.95 and 0.94 for left ventricle, myocardium and right ventricle. Additionally, the method automatically measures regional motion and deformation by probing the deformation field on the segmented contours. Conclusion:Heart wall contour evolution in 4D micro‐CT images can be easily defined and tracked using a BSpline deformable model, with no user interaction required. The method provides pre‐clinical accuracy while eliminating the labor‐intensive segmentation procedure.
Image acquisition was performed at the Duke Center for In Vivo Microscopy an NCRR/NCI National Resource (R21‐CA124584‐01, 2U24‐CA092656, P41‐RR005959).
35(2008); http://dx.doi.org/10.1118/1.2962859View Description Hide Description
Purpose: To develop a new facility to irradiate partial body of zebrafish embryos and test the system with an actual radiobiology experiment. Method and Materials: This micro‐irradiator uses a 50 kV photon beam, miniature x‐ray, Xoft Inc. The source is inserted in a cylindrical brasscollimator of 3 cm diameter and 3 cm long. A pinhole of 1 mm diameter along the central axis produces a well‐focused pinpoint beam with a sharp penumbra. A photodiode monitors the beam and provides readings for dose calculation. Specimens are irradiated at 6 mm from the collimator and they are accurately positioned on the beam using a video camera and a computer‐controlled movable table. The system was used to irradiate total and partial body of zebrafish embryos at 3 days post‐fertilization to investigate radiation induced apoptosis and microphages recruitment at 40 Gy for both irradiation modalities. Results: This irradiation facility is portable and can fit in any radiobiology lab. The image‐guidance and high precision of the movable table enable accurate specimen position. The beam monitoring system provides exact, fast, and easy dose determination. Total body zebrafish irradiation at 40 Gy shows a severe post‐treatment cell ablation effect and substantial apoptotic increase after 3 days post‐irradiation. For partial body irradiation, there is an increase in apoptotic cells and remarkable macrophages recruitment after 3 days post‐irradiation. Conclusion: This robust, simple, and effective image‐guided micro‐irradiator is an appropriate tool to accurately irradiate partial body of zebrafish embryos, cell cultures or any other small specimen used in radiobiology studies. The tests comparing total and partial zebrafish embryo irradiation revealed significant difference in cell response. In general, this novel micro‐irradiator has expanded the radiation modalities for very small animals used in radiobiology studies and opened the possibility to adventure deeper in radiotherapy research.
TH‐C‐351‐04: 18F‐FLT PET Imaging of Proliferative Response to An EGFR Inhibitor in HNSCC Xenograft Mouse Models35(2008); http://dx.doi.org/10.1118/1.2962860View Description Hide Description
Purpose: Growing interest in targeted cancer therapies requires increasingly sophisticated understanding of response in tumor microenvironments. This work quantifies the proliferative response of two murine xenograft tumormodels to an EGFR (epidermal growth factor receptor) inhibitor, cetuximab, using FLT‐PET imaging.Method and Materials: Athymic mice harboring human head and neck squamous cellcarcinoma (HNSCC) xenografts were injected with , a proliferation marker, and imaged on an Inveon microPET/CT scanner. MicroPET/CT imaging was performed on days one and five. Mice were then treated with cetuximab on days two and four with appropriate IgG controls. PET values were normalized by injected dose and weight (%ID/g) and evaluated in the tumor region. Results: Inhibition of FLT proliferation signal following cetuximab administration was statistically significant in a paired t‐test. Average FLT uptake in the tumor decreased from to in cetuximab‐treated SCC‐1483 xenografts and from to in cetuximab‐treated SCC‐1 xenografts. Maximum and cumulative FLT uptake showed similar trends. Initial proliferation rates and magnitude of treatment response were greater in the SCC‐1483 cell line. IgG controls did not show a significant change in FLT uptake. Conclusion: This work demonstrates the capability to measure the effect of a molecular inhibitor of EGFR signaling on proliferation within the tumor microenvironment as measured by PET. This technique should improve understanding of tumor response to EGFR inhibitor therapy and may provide a valuable tool to assess early treatment response to cetuximab in humans.
