Volume 34, Issue 6, June 2007
Index of content:
- Joint Imaging/Therapy: Room M100F
34(2007); http://dx.doi.org/10.1118/1.2761701View Description Hide Description
Purpose:Single Photon Emission Computed Tomography(SPECT) onboard radiation therapy machines could enable the use of functional and molecular imaging in guiding the therapy beam and in monitoring disease. Costs and spatial constraints of onboard SPECT might be minimized if SPECT could be accomplished using the same flat‐panel detectors (FPDs) employed for onboard transmission imaging. This may require some re‐engineering of FPDs, and it is therefore important to understand the emission imaging performance of present‐day FPDs. Here we evaluate the electronic and offset‐and‐gain (OG) noise of current FPDs relative to the quantum noise characteristic of SPECTimaging.Method and Materials: A standard SPECTcollimator was placed over a FPD. A flat circular radiotracer phantom was constructed with an outer region of 18 μCi/ml and a smaller inner region of 53 μCi/ml. (Typical human dose is 1 μCi/ml.) The phantom was placed on the collimator/FPD. Large low‐frequency variations in FPD response were removed by fitting each region (no activity, low activity, high activity) of the emission image with low‐order polynomials. Noise (standard deviation) was computed within each region as a function of number of acquisition frames, enabling separate estimates of electronic, OG, and quantum noise.Results: Electronic and quantum noise were comparable at 12 times the radiotracerdoses typical of human SPECTimaging. For the particular OG correction considered, OG noise exceeded quantum noise after about 8 seconds (4 frames) of acquisition. Conclusion: For the FPD considered, electronic noise is about an order of magnitude above the quantum noise that would arise from radiotracer concentrations typical of human imaging. OG noise is also significant. These conclusions should be considered in the context of other aspects of FPD design. For example, thicker scintillators could boost emission‐imaging signal, thereby alleviating requirements on noise reduction.
TH‐D‐M100F‐02: The Development of a Realistic Digital PET Lung Phantom for the Evaluation of Tumor Volume Segmentation Techniques34(2007); http://dx.doi.org/10.1118/1.2761702View Description Hide Description
Purpose: A problem encountered in the development of novel tumor volume segmentation techniques for PETimages is the lack of a reliable method for evaluating their performance on clinical scans. We have developed a digital lung phantom to address this problem. Method and Materials: The three main components in the creation of the digital phantom were the voxel‐based Zubal anthropomorphic phantom, the simSET package for PET simulations and the creation of digital lesions. The Zubal phantom was used as input to the simSET software to obtain PET simulated projections and then the separately created digital lesions were fed as input to simSET to obtain lesion projections. Those were added to the Zubal phantom projections, which were then reconstructed to obtain the simulated abnormal PETlung scans. In order to test the accuracy of the digital phantom in representing real situations, the results were compared both qualitatively and quantitatively with real clinical scans. We compared image intensity histograms and statistical measures from regions of interest (ROIs) taken from the lung, the mediastinum and the tumors of both clinical and simulated cases. Results: The visual comparison of the image intensity histograms from different regions showed no significant differences in the appearance of the image intensity distributions. This was confirmed by comparing measurements of fractional standard deviation and skewness from the different regions. Adding more inhomogeneity to the digital lesions resulted in more realistic appearing lesions. Conclusion: Our results suggest that the constructed digital phantom can closely model real clinical scans. The full control over the lesion's size, shape and intensity and the activity in normal anatomy that this digital phantom offers, along with improvements that can come from the addition of motion and more inhomogeneity can make this phantom a powerful tool in the evaluation of PET segmentation methods.
TH‐D‐M100F‐03: A PET Head and Neck Tumor Delineation Approach Based On Adaptive Region‐Growing and Dual‐Front Active Contours34(2007); http://dx.doi.org/10.1118/1.2761703View Description Hide Description
Purpose: To present a novel, semi‐automatic hybrid method for PETtumor delineation utilizing adaptive region‐growing and dual‐front active contours. Method and Materials: The process begins when an experienced radiation oncologist manually draws a rough region of interest (ROI) that encloses a tumor. The image voxel having the highest intensity is chosen as a seed point. A region growing algorithm successively appends to the seed point all neighboring voxels whose intensities >= T% of the mean of the current region, which is updated after each voxel is added. When T varies from 100 to 0, the resulting volumes increase from a small region to the entire rough ROI. With this criterion, a sharp volume increase has been observed at certain T values. The process uses the sharp increases as landmarks that are used to define the image intensity level that is used to define the tumor boundary.
However, the tumor volume is found to be slightly larger than the anatomically defined volume possibly due to the partial volume effect. Therefore, we use a dual‐front active contour model to refine it automatically. We assign all points on the initial tumor boundary one label, and the seed point another label. Then we propagate the labeled boundaries towards each other. The evolution stops at meeting points that form the final tumor boundary. Results: A cylindrical phantom with seven hollow spheres of varying size (8–32mm) was constructed and scanned using a Siemens PET scanner under 12 various conditions. The method was tested on these 84 images with errors of volume overlap metric of 57.4% to 99.0% between the detected volumes and the actual volumes. Conclusion: This hybrid method guarantees a reproducible result for a specific image. Experimental results demonstrate the robustness, accuracy, reproducibility, and its potential usefulness in clinical radiation therapy planning.
