Index of content:
Volume 35, Issue 6, June 2008
- Joint Imaging/Therapy: Scientific Session: Auditorium C
- Intrafraction Motion and Correction Strategies
35(2008); http://dx.doi.org/10.1118/1.2962847View Description Hide Description
Purpose: Direct measurements of the lung deformation by MR tagging during breathing motion revealed that the deformation is neither linear nor continuous. To improve the accuracy of deformable registration by finite element analysis, the lung is segmented into subanatomical regions, to allow sliding motion between lobes. Method and Materials: One healthy volunteer underwent MR tagging studies. Multiple‐slice two‐dimensional and volumetric three‐dimensional MR tagged images of the lungs were obtained at end‐inhalation and end‐exhalation, and deformation vector field (DVF) was computed. A patient CT was selected and the left lung was segmented into upper and lower lobes. The 2D contours were converted to 3D mesh and subsequently to tetrahedra for finite element analysis(FEA).Boundary conditions and material properties were assigned in the ABAQUS, FEA modeling software. Diaphragm provided the active driving pressure and the chest wall provided a passive constraint. The FEA computed DVF was compared with the measured DVF using a similarity index (SI) normalized to 1 for a perfect match and 0 for a complete mismatch. Results: A 3D lung DVF was generated from the MR tagging. Distinct discontinuity was observed between lung lobes. With the assumption that the lung is a continuous elastic object, the FEA model failed to model the discontinuity along the fissure and resulted in a low SI (0.53), which was improved to 0.89 by segmenting the lung and introducing the additional freedom of inter‐lobar motion. Conclusion: DVF measured from MR tagging can be a reference in validating deformable registration of lung, particularly in regions with low imaging contrast. Using this reference, the authors were able to significantly improve the accuracy of deformable registration with lobar segmentation in the FEA modeling.
TH‐C‐AUD C‐02: Automatic Extraction of Salient Interest Points in 3D Images for Contour Propagation in IGRT35(2008); http://dx.doi.org/10.1118/1.2962848View Description Hide Description
Purpose: To efficiently propagate patient contours to multi‐fraction Cone‐Beam CT volumes. Method and Materials: An auto‐segmentation scheme was developed which uses anchor points to propagate structure delineations from one image to another, accounting for the changing patient anatomy. Four patients involved in 35‐ or 40‐fraction Head‐and‐Neck treatment schemes were selected retrospectively. The 8–12 Organ‐at‐Risk contours drawn in their initial planning CT included spinal cord, brainstem, optic structures, and parotids, to be propagated to the CBCTimage acquired at each fraction. Our method involves three steps: a) Generate a patient‐specific compressed model in terms of salient points from the planning CTimage. The automatic extraction of these uses a developed 3‐dimensional extension of the SIFT algorithm; b) Retrieve this model in every CBCT dataset, via automatic block‐matching maximizing the local correlation between neighborhoods around the interest points; c) Propagate the original contours based on the thin‐plate‐spline warping transformation derived from salient point pairs. Results: For each patient, over 500 salient points were extracted in 5–7 minutes, and subsequently retrieved within 45 seconds in every CBCTimage, into which the 8–12 contours were propagated in 10 seconds. The results show clear improvement, compared to contours obtained by both a mere copy and voxel‐based rigid registration; the typical neck shrinking and spine flexions especially are followed. Our method is more robust and computationally affordable than voxel‐based deformable image registration.Conclusion: The contours can be successfully propagated to all fractions based on significant salient anchor points extracted to summarize one patient's gray‐level information and geometry. The thus highlighted deformations of the critical structures i) can help determine a safe dose map and be reflected in Planning organ at Risk Volume (PRV) margins, ii) may be used to support time‐efficient re‐planning in adaptive radiotherapy.Conflict of Interest: This research is partially supported by Philips Healthcare.
