Volume 34, Issue 6, June 2007
Index of content:
- Joint Imaging/Therapy Moderated Poster Session: Exhibit Hall C
- Moderated Poster — Area 3 (Joint): Correction Strategies
SU‐DD‐A3‐01: Dosimetric Evaluation of Daily Rigid and Non‐Rigid Geometric Correction Strategies During On‐Line Image‐Guided Radiation Therapy (IGRT) of Prostate Cancer34(2007); http://dx.doi.org/10.1118/1.2760332View Description Hide Description
Purpose: To evaluate a geometric image guidance strategy that simultaneously correct for various interfractional rigid and non‐rigid geometric uncertainties in an on‐line environment, using field shape corrections (by modifying MLC). This technique was dosimetrically compared to simpler and more popular image guidance strategies (e.g., linear corrections). Method and Materials: Five prostate cancer patients with daily CT studies were analyzed. All patients were planned with a simplified intensity modulated radiation therapy (SIMAT) technique. A uniform 5‐mm margin was used. The image‐guided geometric correction strategies simulated were (1) translational correction based on daily gross CTV registration (“CTV”), (2) translational correction with daily MU recalculation (“MU‐CTV”), and finally, (3) translational correction with MU re‐calculation and daily MLC corrections to account for prostate deformations (“MU‐MLC”). Deformable image registration was performed on all treatment CT studies for dose accumulation. Generalized equivalent uniform dose (gEUD) index was used for dosimetric comparisons. Results: As expected, some dosimetric differences in the target volume were observed between the three image guidance strategies. For example, up to ± 2% discrepancy in prostate minimum dose were observed among the techniques. Of them, only the “MU‐MLC” technique did not reduce the prostate minimum dose for all patients (i.e., ⩾ 100%). However, the differences were clinically not significant to indicate the preference of one strategy over another, when using a uniform 5‐mm margin size. For the organ‐at‐risks (OARs), large rectum sparing effect (⩽ 5.7 Gy, gEUD) and bladder overdosing effect (⩽ 16 Gy, gEUD) were observed. Conclusion: The results suggest that a linear translational correction (i.e., “CTV”) is adequate to maintain target coverage, for margin sizes at least as large as 5 mm. In addition, due to large fluctuations in OAR volumes, innovative image guidance strategies are needed to minimize dose and maintain consistent sparing during the whole course of radiation therapy.
SU‐DD‐A3‐02: Automatic Detection of Positional and Anatomical Setup Errors in CT‐Based Image Guided Radiation Therapy34(2007); http://dx.doi.org/10.1118/1.2760333View Description Hide Description
Purpose: The primary objective of this study was to develop a novel tool using Kernel Classification that could be used to assist in identifying patients with setup issues that result in unacceptable random and systematic errors. Method and Materials: Inter‐Fraction motion was retrospectively analyzed for 30 H&N patients that were positioned for treatment using megavoltage CT (MVCT) images acquired on a helical tomotherapy system. Manual adjustments, in the AP, SI, LAT and Roll planes, were made to ensure correct target alignment from MVCT images acquired on the tomotherapy system. Results: A total of 30 Head and Neck patients were MVCT imaged prior to treatment delivery for a total of 992 imaging sessions. All 30 patients had a decrease in the cross‐sectional volume due to a combination of weight loss and/or tissue response to radiation. Patient errors ranged form incorrect headrest to a shirt and tie that where not removed before treatment. A Principal Component Analysis was performed and showed that there was a relationship present between the stage of the cancer, the patient's overall percent weight loss, and the mean lateral, longitudinal, and vertical shift values.
The kernel classification technique correctly identified 23 out of the 24 Head & Neck patients as either having normal set‐up or problematic setup using their respective shift data sets. These classifications were made using only the shift values from the first 14 treatments. The predictive performance seriously degraded when data from fewer than 14 treatments were used. Conclusion: This study demonstrated that the kernel regression classification method was able to correctly identify the cause behind IGRT positioning problems for individual Head & Neck patients. The study also validated the fact that IGRT positioning problems cause abnormal problem‐specific distributions in the shift data without using the complicated, and generally time‐consuming, statistical distribution tests.
