M. Aristophanous

Clinical Utility of 4D FDG-PET/CT Scans in Radiation Treatment Planning

PURPOSE The potential role of four-dimensional (4D) positron emission tomography (PET)/computed tomography (CT) in radiation treatment planning, relative to standard three-dimensional (3D) PET/CT, was examined. METHODS AND MATERIALS Ten patients with non-small-cell lung cancer had sequential 3D and 4D [(18)F]fluorodeoxyglucose PET/CT scans in the treatment position prior to radiation therapy. The gross tumor volume and involved lymph nodes were contoured on the PET scan by use of three different techniques: manual contouring by an experienced radiation oncologist using a predetermined protocol; a technique with a constant threshold of standardized uptake value (SUV) greater than 2.5; and an automatic segmentation technique. For each technique, the tumor volume was defined on the 3D scan (VOL3D) and on the 4D scan (VOL4D) by combining the volume defined on each of the five breathing phases individually. The range of tumor motion and the location of each lesion were also recorded, and their influence on the differences observed between VOL3D and VOL4D was investigated. RESULTS We identified and analyzed 22 distinct lesions, including 9 primary tumors and 13 mediastinal lymph nodes. Mean VOL4D was larger than mean VOL3D with all three techniques, and the difference was statistically significant (p < 0.01). The range of tumor motion and the location of the tumor affected the magnitude of the difference. For one case, all three tumor definition techniques identified volume of moderate uptake of approximately 1 mL in the hilar region on the 4D scan (SUV maximum, 3.3) but not on the 3D scan (SUV maximum, 2.3). CONCLUSIONS In comparison to 3D PET, 4D PET may better define the full physiologic extent of moving tumors and improve radiation treatment planning for lung tumors. In addition, reduction of blurring from free-breathing images may reveal additional information regarding regional disease.

A multi-region algorithm for markerless beam's-eye view lung tumor tracking

Methods that allow online lung tumor tracking during radiotherapy are desirable for a variety of applications that have the potential to vastly improve treatment accuracy, dose conformity and sparing of healthy tissue. Several publications have proposed the use of an on-board kV x-ray imager to assess the tumor location during treatment. However, there is some concern that this strategy may expose the patient to a significant amount of additional dose over the course of a typical radiotherapy treatment. In this paper we present an algorithm that utilizes the on-board portal imager of the treatment machine to track lung tumors. This does not expose the patient to additional dose, but is somewhat more challenging as the quality of portal images is inferior when compared to kV x-ray images. To quantify the performance of the proposed algorithm we retrospectively applied it to portal image sequences retrieved from a dynamic chest phantom study and an SBRT treatment performed at our institution. The results were compared to manual tracking by an expert. For the phantom data the tracking error was found to be smaller than 1 mm and for the patient data smaller than 2 mm, which was in the same range as the uncertainty of the gold standard.

Use of a realistic breathing lung phantom to evaluate dose delivery errors

PURPOSE To compare the effect of respiration-induced motion on delivered dose (the interplay effect) for different treatment techniques under realistic clinical conditions. METHODS A flexible resin tumor model was created using rapid prototyping techniques based on a computed tomography (CT) image of an actual tumor. Twenty micro-MOSFETs were inserted into the tumor model and the tumor model was inserted into an anthropomorphic breathing phantom. Phantom motion was programed using the motion trajectory of an actual patient. A four-dimensional CT image was obtained and several treatment plans were created using different treatment techniques and planning systems: Conformal (Eclipse), step-and-shoot intensity-modulated radiation therapy (IMRT) (Pinnacle), step-and-shoot IMRT (XiO), dynamic IMRT (Eclipse), complex dynamic IMRT (Eclipse), hybrid IMRT [60% conformal, 40% dynamic IMRT (Eclipse)], volume-modulated arc therapy (VMAT) [single-arc (Eclipse)], VMAT [double-arc (Eclipse)], and complex VMAT (Eclipse). The complex plans were created by artificially pushing the optimizer to give complex multileaf collimator sequences. Each IMRT field was irradiated five times and each VMAT field was irradiated ten times, with each irradiation starting at a random point in the respiratory cycle. The effect of fractionation was calculated by randomly summing the measured doses. The maximum deviation for each measurement point per fraction and the probability that 95% of the model tumor had dose deviations less than 2% and 5% were calculated as a function of the number of fractions. Tumor control probabilities for each treatment plan were calculated and compared. RESULTS After five fractions, measured dose deviations were less than 2% for more than 95% of measurement points within the tumor model for all plans, except the complex dynamic IMRT, step-and-shoot IMRT (XiO), complex VMAT, and single-arc VMAT plans. Reducing the dose rate of the complex IMRT plans from 600 to 200 MU/min reduced the dose deviations to less than 2%. Dose deviations were less than 5% after five fractions for all plans, except the complex single-arc VMAT plan. CONCLUSIONS Rapid prototyping techniques can be used to create realistic tumor models. For most treatment techniques, the dose deviations averaged out after several fractions. Treatments with unusually complicated multileaf collimator sequences had larger dose deviations. For IMRT treat-ments, dose deviations can be reduced by reducing the dose rate. For VMAT treatments, using two arcs instead of one is effective for reducing dose deviations.

The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques.