35(2008); http://dx.doi.org/10.1118/1.2962849View Description Hide Description
Purpose: Fiducial markers or bone structure are widely used in patient setup for image guided therapies and is also important in interventional radiology. A general approach (“2D‐2D”) for patient setup is based on 2D projected images on the anterior‐posterior (AP) and lateral (LAT) directions. However, it is still an open question to decide the number of fiducial markers required for patient setup, the optimal locations for marker plantation, the selection of fiducial markers versus bone structure, and the error estimations of different setup methods. Our work will address some of these issues and provide quantified information. Method and Materials: Clinical patient setup procedure (“2D‐2D”) is simulated using rigid registration. Least square metric is applied to minimize the alignment error of markers. Registration under different degree of freedom (DOF) are performed, including 3DOF (translation only), 4DOF (translation and AP couch rotation) and 5DOF (translation and rotation on AP and LAT). Registration errors are calculated based on absolute or percentage of missed volume between estimation tumor location and real tumor location. Results: Patient setup errors were investigated using different registration methods under various tumor motion conditions. The results showed that back and forth shifts using alternating projection planes can worsen the true registration of the target center with each “correction” step given target rotation. Second, for off center rotations of five degrees, no alignment can result in smaller errors than 2D‐2D registrations, despite a true translation of the target center. In addition, a 3DOF alignment process performs better against rotational shifts than both 4DOF and 5DOF that allow rotations within the projection planes. Conclusion: The quantitative analysis and setup error estimation using “2D‐2D” registration will provide better guidance for patient setup, which is important for effective radiation treatment of cancer patients.
35(2008); http://dx.doi.org/10.1118/1.2962850View Description Hide Description
Purpose: Daily setup for head and neck (HN) radiotherapy (RT) can vary randomly due partially to neck rotation. This variation along with anatomy change can not be totally corrected by the current rigid translation. A full‐scope re‐optimization based on the daily CT takes too long to be practical with the current planning techniques. A novel rapid correction scheme that can be used online to correct for both inter‐fractional setup variation and anatomy change for HN RT is presented. Method and Materials: The scheme consists of two major steps after transferring planning contours to the CT of the day by means of deformable registration (MIMVISTA): (1) beam/segment apertures morphing (SAM) based on differences between planning contours and new contours, and (2) segment weight optimization (SWO) for the new apertures. SAM is accomplished by applying the spatial relationship between the planning target contour and the apertures to the new target contour. Dose distribution for each new aperture was generated using a planning system with a fast dose engine and hardware (Prowess), and was input into a newly‐developed SWO package using a fast sequential quadratic programming. The entire scheme was tested based on the daily kVCT images acquired for representative HN IMRT cases treated with a linac and CT‐on‐Rails combo (CTVision, Siemens). Results: The target coverage and/or OAR sparing degradation, arising from the current standard repositioning from rigid registration with the CT of the day, can be corrected by the present SAM/SWO scheme. The target coverage and OAR sparing for the SAM/SWO plans were found to be equivalent to the original plan. The SAM/SWO process took 5–8 min for the head and neck cases studied. Conclusion: The proposed aperture morphing with weight optimization is an effective approach for correcting inter‐fractional patient setup and anatomic changes for head and neck cancerradiotherapy.
TH‐C‐AUD C‐05: Assessment of Intrafractional Motion for Spinal Radiosurgery Patients by Multiple Onboard Megavoltage CT Scans During Treatment35(2008); http://dx.doi.org/10.1118/1.2962851View Description Hide Description
Purpose: To assess the intrafractional patient motion during single‐fraction spinal radiosurgery using multiple CT scans acquired with onboard megavoltage CT (MVCT) during treatment. Method and Materials: Nineteen patients underwent single‐fraction (12 – 24 Gy) spinal radiosurgery on a TomoTherapy HiArt radiotherapy unit with onboard MVCT. Because of the large fractional dose, the treatment was divided into 3 or more sub‐fractions. Patients were immobilized with BlueBAG™ BodyFIX® except for C‐spine patients who were immobilized with face‐and‐shoulder masks. To accurately align the patient, an initial MVCT was acquired after patient was setup to skin marks. Image registration between MVCT and planning CT was performed using an auto‐registration algorithm followed by manual fine tuning to calculate the couch shifts. Immediately after the alignment, a verification MVCT scan was acquired. The couch was further shifted according to verification CT before the first sub‐fraction was delivered. Subsequently, MVCT was reacquired and the patient realigned after each sub‐fraction. A total of 67 MVCT‐guided intrafrational alignments (excluding initial alignments) were evaluated for the 19 patients. Couch shifts were analyzed to obtain the information of patient intrafractional movements. Results: The mean ± standard deviation for the magnitude (and displacement which is expressed in parentheses) of the shift was 1.0±0.9 mm (0.16±1.31 mm) in the lateral direction, 1.2±1.1 mm (0.31±1.62 mm) in the anteroposterior direction, 1.6±1.2 mm (0.44±1.97 mm) in the superoinferior direction, and 2.5±1.4 for the resultant vector. The respective maximum shifts were 5.0 mm, 5.4 mm, 5.7 mm and 5.9 mm. Majority (73%) of the realignments required shifts less than 3.0 mm. Image resolution and registration errors may have contributed to the observed intrafractional motions. Conclusion: Patient intrafractional motion should be considered in treatment planning for spinal radiosurgery patients. More intensive intrafractional monitoring and better immobilization device are necessary to reduce intrafrational patient motion.