34(2007); http://dx.doi.org/10.1118/1.2760334View Description Hide Description
Purpose: The purpose of this work is to develop, validate, and implement a technique in which imaged‐guided patient setup using gated kV images of implanted fiducials can be implemented for mobile lungtumors.Method and Materials:Software for matching markers by comparing 2D images with projections of 3D CTdata sets was validated by acquiring respiratory‐gated kV images of a phantom on a moving platform. The procedures were implemented for patients in our clinic who have mobile lungtumors with fiducials implanted in the periphery of the tumors. Treatment was delivered using respiratory gating, and cine images of each of the gated treatment fields were acquired each treatment to examine uncertainties in tumor location after gated image guided alignment. Results: For the phantom study, the 2D‐3D marker matching software is accurate to within 1 mm. This accuracy is limited by the ability to locate the fiducials in the CTimages (slice thickness 2.5 mm). Setup errors were reduced from 4 to 1 and 6 to 2 mm in the LR and SI directions (p<0.001 f‐test) respectively for our first patient. Shifts larger than 7 mm (PTV margin used to account for setup uncertainties) were observed on 13 of the 33 fractions for our first patient. Similar results were obtained for the other fiducial patients. Conclusion: Large variations in daily tumor position were observed and corrected for by acquiring gated kV images using the fiducials in these images for daily alignment. Respiratory gating in conjunction with image guided patient setup (planar kV imaging) using fiducials implanted near mobile lungtumors is an effective method of accurately aligning tumors without acquiring higher‐dose volumetric CT scans. This technique can be implemented using both kV imaging and standard MV portal imaging.
SU‐DD‐A3‐04: On‐Line Re‐Optimization of Prostate IMRT Plans for Image Guided Adaptive Radiation Therapy34(2007); http://dx.doi.org/10.1118/1.2760335View Description Hide Description
Purpose: This study proposes a novel on‐line adaptive planning technique for the prostate treatment by directly re‐optimizing the IMRT plan based on both the “anatomy‐of‐the‐day” and the original plan information. Method & Materials: Using a commercial treatment planning system, an initial IMRT plan for prostate cancertreatment was optimized using seven coplanar 15‐MV beams based on the structures of interest (SOIs) delineated in the planning CT. Then, the optimized plan with both intensity fluence maps and dose distributions was exported to an in‐house adaptive planning platform. To correct for target variations, a set of on‐board cone‐beam CT(CBCT)images were acquired prior to treatment and the daily SOIs (“anatomy‐of‐the‐day”) were identified. The original planning SOIs and daily SOIs were registered using a thin‐plate‐spline based deformable registration algorithm. The optimal dose distribution was then deformed to match the “anatomy‐of‐the‐day” and served as the “prescription dose distribution” for the re‐optimization process. Linear programming algorithm was used for re‐optimization and could reach a solution in seconds. The algorithm also used the original IMRT plan information as the initial solution. Both hard and soft constraints were used to differentiate the priorities of meeting the prescription dose distribution. A proof‐of‐concept case using an IMRT prostate plan was presented. The dose‐volume histograms from the original, the uncorrected and re‐optimized plans were compared. Results: The proposed re‐optimization technique reached solutions within 1minute on a desktop PC. The D95(min/max) doses to the GTV were 99.3%(90.0%/104.1%), 83%(57.6%/104.1%) and 98.3%(90.0%/103.8%), for the original, uncorrected and re‐optimized plans, respectively. The median doses to the rectum and bladder were comparable for the original and re‐optimized plans (41.0% vs. 45.0%, and 14.0% vs. 16.4%, respectively). Conclusion: It is technically feasible to perform on‐line re‐optimization based on the “anatomy‐of‐the‐day” and to achieve results similar to the original dose distribution.
SU‐DD‐A3‐05: Scatter Kernel Estimation and Correction with Edge‐Spread‐Function Method for CBCT Imaging34(2007); http://dx.doi.org/10.1118/1.2760336View Description Hide Description
Purpose: To study the characteristics of scatter in kilo‐voltage CBCTimaging, and to develop a method of reducing or removing scatter related artifacts and improve CBCTimage quality. Methods and Materials: The scattered radiations were modeled as depth‐dependents pencil‐beam kernels, which were derived using an edge‐spread‐function (ESF) method, for a CBCTimaging system. The ESF geometry was achieved with a half‐beam block created by a 3‐mm‐thick lead sheet placed on top of a stack of slab solid‐water phantoms. Measurements for 8 water‐equivalent thicknesses (range from 0 to 41 cm) were taken with (half‐blocked) and without (unblocked) the lead sheet, and corresponding pencil‐beam scatter kernels or point‐spread‐functions (PSF), were then derived without assuming any empirical trial function. Scatter correction was incorporated into the reconstruction process to improve CBCTimage quality. The process is summarized as the following: 1) The ESF was extracted from the half‐blocked images; 2) The line‐spread‐function (LSF) of the system was calculated by taking the derivative of the ESF; 3) By assuming a symmetrical scatter kernel, the PSF was derived using a filtered‐back‐projection technique; 4) For thicknesses not measured, the corresponding PSF was interpolated from the measured data; 5) An iterative method was used to remove scatter from the projection image; 6) A reconstruction algorithm was applied to the scatter removed projection to derive CBCTimages with improved image quality. Results: The scatter kernels were successfully derived and verified with phantoms. The scatter artifacts were reduced and the image quality was improved for CBCTimages incorporating the scatter removal technique. For a 32 cm‐diameter uniform phantom, the flatness of reconstruction image was improved from 21.6% to 3.8%. Conclusion: We developed a method to determine the scatter kernel in CBCT system and reduce the scatter related artifacts utilizing the derived kernel. Conflict of Interest: Supported by Varian.