34(2007); http://dx.doi.org/10.1118/1.2761704View Description Hide Description
Purpose: Quantitative validation of multimodality deformable registration and segmentation algorithms is a challenging problem. To address this issue, we built a deformable multimodal imaging phantom system based on a preserved swine lung and a computer controlled airflow system. Comparing to most other digital and physical phantoms, this phantom is truly deformable, MRI compatible, biologically similar to human tissues, and more representative of clinical conditions. The phantom was imaged with CT and MRI protocols and yielded high resolution benchmark images. Our goal is to utilize these benchmark images to provide realistic and robust validation of medical image processing algorithms in radiotherapy applications, including multimodal deformable image registration, segmentation, tissue, as well as 4D‐CT imaging and reconstruction methods. Method and Materials: After the preserved swine lung leakage problems were addressed, the lung was inflated using a computer controlled airflow system and then imaged. 3D‐CT and MRI scans were taken at certain tidal volumes. For 4D‐CT imaging, the lung was scanned according to 4DCT protocol (Low, Med Phys v30, p1254–63, 2003) while the airflow cycle in the lung was computer‐controlled. For demonstration, we performed deformable CT‐CT registration and CT‐MR manual fusion on the collected images. 3D‐CT images were acquired at different spatial resolutions, up to image dimension of 1024×1024×530 and voxel volume of 0.244×0.244×0.67 mm. Results: 3D‐CT images allow us to clearly identify up to the 6th airway bifurcation. MR images were also of adequate quality. Conclusion: We have built the deformable phantom system and obtained images of high resolution with 3D‐CT, 4D‐CT and MRI. The images are expected to be very useful for validation of different medical image processing algorithms. We conclude that this system is promising tool for investigating and validating deformable image algorithms for radiotherapy.
34(2007); http://dx.doi.org/10.1118/1.2761705View Description Hide Description
Purpose: Online imaging modalities, such as cone beam computed tomography(CBCT) or CT on‐rail provide online volumetric images. A fast, automatic and robust region‐of‐interest (ROI) delineation method is highly desired in image guided radiation therapy(IGRT). We have developed such a method and tested it via segmentation of head and neck (HN) fan beam CT and CBCTimages.Material and Methods: ROIs on planning CTimages were manually delineated using commercial treatment planning system. A variational‐based deformable image registration algorithm was implemented to register planning CTimages to daily CTimages. ROIs on planning CTimages were automatically mapped to daily images using voxel matching information between planning and daily image datasets. The results were quantitatively and qualitatively validated by comparing to manual delineation. In order to accelerate computing speed, we paralleled the algorithm using message passing interface (MPI) on a Beowulf cluster with 16 processing elements (PE). Speed improvement was benchmarked. Results: The discrepancies between automatically and manually delineated ROIs on fan beam images were mostly within 2mm. Automatic segmentation of CBCTimages was acceptable by visual inspection. Benchmark results showed that paralleling efficiencies were above 95% and speedup factors were approximately equal to the number of PE used. With 16 PEs online delineation of HN images took about 1 minute. Conclusion: The online ROI delineation method we have developed is robust, fast and is suitable for HN online adaptive radiation treatments.
This research was partially supported by the Department of Defense Prostate Cancer Research Program under award number W81XWH‐07‐0083.
34(2007); http://dx.doi.org/10.1118/1.2761706View Description Hide Description
Purpose: The modeling of a 3D structure by interpolating a stack of 2D contours may result in an unrealistic faceted shape, even though each contour is smooth. A difficulty with geometric smoothing is that the surface can shrink after a number of iterations. This work investigates the use of a non‐shrinking smoothing algorithm in structure delineation for radiotherapytreatment planning.Materials and methods: The surface of a tubular structure is parameterized by an interpolating function using original contour data points in cylindrical coordinates. The center of the polar coordinates for each axial contour is placed on a smooth fitting function while the contour is still a single‐valued function. By interpolation the surface is re‐sampled into a set of evenly spaced vertices. In an iterative process each vertex is shifted by an average displacement vector from its neighbor vertices and scaled by a factor. Each step of iteration involves two shifts for every vertex with the scaling factor in opposite signs, in order to avoid shrinkage. The iterative process stops after a desired smoothness is achieved with all average displacement vectors smaller than a specified tolerance. This method is tested on five prostate IMRT cases. The axial contours of smoothed structure are displayed with the original contours for validation by three physicians. Results: The resulting surface appeared smooth in all projections. The physician approved the new contours for all five patients. The volume change for each structure was less than 2%. Treatment planning using smoothed CTVs and PTVs reduced the numbers of MU and MLC segments by 8 – 11%. Conclusions: A technique was developed for smoothing a structuresurface constructed using 2D contours. The calculation was fast for 3D contouring. Our planning results suggested that unrealistically irregular target shapes can have adverse effects on dose conformity and delivery efficiency.