TH‐C‐AUD C‐06: Nearly Real‐Time Tumor‐Position Monitoring During Arc Therapy with Combined MV and KV Imaging35(2008); http://dx.doi.org/10.1118/1.2962852View Description Hide Description
Purpose: To examine the feasibility and accuracy of using treatment MV beam and on‐board kV imaging for monitoring of the positions of implanted fiducials during arc therapy. Method and Materials: A Varian Trilogy LINAC with onboard kV imager was used for the study. A phantom with 13 ball bearings at known locations was used to calibrate the hybrid MV/kV imaging system to determine the spatial transformation matrix from the pixel coordinates to the radiation‐source‐centered coordinates. The feasibility and accuracy of the fiducial tracking system was examined using a 4D motion phantom capable of moving in accordance with a pre‐programmed trajectory. During an arc delivery, MV images acquired by the EPID and cine kV images were obtained simultaneously using a two‐channel frame‐grabber for real‐time analysis. A fast fiducial detection algorithm was developed to extract fiducial coordinates. Tracking results are compared with the preset trajectories. The accuracy of the tracking system was evaluated for a number of fiducial motion trajectories and for a variety of kV beam sampling rates ranging 15fps to 0.5fps. Results: The studies showed that it is feasible to use treatment MV beam and the orthogonal kV beam to monitor the fiducial motion in nearly real‐time during arc therapy. A time delay of ∼150ms was observed, which was caused by the imaging electronics and data analysis. This delay does not pose any significant error in fiducial tracking for the motion speed commonly seen in the clinics and can be compensated by a motion prediction algorithm when needed. Overall, the spatial accuracy was found to be better than 1mm. Conclusion: Nearly real‐time monitoring of implanted markers using hybrid MV/kV imaging during arc treatments is achievable. The system requires no hardware modification and delivers much less dose to the patient as compared with the conventional stereoscopic imaging technique.
35(2008); http://dx.doi.org/10.1118/1.2962853View Description Hide Description
Purpose: Several groups have investigated monitoring respiratory tumor motion during a radiotherapy treatment session by fluoroscopically tracking implanted markers. However tracking requires prediction because there is a mechanical latency involved in either shutting the beam off or moving the position of the beam to adjust for the tracking results. Investigators have examined predicting respiratory tumor motion using various linear, non‐linear, and adaptive techniques. Here, we run a pilot study and try a new non‐linear regression method for prediction and compare it with linear prediction on three patients with respiratory tumors.Method and Materials: We examine two methods to predict the future location of the tumor, moving linear regression and moving support vector regression. By trial and error, we find that using 8 prior locations of the tumor is optimal for the linear model. The support vector regressor is non‐linear because we use a radial basis kernel function to expand the input space. Like the linear model, it also uses 8 prior locations to predict the future location.. The loss function is the ε‐insenstive. We test our models on data from 3 patients with respiratory tumors. The motion data was collected with Accuray's Synchrony system at 30Hz. Results: We predict the location of the tumor 1 second ahead. The root mean square error of no prediction, linear regression, and support vector regression respectively is 7.41 mm, 1.93 mm, and 1.47 mm. Conclusion: On this small set of patients, we appear to predict tumor motion further into the future than previously reported. Although this might be because of the small sample size, what remains significant either way is the fact that support vector regression out‐performed the linear method for predicting tumor location for each of the three patients.