34(2007); http://dx.doi.org/10.1118/1.2760337View Description Hide Description
Purpose: Previously proposed approaches to mitigate the effect of internal motion in IMRT include motion‐correlated delivery (gating, tracking), and adaptive treatment plan optimization employing a probabilistic description of motion (pdf). We test “tumor trailing” strategy, utilizing the synergy of motion‐adaptive treatment planning and delivery methods. Methods: The target motion is a superposition of a “fast” cyclic component (respiratory) and “slow” aperiodic trends (exhale baseline drift). The trailing strategy employs real‐time motion monitoring to identify “slow” shifts, and corrects for these by applying set‐up (e.g., couch position) adjustments. Delivery does not track the target position exactly, but trails the slow systematic trend due to the delay between the time a shift occurs, is reliably detected and, subsequently, corrected. The “fast” cyclic motion is accounted for with robust motion‐adaptive treatment planning, which allows for variability in motion parameters [Chan et al. Phys. Med. Biol. 51:2567–83 (2006)]. Motion‐surrogate data from gated IMRT treatments were used to test algorithms that identified systematic shifts (based on observation of “running mean” position), and to provide pdf data for motion‐adaptive planning. Test IMRT fields were delivered on linac to a programmable moving phantom. Dose measurements were performed with a commercial two‐dimensional ion‐chamber array. Results: Detection of systematic shifts was possible with a delay of 12–15 seconds. Correcting for slow systematic shifts during delivery of motion‐adaptive plans led to improvement in dose uniformity, conformity to target (vs. standard). Conclusion: By reducing intrafractional pdf variability, trailing strategy enhances relevance and applicability of motion‐adaptive planning methods, improves conformity of delivered dose to the target in the presence of irregular motion. In combination with respiratory gating, trailing can increase the duty cycle, account for residual motion within the gating window. It requires relatively minor modifications to software, equipment already in place for respiratory‐gated treatments.
Supported by NIH (5P01‐CA21239‐25), MIT, DKFZ grants.
- Moderated Poster — Area 3 (Joint): Imaging for Therapy Assessment
34(2007); http://dx.doi.org/10.1118/1.2760358View Description Hide Description
Purpose:PET use for radiotherapytreatment planning and monitoring has rapidly increased. Accumulating evidence suggests that characteristics of pre‐treatment FDG‐PET could be utilized as prognostic factors to predict radiotherapy outcomes in different cancer sites. Direct standardized uptake value (SUV) measurements were traditionally used to assess risk. However, such measurements are limited to intensity values irrespective of tumor‐microenvironment topological features. To improve our understanding of embedded information in PET, we are investigating new approaches for analyzing and extracting statistically robust candidate variables for predicting post‐radiotherapy relative‐risk. Method and Materials: We investigated two approaches for summarizing reliable information from PETimages to predict outcomes. The first approach is a generalization of the dose‐volume histogram (DVH) concept into functional imaging referred to as intensity‐volume histogram (IVH). An IVH would summarize the 3D functional imaging information for any anatomical structure into a single curve. Analogous to its DVH counterpart, intensity‐volume metrics are derived. The second approach utilizes extraction of shape and texture features that would characterize the structure of interest. These features enable mimicking of human perception of in terms of geometrical differences and texture variability. The tools are demonstrated on a subset of 14 cervix cancer patients who underwent pre‐treatment FDG‐PET scanning. Results: Nineteen candidate features, extracted from delineated tumor volumes, were analyzed for assessment of patients' risk of failure. Our preliminary results indicate that an intensity‐volume difference metric and texture energy had the strongest predictive power. A combined multivariable logistic regression model of these two variables yielded a Spearman's correlation of 0.53 (p=0.02) and area under ROC curve of 0.8. Conclusion: We have demonstrated new methods for analyzing functional imaging data. These methods allow for extracting visual cues and metrics that could facilitate incorporation of PETimaging information into assessment predictors of patient's response to radiotherapy treatment.