35(2008); http://dx.doi.org/10.1118/1.2962861View Description Hide Description
Purpose: FDG‐PET imaging is routinely used to diagnose and stage cancer patients. It is also gaining wide acceptance as a tool to assist in tumor delineation in radiotherapy (RT) treatment planning. However, target volume definition is subject to inter‐observer variability. The objective of this study was to evaluate several existing auto‐contouring methods and develop a technique that would reduce inter‐observer variability. Method and Materials: Eighteen rectal and anal cancer patients who had undergone PET‐CT imaging and received RT were retrospectively reviewed. For each patient, a FDG‐PET avid (AVID) region was contoured by an experienced clinician without the use of the CT scan. The AVID volume was compared to volumes derived by the automated methods. Three automated methods were used: a fixed SUV cutoff of 2.5, previously suggested in the literature, a percentage of the maximum SUV (%SUVmax), and an in‐house derived mathematical technique. A 43% threshold was found for %SUVmax using a phantom study with cylinders of known volumes filled with varying concentrations of FDG. The mathematical approach generated 3D volumes using a Confidence Connected Region Growing (CCRG) technique that calculated the mean and standard deviation from pixel intensities contained in a 3D volume grown from a seed pixel. Results: The class solution of using a single value of SUV or a %SUVmax proved limited. These two methods depend on the correct threshold being applied and need to be different for each patient. The resulting volume differences ranged from 1%–129%. The CCRG based volumes were within 8% of the AVID volumes with a range of 1%–23%. Conclusion: Assuming that the same seed pixel is chosen, the CCRG method reduces inter‐observer contouring variability on FDG‐PET images and provides a viable clinical solution by always growing the same volume.
Research partially supported by Siemens Medical Solutions.
TH‐C‐351‐06: Evaluation of a Compartmental Model for Estimating Tumor Hypoxia Via FMISO Dynamic PET Imaging35(2008); http://dx.doi.org/10.1118/1.2962862View Description Hide Description
Purpose: To evaluate a pharmacokinetic compartmental model for identifying intra‐tumor hypoxia using dynamic positron‐emission‐tomography (PET) imaging with 18F‐fluoromisonidazole (FMISO) radiotracer.Method and Materials: The compartmental model used for this work is an irreversible generic two‐tissue type implemented within a pharmacokinetic modeling program called Voxulus by Philips Research. A dynamic PET image dataset (spatial and time) was simulated with 3 tissue regions: normoxia, hypoxia and necrosis, and with an image‐based arterial input function. Each voxelized tissue time‐activity‐curve (TAC) simulation used typical kinetic parameters, generalized from 6 head‐and‐neck cancer patient FMISO‐PET data. The dynamic image was first produced without any statistical noise, to ensure that correct kinetic parameters were reproducible by Voxulus. Next, to investigate the stability of kinetic parameter estimation in the presence of noise, 1000 noisy samples of the dynamic image were generated, from which 1000 noisy samples of kinetic parameters were calculated, and used to estimate sample mean and covariance matrix. To further investigate how bias in the arterial input function affected the kinetic parameter estimation, a shift error was introduced in the peak amplitude, peak location and tail amplitude of the input TAC, and the bias of various kinetic parameters computed. Results: Without noise, the estimated kinetic parameters matched their true values perfectly. With noise, the hypoxia rate constant k3 had more variation than other parameters. The plasma‐to‐tissue and tissue‐to‐plasma rate constants (k1 and k2) for diffusible compartment, and vascular density β were highly correlated with each other; while k3 had no correlation with others. Voxulus was applied to estimate parametric image maps of hypoxia for 6 head‐and‐neck cancer patients. Conclusion: Mathematical phantom studies have been used to determine the statistical accuracy of Voxulus, which provides us guidance and confidence in clinical dynamic FMISO‐PET data analysis.
TH‐C‐351‐07: Partial Volume Effect Correction in PET Using Regularized Iterative Deconvolution with Variance Control Based On Local Topology35(2008); http://dx.doi.org/10.1118/1.2962863View Description Hide Description
Purpose: To present a new partial volume effect (PVE) correction method for PET with variance control, which is not dependent on prior anatomical information: a necessity for radiation treatment planning. Method and Materials: The method performs post‐reconstruction iterative deconvolution using a 3D Maximum Likelihood Expectation‐Maximization algorithm. To achieve convergence a One Step Late (OSL) regularization procedure that follows the work of Alenius et al is used. This technique was further modified to selectively control the variance depending on the local topology of the PETimage. Different regularization weights can be selected by the user for different parts of the image based on the signal‐to‐noise ratio. The procedure was tested for isotropic gaussian deconvolution functions with FWHM ranging from 6.31 mm to infinity. The method was applied to simulated and experimental scans of the NEMA NU‐2 image quality phantom with the GE Discovery LS PET/CT scanner and on patient scans. Results: Optimal sphere‐activity‐to‐variance ratio was obtained, when the deconvolution function was replaced by a few voxels wide step function. In this case the deconvolution method converged in ∼ 3 to 5 iterations for most points in both the simulated and experimental images. For the 1 cm diameter sphere, the contrast recovery improved from 12 % to 36 % and from 21% to 55 % for the simulated and the experimental data respectively. For the larger spheres the recovery coefficients were increased to above 68% and 80%. No increase in variance was observed except for few voxels neighboring strong activity gradients and inside the largest spheres. Testing the method using patient images indicated potential for increasing the visibility of small lesions within a non‐uniform background. Conclusion: Regularized iterative deconvolution with variance control based on local topology and on estimated image noise is a promising approach for partial volume effect corrections in PET.