TH‐C‐AUD C‐08: Moving Towards Real Time Adaptive Radiation Therapy: Integrating Patient Imaging, Plan Adaptation and Radiation Delivery35(2008); http://dx.doi.org/10.1118/1.2962854View Description Hide Description
Purpose: To propose a new approach to on‐line adaptive radiation therapy(ART) in which daily image acquisition, plan adaptation and radiationdelivery are performed concurrently. Method and Materials: Daily imaging was performed using an on‐board cone beam CTimaging system. X‐ray projections were continuously acquired as the gantry rotates between treatment positions. A filtered back‐projection algorithm was used to reconstruct 3D digital tomosynthesis (DTS) images from the limited angle x‐ray projection data. An edge detection algorithm was used to automatically segment the 3D DTS images as the gantry arrives at each treatment position. The treatment plan was then re‐optimized for the most recent DTS image contours using modified direct aperture optimization (DAO). To test our system, a model representing an average prostate case was generated. A treatment plan based on this original anatomy was created using our DAO system. To simulate inter‐fractional prostate deformations, three clinically relevant deformations (labeled: small, medium and large) were modeled by systematically deforming the original anatomy. The ability of our integrated approach to adapt the original treatment plan to account for the anatomy deformations was investigated. Results: The original treatment plan becomes clinically unacceptable for all three deformations, based on the dose‐volume constraints from the Radiotherapy and Oncology Group 0415 prostate protocol. Using our integrated approach to on‐line ART, the original treatment plan was successfully adapted to arrive at a clinically acceptable plan for all three anatomy deformations, with the treatment time drastically reduced compared to the current on‐line ART procedure. Conclusion: We have developed a new approach to on‐line ART in which image acquisition, plan adaptation and radiationdelivery are temporally integrated. We have shown that it can successfully adapt the original treatment plan for three clinically relevant prostate deformations while considerably reducing treatment time.
TH‐C‐AUD C‐09: Efficacy of Virtual Reality Simulation for Noncoplanar Prostate IMRT — a Peek of Future RTP System35(2008); http://dx.doi.org/10.1118/1.2962856View Description Hide Description
Purpose: To evaluate the efficacy of virtual reality simulation for noncoplanar prostate IMRT optimization process. Method and Materials: 14 prostate IMRT cases, randomly selected from current clinic, were re‐simulated using a pioneer virtual reality simulation (VRS) system for noncoplanar beam arrangement. Using 3D stereoscopic technology, the VRS system displays the virtual treatment environment on a DTI™ real 3D LCD screen without aid of gaggles. With the virtual patient loaded to the screen, one can simulate the treatment by realistically manipulating the gantry and table. An optimal beam projection is suggested when the inclusion of vital organ was reduced in the volumetric beam eye‐view. When exploring a noncoplanar beam position, the anti‐collision function effectively alerted the mechanical limitations. In comparing with original seven evenly‐spread coplanar beams, in the resultant new setting all beams were rotated more anteriorly, while two anterior oblique beams were tilted 20–30° inferiorly. All IMRT plans were calculated with similar modulated intensity level and number of segments. To standardize the endpoint for comparison, PTV D95 was normalized to 45 Gy. Results: VRS created non‐coplanar beam arrangements were proved clinically to be deliverable beam arrangement without risk of collision. Dose homogeneity in new plans were improved, indicated by 2.2% reduction in global maximal dose and 2.0% in high dose tail (D5) of PTV. Enhancement of rectal dose sparing was suggested by 5.7% and 3.7% lower values for rDmean and rD10cc respectively. Less inclusion of bladder in BEV of the two anterior noncoplanar oblique beams translated a 14% and 5.1% reduction in bladder mean dose and bD30cc respectively. Conclusion: The VRS effectively concluded deliverable noncoplanar beam arrangements for prostate IMRT with resultant improvement of dose characteristics. Further development of VRS may present future RTPS.