34(2007); http://dx.doi.org/10.1118/1.2760359View Description Hide Description
Purpose: Conventional deformable registration treats every voxel in an image set equally. In reality, not all regions are equal: some parts are rigid and some are deformable. We investigate a strategy of using a priori knowledge of the system to improve the accuracy and robustness of deformable registration. Method: Our calculation consisted of two natural steps. First, the input images are auto‐classified into feature regions based on the intensity information. The feature regions in the fixed and moving images are matched as feature region pairs using SIFT (scale‐invariance feature transformation) method. Secondly, the established feature region pairs are used as pre‐determined control point associations to facilitate the thin plate spline (TPS) deformable registration. Because of the pre‐association of the feature pairs, there is no need to manually place the homologous control points, which is a difficult task (as detailed anatomy knowledge is often required) and has been a major source of inaccuracy. The proposed algorithm is evaluated by using a digital phantom and five sets of 4D CTimages of different disease sites. Results: A method of incorporating prior knowledge into TPS deformable image registration has been developed. Using SIFT method, the feature region pairs can be easily identified. For example, for a region inside or close to a piece of bony structure, the correspondence can be found using SIFT because of the lack of deformation. In the digital phantom experiments, the new TPS algorithm is able to obtain a transformation matrix, which agrees with the known ones to within 3%. For the patients, similar level of success was achieved. A comparison with conventional TPS or BSpline approaches, the technique improves the accuracy and robustness. Conclusions: With incorporation of feature pairs, the deformable registration is significantly improved and much robust and accurate registration is attainable.
SU‐EE‐A3‐03: Automatic Target Position Verification with 3‐D Reconstruction of Implanted Markers Using EPID Images34(2007); http://dx.doi.org/10.1118/1.2760360View Description Hide Description
Introduction: In previous studies, a technique using an electronic portal imaging devices(EPIDs) in cine mode has been validated for tracking gold fiducials implanted in the liver for respiratory gating and stereotactic body radiation therapy. However, it was time‐consuming and labor‐intensive since the marker recognition was not performed automatically. In this study, we present an automatic algorithm to quickly and accurately extract the markers in EPIDimages and reconstruct their 3‐D positions. Materials and Methods: The markers were placed in a solid water phantom. Images were acquired using a linear accelerator operating with the 6 MV photon beam at several gantry angles and couch angle positions. A sequence of images was created by collecting the exit radiation using the EPID in cine mode. The fiducials were recognized and detected using a sequence of filters including the Wiener, median, and Laplacian filters. The position of each seed in the EPIDimages was backprojected towards the source position at each beam angle. To reconstruct the optimized seed position in 3‐D, the centroid and Gaussian fit were applied respectively to the distribution of the center points. Results: The average displacement between the seed positions reconstructed with the EPIDimages and the seed locations in 3‐D CTimage is measured to be 3.57 ± 2.59 mm (0.44–7.66 mm) using the centroid. Using Gaussian fit we can accurately reconstruct the marker locations with 0.98 ± 0.38 mm positioning error (0.39–1.47 mm) by significantly reducing the statistical error introduced by the outliers arising from anti‐parallel beam projections. Conclusions: The 3D positions of implanted fiducials can be reconstructed using images from several beam angles. This algorithm will be used for patient data to find the average 3D target position duringradiotherapy treatment.
This work was partially supported by a grant from Varian Medical Systems, Inc.
34(2007); http://dx.doi.org/10.1118/1.2760361View Description Hide Description
Purpose: To assess the therapy‐induced changes of head and neck cancers using 18F‐FDG PET/CT imaging studies performed prior to and following the completion of a course of radiotherapy (RT) through a region‐of‐interest based (ROI) analysis.Method and Materials: As part of an ongoing study, nine patients with carcinoma of the head and neck had PET/CT imaging studies performed prior to the start of treatment (range: 4 – 58 days; median: 36 days) and following the conclusion of treatment (range: 33 – 63 days; median: 59 days). RT contours, created with the Pinnacle treatment planning system and CT simulation images, from physician‐approved plans were collected to align with pre‐ and post‐RT PET/CT images. Utilizing in‐house developed software, the contours were deformed to the pre‐ and post‐RT PET/CT images through a non‐rigid registration. The non‐rigidly aligned RT contours were then used as ROIs to collect data from the pre‐and post‐RT PET images. The standard uptake value (SUV) was calculated assuming that identical structures were contained in the pre‐ and post‐RT deformed volumes, which were altered by anatomic changes as a result of RT. Results: For the GTV contour, the mean pre‐ and post‐RT SUV and standard deviation was (5.6 ± 0.9) and (2.4 ± 0.6), respectively. Within the GTV, the mean maximum SUV prior to RT was (23 ± 4.6), while after the conclusion of RT, the mean maximum SUV was (5.0 ± 1.9). Additionally, the mean ratio of the pre‐ to post‐RT mean SUV was (2.5 ± 0.7), indicating an overall decrease in uptake of the tracer in tissues within the GTV contour. Conclusion: Through the use of deformable image registration, the feasibility of 18F‐FDG PET/CT ROI‐based analyses of RT‐induced changes in patients with head and neck cancers has been demonstrated.