TH‐D‐M100F‐07: Topology Preserving Diffusion Registration Incorporating with Gradient Orientation Information34(2007); http://dx.doi.org/10.1118/1.2761707View Description Hide Description
Purpose: The goal of this study was to develop an image intensity‐based diffusion registration algorithm that can be used for reliable automatic delineation of anatomical structures on daily CTimages. The constraint of the topology preservation method used in this algorithm ensures that the transformations are reliable and accurate. Method and Materials: To achieve accurate deformable image registration using a diffusion‐based method, we proposed to incorporate the gradient orientation information into the driving force in diffusion registration. This is similar to the symmetric force in the demons algorithm. We also introduced to use the positive Jacobian constraint, which is the fundamental requirement for topology preservation, as a guideline to determine the smoothing parameter in the diffusion algorithm to ensure that a realistic registration can be achieved. The planning contours were mapped onto the daily CTimage using the displacement field after the deformable image registration. The contour mapping serves as a validation of the algorithm proposed in this study. The performance of the proposed algorithm for register 3D CTimages of prostate and head and neck cancer patients were evaluated by visually assessing the agreement of the anatomical structures with deformed contours in target image.Results: The contours deformed with the topology preservation method are more accurate segmentation of the anatomical structures, compared to a similar method without this constraint. The positivity of Jacobian provided a way to evaluate the performance of the registration and to serve as guidance for selecting smoothing parameters. Conclusion: We proposed a diffusion registration algorithm incorporated with the intensity gradient information and the positive Jacobian constraint that ensures the transformations to topologically preservation of anatomical structures. Compared to demons algorithm, the proposed algorithm is more computational efficient and accurate, especially for prostate CTimages registration.
The research is in part sponsored by Varian.
TH‐D‐M100F‐08: Development and Evaluation of An Automatic Contour Propagation Method for 4D Radiotherapy34(2007); http://dx.doi.org/10.1118/1.2761708View Description Hide Description
Purpose: To develop and evaluate automatic contour propagation method for 4D radiotherapyMethod and Materials: 4D CTimages of one lungcancer patient and dynamic phantom images were acquired and resorted into ten respiratory phases. Demons deformable registration was performed to find the deformation field from the end‐exhale fixed phase to moving phases such as the end‐inhale phase. Regions of interest (ROIs) were contoured in the fixed phase with the Pinnacle3treatment planning system. The planar parallel contours were reconstructed as the 3D binary mask image data which has zero value outside and non‐zero value inside of the 3D outline of contouring. The 3D contour image data were transformed to another moving phase using the deformation field from the image registration result. On each 2D plane the binary image boundary was found to trace the closed intersection points. To assess the quality of the automatic contour propagation method, the contour for the moving phase was delineated manually. Visual inspection between the contour propagation and manual contour was performed as the qualitative evaluation. Two indices were used as the quantitative examination. One was the match index to indicate how well two contour sets match each other. The other was volume difference evaluation between two contour sets. Results: The demons deformable registration results show good alignment for both the phantom and the patient. The match index between manual and automatic contouring ranged from 0.96 to 0.99 for the dynamic phantom and from 0.95 to 0.99 for the lung patient. The volume differences were between 0.75 and 2.97 percent for the dynamic phantom and between 1.50 and 7.32 percent for the lung patient. Conclusions: The proposed automatic contour propagation method is simple and feasible without surface construction. It is a variable method for defining the ROIs for large amount of 4DCT data sets.
34(2007); http://dx.doi.org/10.1118/1.2761709View Description Hide Description
Purpose: The prostate contours drawn by physicians on CTimages tend to overestimate the real volume due to the poor contrast between the prostate and the surrounding soft tissues. The aim of this study was to utilize ultrasound (US)‐CT to guide a more accurate prostate segmentation for prostate IMRT planning. Method and Materials: In US‐CT modality, the ultrasound system (Restitu™, Resonant Medical System, Montreal, Canada) was integrated with CT‐Sim through an optic camera system, which was calibrated to the intersection point of wall lasers of CT‐Sim and was able to trace the position of ultrasound probe in real‐time. Thus, for each patient, the CT scan and ultrasound scan can take place at the same position and almost the same time. After compensating for the mechanical inaccuracy of CT‐sim and the image distortion of 3‐D USimages due to inconsistent ultrasound wave propagation speed in different tissue types, 3‐D CT and 3‐D USimages can be superimposed together naturally, since they share the same spatial coordinate system. Thus, the prostate contour drawn on 3‐D Ultrasoundimages can be transferred into CTimages for IMRT planning. Results: Five patients underwent 3‐D US‐CT scan in this study. First, a physician contoured the target volumes and surrounding critical organs on CTimages. Then the prostate was contoured on USimages for comparison. The fused US‐CT images revealed that the discrepancy between the prostate volume drawn on CTimages and ultrasoundimages takes place mostly at the lateral surface of prostate and the interface between prostate and rectum. The volume of the prostate drawn on USimages is 30%∼50% less than that obtained from CT.Conclusion: Ultrasound‐CT, a new multi‐modality imaging system, has a potential to provide a more accurate prostate anatomy definition for prostate IMRT planning thereby reducing radiation dose to surrounding critical structures.