- Margin Assessment
WE‐E‐AUD C‐01: Prediction of Weight Loss, Tumor Response, and Set‐Up Errors For Head and Neck Patients35(2008); http://dx.doi.org/10.1118/1.2962782View Description Hide Description
Purpose: The objective of this study was to develop a novel tool using Kernel Classification that can be used to automatically identify patients that have, or will have, setup issues requiring intervention such as re‐simulation and/or re‐planning. Method and Materials: Inter‐Fraction motion was retrospectively analyzed for 43 H&N patients that were treated on a helical tomotherapy system. For each patient, CTimages were acquired and transferred to the tomotherapy database for treatment planning and image‐guided patient setup. Both custom aquaplastic masks and a positioning mouthpiece were used in 10 of the 43 patients. Results: Fifteen patients had greater than 10% weight loss during the course of treatment. Six patients had a visible reduction in GTV volume. Immobilization effectiveness decreased as the tumors regressed in size and/or the patients lost weight. If the tumor regression was occurred then time could be scheduled to periodically check the mask fit and to make a new mask if needed. The kernel classification technique correctly identified all 43 H&N patients as either having normal or problematic setup using their respective shift data sets. 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. However, adding more did little to improve the performance. Conclusion: This study demonstrated that the kernel regression classification method was able to correctly identify the cause behind IGRT positioning problems for H&N patients. The study validated that IGRT positioning problems cause abnormal problem‐specific distributions in the shift data without using statistical distribution tests. Since this technique is fully automated, it could potentially be used during IGRT sessions to help the therapists decipher the factors that hinder patient setup early in a patient's treatment so that the proper precautions can be in place.
35(2008); http://dx.doi.org/10.1118/1.2962783View Description Hide Description
Purpose: Different CT modalities with varying image quality are being used to correct for interfractional variations in patient setup and anatomy changes, thereby reducing CTV‐to‐PTV margins, for prostate radiotherapy (RT). We explore how CTimage quality affects patient repositioning and CTV‐to‐PTV margin. Method and Materials: Three CT‐based IGRT modalities routinely used in our institute for prostate RT are considered in this study: MV fan beam CT (Tomotherapy), MV cone beam CT (MVision, Siemens) and kV fan beam CT (CTVision, Siemens). Daily shifts are determined by manual registration to achieve the best soft tissue agreement. Effect of image quality on patient repositioning was determined by statistical analysis of daily shifts for 65 prostate cancer patients (34 Tomotherapy, 21 CTVision,10 MVision) treated in our clinics. The impact of soft tissue contrast on organ interface identification was evaluated by analyzing contours drawn by 7 users on the scans from each imaging modality. In addition, variability of soft tissue registration between 10 users was evaluated based on the registration of representative scan for each CT modality with its corresponding planning scan. CTV‐to‐PTV margin was defined as 1.96σ. Results: Inferior image quality with MV CT based IGRT leads to increased variations in daily shifts (3, 4, 5 mm for CTVision, Tomotherapy and MVision) and in prostate delineation (6, 3, 10 mm for CTVision, Tomotherapy and MVision). Superior image quality with the kV CT results in reduced variation between 10 users in soft tissue registration. Uniform margin introduced to account for the uncertainty in the identification of prostate edge are determined to be 2, 6 and 5 mm for CTVision, Tomotherapy and MVision. Conclusion:Image quality adversely affects the reproducibility of the manual registration for IGRT and necessitates a margin of 2 mm for kV CT and 6 mm for MV CT to ensure adequate coverage.
WE‐E‐AUD C‐03: Determining An Appropriate Margin Around CTV to Account for Interfraction Motion During IMRT for Cervical Cancer Patients Based On Daily Imaging35(2008); http://dx.doi.org/10.1118/1.2962784View Description Hide Description
Purpose: To derive an appropriate CTV‐to‐PTV margin that will ensure delivery of 100% of the prescribed dose to 95% (on average) of daily CTV's during a treatment course. Method and Materials: Daily CBCTimages are taken for setup of Stage I–IVa cervical cancer patients at our institution. Daily CTV contours drawn on these images take into account the effect of interfraction motion due to organ motion/deformation and tumor regression. To date, we have acquired CBCT data for 15 patients out of which 5 have been analyzed so far. On each CBCT, expert radiation oncologists manually segment CTV, bladder and rectum of that particular day. The CTV contours from all the CBCT scans of the same patient are superimposed on the planning CT scan after rigidly registering each CBCT scan to the planning CT scan. A uniform margin of 0, 3, 5, 7, 10, or 15 mm etc is added around the planning CTV and the average volume of CTV missed over all fractions is analyzed. The isotropic margin that encompasses 95% of daily CTVs on an average is considered adequate for the patient. Results:Statistical analysis of 5 patient's shows that a 7 mm margin is sufficient to cover on an average 98% (min: 95.2, max: 99.6) of daily CTVs of these 5 patients. However a bigger margin of 15 mm is required to adequately cover the high risk cervix region. The mean of the standard deviations in average volume of CTV covered for these 5 patients at 7 mm margin is 2.4 % indicating large inter‐patient variation. Analysis of more patients is required to reduce variation in average CTV volumes among patients. Conclusion: Clinical implementation of CBCTimaging devices has made possible margin evaluation based on daily images that provides a more accurate means of accounting for interfraction motion.