SU‐EE‐A3‐05: Evaluation of Kilo‐Voltage Cone Beam CT Image Quality in Context to Dose Re‐Computation34(2007); http://dx.doi.org/10.1118/1.2760362View Description Hide Description
Purpose: The purpose of this study was to evaluate volumetric kV Cone Beam CT(CBCT)image quality at different scan parameter settings in context to treatment planning tolerances. Method and Materials: Both large and small density phantoms with eight density inserts were scanned by GE LS CT/PET system, as well as the Varian's OBITM system in half fan and full fan scanning modes. Scans for CBCTimages were performed at different tube currents (20‐, 40‐ and 80‐mA) and source‐imager distance (SID) (150cm and 160cm) after prior calibration of each mode. Deviation of the Hounsfield Unit (HU) values at different settings compared to conventional kV CTimages were obtained for further evaluation. We also adjusted the CT number in CTimages to simulate CBCT artifacts that was not produced by our experiments, and to see how much degradation of image would violate dosimetric feasibility of CBCT based treatment planning. Treatment plans for single beam or multiple beams were calculated based on CT,CBCT and modified CTimages for various phantoms geometries and patients. Results and Conclusions: Results show that the HU for different anatomies in the body have different amount of change for different scan parameters settings (including current, SID and fan angle used) for CBCTimage acquisition. Larger variations in HU appeared in lung and dense bone regions, compared to those with HU closer to tissue. Maximum variations in HU were found in the images with data truncation. Dose profiles, dose volume histograms, isodose distributions and Gamma values of CBCT based plan with images scanned at full fan mode agree relatively well with CT based plan. Larger dose discrepancy appears in lung or dense bone region. Results from the CT‐modified images based plans show that the dosimetric error becomes significant as the HU variation goes beyond 50.
34(2007); http://dx.doi.org/10.1118/1.2760363View Description Hide Description
Purpose: To investigate clinical feasibility for functional‐image‐based target localization via onboard single photon emission computed tomography (onboard SPECT). Specifically, to understand lesion visibility and signal‐to‐noise ratio (SNR) of onboard SPECTimaging with the following simulated variables: lesion size, target‐to‐background radiotracer uptake, and scan time. Method and Materials: We simulated onboard SPECTimaging for a patient in treatment position who had received a diagnostic‐level dose of hypoxia radiotracer. The planning CT of a typical breast cancer patient with lung mets was segmented into bone, lung, and soft tissue. Lesions were simulated in the right lung that varied in diameter (0.5cm, 1cm, 1.5cm, 2cm, 3cm) and radiotracer uptake (lesion:lung:soft tissue — 6:1:3, 12:1:3, 18:1:3). Radiotracer concentration was 1.4μCi/g in aerobic soft tissue. Attenuation was modeled for 140 keV photons. Projection images were simulated for a gamma camera with parallel collimation, and Poisson noise was added to projections to simulate several scan times: 1min, 2min, 5min, 10min, 20min. Noisy projection images were reconstructed by OSEM (10 subsets, 5 iterations). Post‐filtered reconstructed images were then analyzed by plotting image profiles across each lesion and calculating SNR.Results: One minute scan time provided good visualization of a 2cm lesion with 12:1:3 uptake ratio. When scan time remained constant at 5 minutes, 2cm lesions were visible for each uptake ratio. In another simulation, a 1cm lesion (18:1:3) was clearly visible following 5 minutes of scan time. As expected, SNR generally improved with increased lesion size, scan time, and relative radiotracer uptake. Conclusion: Scan times on the order of a few minutes yield sufficient information to visualize lesions as small as 1cm in the lung. These results suggest that onboard SPECT may be effective for real‐time and functional‐image‐based target localization.