35(2008); http://dx.doi.org/10.1118/1.2962864View Description Hide Description
Purpose: To assess tumor differentiation in patients with glioblastoma multiforme (GBM) using dynamic 11C‐methionin (D‐MET) PET and fuzzy c‐means (FCM) clusteringanalysis; and to evaluate the added value of D‐MET PET in radiation therapy (RT) target definition. Method and Materials: D‐MET PETimages were obtained prior to RT in 25 patients with GBM. Each scan was composed of 15 phases acquired at 0–50 minutes following injection. Conventional MRI was also acquired before RT for target volume definition and after RT for evaluation of treatment outcome. D‐MET PET data were normalized to the mean uptake of each individual's cerebellum. Volume of interest (VOI) for the analysis was defined based on pre‐RT FLAIR‐MRI and extended to incorporate regions of high uptake of MET. Time‐activity curves of MET uptake in the VOI were classified using a FCM clustering algorithm with spatial constraints. The optimal number of clusters was determined for each dataset by calculating several clustering validity indices. The results of classification were reviewed by experts; and were also correlated to the patterns of local failure after RT. Results: Using the FCM clustering algorithm, time‐activity curves of MET uptake in the VOI were successfully partitioned into tumor, normal braintissue, inflammation response, surgical cavity and edema. Heterogeneous MET uptake in the tumor was also differentiated. In 15 of the 25 patients who had tumor progression, the pre‐RT PET in the clusters correspondent to the locations of recurrence had a median uptake value of 1.47 (last dynamic phase), which involves clusters beyond the hottest ones. Conclusion: This study demonstrated that dynamic MET‐PET is capable of differentiating active tumors in patients with GBM. It is also promising in providing extra information for RT target definition.
Supported by NIHP01CA59827.
TH‐C‐351‐09: Partial Volume Correction of PET‐Imaged Tumor Heterogeneity Using Expectation Maximization35(2008); http://dx.doi.org/10.1118/1.2962865View Description Hide Description
Purpose: Due to the limited spatial resolution of PETimaging, small objects experience partial volume effects which impact the quantification and spatial distribution of imagedtumor heterogeneities. This study examines the ability of an iterative partial volume correction (PVC) method to restore imaged heterogeneity to an object heterogeneity map using expectation maximization. Method and Materials:TreatingPET as a linear system, images obtained through the convolution of a true object's radioactivity distribution and the system's point spread function (PSF) may be used to uncover the true object's activity distribution. The presented method uses iterative expectation maximization with the measured system PSF to determine the true object activity distribution. The three‐dimensional spatially dependent PSF was obtained by Gaussian fitting point objects imaged on a Discovery LS PET/CT scanner radially in single plane. The technique was tested on dual‐tracer heterogeneity phantoms using spheres of 10 and 15 mm diameter as both hot and cold heterogeneities relative to the phantoms' uniform tumor activity. The method was also applied to clinical studies to observe the impact of quantitative changes. Results: The observed PVC showed dependencies on heterogeneity size and contrast with surrounding regions. This PVC technique successfully recovered phantom heterogeneities hotter than uniform tumor activity, but experienced difficulty with cold heterogeneity. Quantitative image accuracy was restored as heterogeneities experienced shifts of 25 and 35% for diameters of 15 and 10 mm, respectively. Using changes in the image correction matrix proved a successful stopping criteria for determining the optimal number of iterations for PVC in phantoms. Changes as large as ±20% were frequently observed in patient heterogeneities. Conclusion: The expectation maximization PVC method successfully recovers tumor heterogeneity lost through PETimaging. Inclusion of PET information into treatment planning or treatment assessment would greatly benefit from the quantitative accuracy gains shown in this PVC method.