35(2008); http://dx.doi.org/10.1118/1.2962785View Description Hide Description
Purpose: The range of a proton beam in the patient is estimated using the relationship between CT numbers (HU) and relative linear stopping powers (RLSP). The purpose of this study is to assess the range uncertainties introduced by the size of the phantom used for determining the calibration curve. Method and Materials:CT scans were performed on tissue equivalent inserts placed at the center of two phantoms of different sizes representing human head and body with a GE Lightspeed 16‐slice CT scanner. The HU to RLSP calibration curve was established for each phantom based on a stoichiometric calibration technique. For each planning CTimage data set, two plans were generated using head and body calibration curves, respectively. The range difference between two corresponding fields was evaluated using distance‐to‐agreement (DTA) along the beam direction in a homogenous water phantom in water equivalent thickness (WET). The DTAs were calculated with a threshold of percent dose difference of 3%. Plans for four prostate and four lung patients were investigate. Results: The measured CT number for the same insert placed in two phantoms of different sizes could differ up to 200 HU for the bone equivalent materials. The maximum dose difference between two proton plans generated with head and body calibration curves could be up to 33%. The difference in DTA was up to 2.4 mm WET for prostate patients and 5.4 mm WET for lung patients. Conclusion: The calibration phantom size could contribute up to 5.4 mm WET range uncertainties for the lung patients in our study. The current practice is to use average calibration curve to minimize the uncertainty. Advanced imaging techniques such as dual‐energy CT scans could be utilized to reduce the uncertainty in CT numbers. Other factors that introduce range uncertainty in proton therapy will be investigated in future.
WE‐E‐AUD C‐05: Predictability of Patient Specific Prostate Margins From Real‐Time Intrafraction Motion Measurements35(2008); http://dx.doi.org/10.1118/1.2962786View Description Hide Description
Purpose: To investigate the ability to predict individualized PTV margins for prostate treatment based on limited real‐time intrafraction motion data. Method and Materials: Under IRB approved protocols, 35 patients with 3 transponders implanted in the prostate were studied. Transponders were placed at the apex, right‐ and left‐base under ultrasound guidance. Isocenter was chosen relative to the centroid of the transponders and the patients were initially positioned using the electromagnetic system. The relative position of the transponders was monitored continuously during each fraction, at 10 Hz. The probability distribution of absolute displacements from isocenter was found in each direction for the first fraction. Cumulative margins, Mn, were found using van Herk's formula after each fraction, including all fractions (n=N), to determine the best retrospective PTV margin for each individual patient. Metrics from the first fraction's probability distribution were tested for correlation (Pearson's r, Pr) with the final cumulative margin. These metrics included the position in each direction which was >= 50% and 95% (R50, R95) of absolute deviations from isocenter. The percentage of patients for which was found after n fractions. Results: The correlation coefficients in the Left‐Right, Anterior‐Posterior, and Superior‐Inferior directions were Pr50 = (0.275, 0.422, 0.177), Pr95 = (0.435, 0.489, 0.168). The percentage of patients within 1 mm of their final margin after n=2, 3, 5, 10, 20 days was LR = (94.3, 97.1, 97.1, 100.0, 100.0)%, AP = (40.0, 57.1, 74.3, 91.4, 97.1)%, and SI = (34.3, 37.1, 65.7, 91.4)%. Conclusion: R50 and R95 from a single fraction of measured intrafraction displacements are poorly correlated with a given patient's final individualized margins. In addition, 15 to 20 fractions are required to estimate margins within 1 mm for 90% of patients. Conflict of Interest: Supported by NIHP01CA59827 and Calypso Medical.