- Moderated Poster — Area 3 (Joint): Localization
TU‐EE‐A3‐01: Evaluating the Impact of Probe Depression On Prostate Displacement in Ultrasound‐Guided Prostate IMRT Treatment34(2007); http://dx.doi.org/10.1118/1.2761410View Description Hide Description
Purpose: The prostate displacement under the mechanical pressure of abdominal ultrasound probe may compromise the accuracy of ultrasound‐guided prostate IMRT treatment. The purpose of this study is to investigate the impact of ultrasound probe depression on the prostate displacement by finite element method(FEM).Method and Materials: An ultrasound system (Restitu™, Resonant Medical System, Montreal, Canada) integrated with a CT‐Sim was used to acquire a full set of 3‐D ultrasound (US)‐CT images for a typical prostate cancer patient. The patient's structures, such as body, bone, bladder, and prostate, were contoured on the CTimages. These structures were utilized to generate a 3D finite element model. The mechanical properties of the patient's tissues and ultrasound probe, including elastic module (E) and Poisson's ratio (v), were set as: body (E=55kPa, v=0.45), bone (E=10GPa, v=0.45), bladder (E=10kPa, v=0.45), prostate (E=100kPa, v=0.45), and probe (E=3GPa, v=0.35), respectively. The corresponding displacement of prostate centroid during ultrasound localization was calculated by a FEM software (Ansys). Results: When the depression of ultrasound probe increased from 10mm to 50mm with an increment of 10mm on the patient's abdomen at an angle of 45 degree to horizontal plane, the displacement of prostate centroid was increased from 0.5mm to 4.9mm in the inferior direction and 0.4mm to 3.6mm in the posterior direction. There was no significant left‐right displacement of the prostate. The 3D vector displacement of prostate centroid is increased from 0.6mm to 6.1mm based on FEM calculation. Conclusion: The displacement of prostate was non‐negligible when the depression of ultrasound probe was more than 20mm on the patient's abdomen. This displacement should be considered in the 3‐D US—CT image‐registration and US‐guided radiation therapy.
TU‐EE‐A3‐02: Registration of Ultrasound Tissue‐Typing Images with CT Images for Image‐Guided Prostate Cancer Radiation Therapy34(2007); http://dx.doi.org/10.1118/1.2761411View Description Hide Description
Purpose: To develop an image registration scheme, which combines both rigid and deformable registration techniques, to map the ultrasonic tissue typing (UTT) images to the computed tomography(CT)images for tumor targeted prostate IMRTtreatment.Method and Materials: UTT spectrum analysis can identify the cancerous regions inside the prostate to guide prostate radiation therapy. Current IMRTtreatment plans for the prostate are designed on CTimages. We developed a rigid registration followed by a deformable registration scheme for tumor region mapping between CT and UTT images. The rigid registration was achieved by adjusting the relative position of the two image sets until the mutual information between them was maximized. The deformable registration was based on a biomechanical model and finite element method(FEM). The prostate from both image sets was outlined by the clinician and the surface deformation between the two contour sets were fed into the FEM software algorithm to derive the volumetric displacement inside the prostate. When the voxel points correspondence between two image sets were known, the tumor area detected with UTT was mapped onto the CTimages and used for 3D planning for dose escalation. Results: The algorithm was validated using a tissue mimicking deformable prostate phantom and ten prostate specimens. The urethra served as a marker for verifying the registration process. For phantom and ex vivo study, the displacement of the urethra matched well between the CTimages and the deformed ultrasoundimages. The distribution of the 2D matching errors for the urethra central point had a mean and standard deviation of 2.0mm ± 1.1mm. We started to apply our registration scheme using in vivo prostate scans.
Conclusion: We validated our registration scheme with our phantom and ex vivo studies. We demonstrated the feasibility of clinically employing this UTT method for intra‐prostatic tumordose escalation.
34(2007); http://dx.doi.org/10.1118/1.2761412View Description Hide Description
Purpose: To investigate the clinical feasibility of performing concurrent kilovoltage (kV) x‐ray imaging while the patient at the treatment position was irradiated by megavoltage (MV) beams. Method and Materials: Anatomical visibility and contrast details in kV images acquired with and without concurrent MV irradiations were compared. An on‐board imager (OBI) mounted to the gantry of a Varian 21EX Linac with an electronic portal imager (EPI) was used in this study. A pelvis (Rando) and a contrast (Vegas) phantoms were placed near the treatment isocenter with a detector‐to‐isocenter distance of 50 cm. The effect of MV beam and its dose rate on noise‐to‐signal ratio (NSR), defined as the ratio of the standard deviation to the mean pixel value in a region‐of‐interest (ROI), and relative contrast, defined as the ratio of mean pixel values between the signal ROI and the background ROI, were studied based on Vegas phantom. Results: Scattering MV beams and electronic noises due to kV detector scanning rate and MV beam pulse rate are two major factors affect the concurrent kV image quality. The calculated NSR increases as dose rate increases. Compared to kV images without concurrent MV irradiation, the NSRs of the contrast phantom were increased by a factor of 2.6 with 300MU/min MV beams and 4.5 with 600MU/min MV beams. However, the relative contrasts were increased by a factor of 1.02 with 300MU/min MV beam and 1.06 with 600MU/min MV beam. Conclusion: It is clinically feasible to acquire concurrent kV images during treatment with MV beams. The kV image quality may be improved by minimizing MV scattering effect and by correcting electronic noises. The ability to take kV images with concurrent MV irradiation allows real‐time verification and tracking of moving target during the gated treatment.
Partially supported by a research grant from Varian Medical Systems.
34(2007); http://dx.doi.org/10.1118/1.2761413View Description Hide Description
Purpose: Improving the quality of signals obtained with optical and magnetic tracking systems.Special focus is placed on the measurement of respiratory motion signals for motion compensated IGRT and the possibility of filtering this data to obtain low‐noise breathing signals. Method and Materials: The accuracy of five different tracking systems (NDI Polaris™, active and passive,Clarion MicronTracker™,BIG FP5000, NDI Aurora™) was examined by (a)tracking stationary markers over several hours,and (b) by attaching the markers to a Kuka KR16 robot to simulate human respiration.The à trous wavelet decomposition was used to decompose the measured signal into scales, and to remove scales related to high frequencies, i.e., noise. The method was applied to a sinusoidal signal with artificial noise modeled according to (a), to real measurements for a sinusoidal motion of the robot,and to a set of breathing motion data from an actual patient treated with the CyberKnife®. Motion prediction was applied to the data. Results: The error on the measurements of the stationary marker approaches a Gaussian distribution.For a tracking rate of 60 Hz, information related to breathing motion is represented by higher scales of the à trous wavelet decomposition. Removing the first three scales and resconstructing the signal from the remaining scales and trend it is possible to obtain close and smooth approximations of the original signal. The normalized RMS error for motion prediction is 0.3368 mm and 0.1378 mm for a simulated and the smoothed signal using normalized LMS prediction. Conclusion: Data from tracking devices is subject to device specific measurement noise. The à trous wavelet decomposition can be used to remove frequencies related to noise from measured breathing signals. The resulting signal is suitable for further processing, e.g., correlation with or prediction of tumor motion in the context of motion compensated IGRT.
TU‐EE‐A3‐05: Assessment of Treatment Site‐Specific Setup Accuracy and Reproducibility in Patient Positioning Using Daily MVCT Imaging34(2007); http://dx.doi.org/10.1118/1.2761414View Description Hide Description
Purpose: To assess treatment site‐specific corrections in patient positioning from TomoTherapy Hi‐ART® megavoltage images.Method and Materials: Initial positioning corrections, determined by fusion of daily pre‐treatment MVCT to planning CTimages,wereanalyzed for different anatomical treatment sites.Over 600,1400, and 1200 fractions of head and neck (H&N) and brain,lung, and prostate tomotherapy treatments, respectively, were assessed. Translational and rotational, per‐fraction setup corrections were retrospectively compiled from individual patient archive files. Setup corrections were compared amongst anatomical treatment sites. Setup reproducibility was assessed by analyzing standard deviations and histograms of setup corrections. Setup accuracy was also assessed by analyzing 3D vector lengths and magnitudes of corrections. Results: Large variations in setup corrections were seen in all three disease sites. H&N treatments had a significantly smaller (p<0.001) vector length standard deviation of 2.54 mm compared with 6.99 mm and 6.46 mm for lung and prostate treatments, respectively, but had no significantly different standard deviation in roll rotations. The frequency of translations of vector lengths ⩾ 10 mm were 48.9% and 48.1% of all lung and prostate treatments, respectively, whereas corrections of that magnitude occurred in only 1.56% of H&N treatments. Frequencies of roll rotations were more comparable among the three disease sites. Conclusions:Analysis of patient positioning corrections indicates large variations between patients and treatments, suggesting a role for imaging every patient per‐fraction to ensure the most accurate reproducibility. H&N treatments had a higher setup reproducibility and accuracy in the lateral,longitudinal,and vertical directions. Even so, they were not significantly different rotationally than prostate and lungtreatments, suggesting a role for daily imaging in all three treatment sites. In quantifying site‐specific positioning reproducibility and accuracy, this work may be useful for assessing new treatment planning margins for image‐guided procedures or for developing adaptive radiotherapy techniques.