WE‐E‐AUD C‐06: 4D Imaging of Lung Cancer Patients Treated with Stereotactic Body Radiotherapy (SBRT): Assessment of Target Volumes35(2008); http://dx.doi.org/10.1118/1.2962787View Description Hide Description
Purpose: To compare target volumes assessed via 4D and free‐breathing CT scans for patients treated with peripheral lung lesions. Method and Materials: The target volumes of five lungcancer patients imaged using 4D‐CT and treated with hypo‐fractionated SBRT (12 Gy/Fxn × 4Fxn) were retrospectively analyzed. For each patient 6‐to‐8 CT datasets were acquired between inhale and exhale respiratory phases on a Philips 16 slice 4D‐CT scanner. The GTV was segmented on each dataset using a maximum‐intensity‐projection (MIP) method and an ITV (ITV4D), representing the composite of GTVs, was formed. The ITV4D was expanded uniformly 5mm to generate a PTV (PTV4D). The GTV was also contoured on the free‐breathing scan and expanded using population‐based margins of 5mm and 10mm in the axial and longitudinal planes, respectively, to form a free‐breathing‐based PTV (PTVFB), following RTOG ♯0236. Finally, a target volume defined as a composite of GTVs contoured on only the inhale and exhale datasets was generated to form the ITVInh_Exh. Results: For three of five patient images, PTV4D was substantially larger than PTVFB (average increase of 33%; max.=65%). In one case the volumes were equivalent and in the remaining case PTV4D was 11% smaller than PTVFB. Significant shape changes were also observed in some instances between PTV4D and PTVFB suggesting that PTVFB was improperly designed. The ITVInh_Exh was smaller than the ITV4D in all cases (mean=31%; max.= 78%, smaller) suggesting that the inhale and exhale breathing phases sometimes fail to capture the largest extents of tumor motion in the respiratory cycle. Conclusion: Results suggest that, based on 4D imaging, the use of population‐based margin expansions may not adequately account for tumor motion of peripheral lungtumors. This may be of increased consequence in the SBRT setting, where the overall effects of motion may be escalated given the small number of fractions.
WE‐E‐AUD C‐07: A Robust Approach for Estimating Tumor Volume Change During Radiotherapy of Lung Cancer35(2008); http://dx.doi.org/10.1118/1.2962788View Description Hide Description
Purpose:Radiotherapytreatment of lungcancer patients is complicated by changes in tumor position due to breathing and changes in size due to regression. Accurate quantification of these changes during the course of treatment would likely improve tumor response and reduce toxicity risks. We are investigating robust methods for tracking and estimating tumor volume changes between treatment fractions. Method and Materials: We have developed a registration—assisted segmentation approach based on the level‐set deformable model, in which pre‐treatment contours are propagated and adapted to fractions times at selected respiratory phases. At any time‐point during treatment, a reference respiratory phase is selected and corresponding 3D‐CT volumes are reconstructed from 4D‐CT acquisition data. Images are then globally aligned using an efficient registration algorithm. Pre‐treatment planning contours are copied to selected time‐points. In our tumor regression analysis, the GTV contour was used to initialize the level set algorithm in‐place and the PTV contour was used to narrowband the region, thus improving the algorithm's convergence. The feasibility of the proposed approach was investigated on a set of patients with repeated 4D scans acquired at three time‐intervals. Results: Our preliminary analysis indicates that the proposed approach can properly capture the boundary of the shrinking tumor region or split regions due to its topological adaptation ability. On an initial cohort of four NSCLC patients, the estimated tumor volume reductions ranged between 3–46% with a median of 8% by mid‐treatment and between 26–51% with a median of 34% by the end of treatment.Conclusion: We have demonstrated a new approach for tracking tumor regression during the course of radiotherapytreatment of NSCLC patients based on a novel level‐set segmentation algorithm. This approach provides us with a semi‐automated tool for quantifying tumor shrinkage and allows accurate estimates of ‘true’ accumulated dose to the tumor.
Supported by CA85181 grant.