TU‐EE‐A3‐06: Comparison of Prostate Localization with Online Ultrasound and Mega‐Voltage Cone‐Beam Computed Tomography34(2007); http://dx.doi.org/10.1118/1.2761415View Description Hide Description
Purpose: To analyze the online image‐guided localization data from 846 ultrasound (US)and 350 MV‐CBCT couch alignments for patients undergoing IMRT of the prostate. Method and Materials: Daily volumetric MV‐CBCT and USimages were acquired for 11 and 23 patients, respectively, after each patient was immobilized in a vacuum cradle and setup to skin markers as the center‐of‐mass. The couch shifts applied in the lateral (left‐right/LR), vertical (anterior‐posterior/AP), and longitudinal (superior‐inferior/SI) directions, along with the magnitude of the three‐dimensional (3D) shift vector, were analyzed and compared for both methods. The percentage of shifts larger than 5 mm in all directions was also compared. CTV‐to‐PTV expansion margins were estimated based on the localization data with US and CB image‐guidance.Results: Systematic and random shifts from CB versus US were: laterally, 1.6 ± 3.8 mm vs. − 0.7 ± 6.9 mm; vertically, − 0.9 ± 5.4 mm vs. − 0.2 ± 6.4 mm; longitudinally, −1.4 ± 2.9 mm vs. −2.9 ± 5.2 mm. The mean 3D shift distance was smaller using CB (6.6 ± 3.6 mm vs. 9.1 ± 6.5 mm) with a p‐value < 0.05. The US data show greater variability. The percentage of US shifts larger than 5 mm were 33%, 40%, and 31% in the LR, AP, and SI directions, respectively, compared to 17%, 31%, and 7% for CB. Conclusion: MV‐CBCT localization data suggest a different distribution of prostate center‐of‐mass shifts with smaller variability, compared to US. The online MV‐CBCT image‐guidance data show that for treatments that do not include daily prostate localization,one can use a CTV‐to‐PTV margin that is 2.5 mm smaller than the one suggested by US data,hence allowing more rectum and bladder sparing and potentially improving the therapeutic ratio.
- Moderated Poster — Area 3 (Joint): Motion Modeling
TU‐FF‐A3‐01: Periodic Autoregressive Moving Average Model for the Prediction of Intrafraction Respiratory Motion34(2007); http://dx.doi.org/10.1118/1.2761443View Description Hide Description
Purpose: The prediction of intrafractional tumor motion is required for the development of real‐time motion managed radiation therapy. This work investigates the ability of a Periodic Autoregressive Moving Average (PARMA) algorithm to model and predict respiration motion. Methods and Materials: The PARMA algorithm models input signals as partially correlated time‐series superimposed onto periodic waveforms. This investigation assessed the limitations of the PARMA method for accurately predicting signals with varying levels of non‐periodic behaviour. A one‐dimensional respiratory motion phantom was developed, which generates waveforms to simulate breathing patterns with randomized non‐periodicity and noise. Multiple signals were analysed for each of 441 levels of non‐uniformity in cycle‐length and amplitude. These signals were approximately 200 seconds long with 50 inhale‐exhale cycles on average. Results: Prediction errors were found to be dependent on prediction lag and the level of variation in the signal. At 0.5 s ahead of the input signal, the PARMA prediction was accurate to within 4.8±0.4%of the total motion extent when the respiration signals had a 14% standard deviation in cycle‐length and 14% standard deviation in inhale‐exhale amplitude. These errors increased to 9.4±0.8% for predictions made at 1.0 second ahead of the input signal. As the inter‐cycle variation in the phantom signals increased to 34%, the prediction errors also increased to 11.6±1.4% at 0.5 second and 21.6±2.0% at 1.0 second. Conclusion: Our investigation showed the PARMA prediction algorithm can be used to predict respiration patterns within the lag‐time required to make the mechanical adjustments to steer the treatment beam in‐sync with the motion of the target. However, an assumption of a single harmonic component results in a strong dependence on the stability in cycle‐length, with less dependence on the cycle‐to‐cycle amplitude of motion. As such, the PARMA algorithm may be used to predict resting respiration and well coached breathing patterns.
34(2007); http://dx.doi.org/10.1118/1.2761444View Description Hide Description
Purpose: We have developed a novel 4D‐CT reconstruction method based on deformable image registration. We used it to reconstructlung 4D‐CT images at any breathing tidal volume. We also fitted the estimated the lung motion field to the 5D lung motion model (Low 2005). Method and Materials: The multi‐slice 3D‐CT images were acquired by scanning the patient multiple times at each couch position and at multiple couch positions while the patient was free breathing. For 4D‐CT reconstruction, firstly we reconstructed the reference 3D‐CT image by using scans from the end of exhalation phases. We then computed the image motion for all multi‐slice scans with respect to the common reference image. The motion computation was accurate and free from the boundary occlusion problem. Motion field at any target tidal volume can be generated by interpolation on the computed motion fields. We then deformed the reference image according to the interpolated motion field to get the full volume 3D‐CT images at the target tidal volume. Finally, we fitted the computed image motion to the 5D lung motion model at selected voxel positions. Results: The reconstructed 3D‐CT images were smooth and without misalignment on the couch position boundaries. Statistical analysis results suggest that our new 4D‐CT reconstruction method typically (for over 87% situations) generates more accurate 3D‐CT images than conventional amplitude or phase angle sorting 4D‐CT reconstruction methods. The motion field fitted very well with the 5D lung motion model. Conclusion: Our new method to compute image motion with 4D‐CT dataset enables more accurate estimation of the respiratory motion parameters and allows for motion free 4D‐CT reconstruction. This help would help improve the lung breathing motion models and enhance its integration into radiation therapytreatment planning systems.