Methods for accurate tumor volume segmentation of positron emission tomography (PET) images have been under investigation in recent years partly as a result of the increased use of PET in radiation treatment planning (RTP). None of the developed automated or semiautomated segmentation methods, however, has been shown reliable enough to be regarded as the standard. One reason for this is that there is no source of well characterized and reliable test data for evaluating such techniques. The authors have constructed a digital tumor phantom to address this need. The phantom was created using the Zubal phantom as input to the SimSET software used for PET simulations. Synthetic tumors were placed in the lung of the Zubal phantom to provide the targets for segmentation. The authors concentrated on the lung, since much of the interest to include PET in RTP is for nonsmall cell lung cancer. Several tests were performed on the phantom to ensure its close resemblance to clinical PET scans. The authors measured statistical quantities to compare image intensity distributions from regions-of-interest (ROIs) placed in the liver, the lungs, and tumors in phantom and clinical reconstructions. Using ROIs they also made measurements of autocorrelation functions to ensure the image texture is similar in clinical and phantom data. The authors also compared the intensity profile and appearance of real and simulated uniform activity spheres within uniform background. These measurements, along with visual comparisons of the phantom with clinical scans, indicate that the simulated phantom mimics reality quite well. Finally, they investigate and quantify the relationship between the threshold required to segment a tumor and the inhomogeneity of the tumors image intensity distribution. The tests and various measurements performed in this study demonstrate how the phantom can offer a reliable way of testing and investigating tumor volume segmentation in PET.

Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer

Background PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. Methods Twenty-six patients (34 lesions) received 3D and 4D [18F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ3D-4D) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ3D-4D were also assessed. Results 4D-PET increased LRLG ( = 1%–2%, p<0.02), Busyness ( = 7%–19%, p<0.01), and decreased MCC ( = 1%–2%, p<7.5×10−3), Coarseness ( = 5%–10%, p<0.05) and Contrast ( = 4%–6%, p>0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ3D-4D for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast. Conclusions Significant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion.

Evaluating FDG uptake changes between pre and post therapy respiratory gated PET scans

PURPOSE Whole body (3D) and respiratory gated (4D) FDG-PET/CT scans performed pre-radiotherapy (pre-RT) and post-radiotherapy (post-RT) were analyzed to investigate the impact of 4D PET in evaluating 18F-fluorodeoxyglucose (FDG) uptake changes due to therapy, relative to traditional 3D PET. METHODS AND MATERIALS 3D and 4D sequential FDG-PET/CT scans were acquired pre-RT and approximately one month post-RT for patients with non-small cell lung cancer (NSCLC). The lesions of high uptake targeted with radiotherapy were identified on the pre-RT scan of each patient. Each lesion on the 3D and each of the five phases of the 4D scan were analyzed using a region of interest (ROI). For each patient the ROIs of the pre-RT scans were used to locate the areas of initial FDG uptake on the post-RT scans following rigid registration. Post-RT ROIs were drawn and the FDG uptake was compared with that of the pre-RT scans. RESULTS Sixteen distinct lesions from 12 patients were identified and analyzed. Standardized uptake value (SUV) maxima were significantly higher (p-value <0.005) for the lesions as measured on the 4D compared to 3D PET. Comparison of serial pre and post-RT scans showed a mean 62% decrease in SUV with the 3D PET scan (range 36-89%), and a 67% decrease with the 4D PET scan (range 30-89%). The mean absolute difference in SUV change on 3D versus 4D scans was 4.9%, with a range 0-15% (p-value = 0.07). CONCLUSIONS Signal recovery with 4D PET results in higher SUVs when compared to standard 3D PET. Consequently, differences in the evaluation of SUV changes between pre and post-RT plans were observed. Such difference can have a significant impact in PET-based response assessment.

EPID-guided 3D dose verification of lung SBRT

PURPOSE To investigate the feasibility of utilizing tumor tracks from electronic portal imaging device (EPID) images taken during treatment to verify the delivered dose. METHODS The proposed method is based on a computation of the delivered fluence by utilizing the planned fluence and the tumor motion track for each field. A phantom study was designed to assess the feasibility of the method. The CIRS dynamic thorax phantom was utilized with a realistic soft resin tumor, modeled after a real patient tumor. The dose calculated with the proposed method was compared to direct measurements taken with 15 metal oxide semiconductor field effect transistors (MOSFETs) inserted in small fissures made in the tumor model. The phantom was irradiated with the tumor static and moved with different range of motions and setup errors. EPID images were recorded throughout all deliveries and the tumor model was tracked post-treatment with in-house developed software. The planned fluence for each field was convolved with the tumor motion tracks to obtain the delivered fluence. Utilizing the delivered fluence from each field, the delivered dose was calculated. The estimated delivered dose was compared to the dose directly measured with the MOSFETs. The feasibility of the proposed method was also demonstrated on a real lung cancer patient, treated with stereotactic body radiotherapy. RESULTS The calculation of delivered dose with the delivered fluence method was in good agreement with the MOSFET measurements, with average differences ranging from 0.8% to 8.3% depending on the proximity of a dose gradient. For the patient treatment, the planned and delivered dose volume histograms were compared and verified the overall good coverage of the target volume. CONCLUSIONS The delivered fluence method was applied successfully on phantom and clinical data and its accuracy was evaluated. Verifying each treatment fraction may enable correction strategies that can be applied during the course of treatment to ensure the desired dose coverage.

3-D fiducial motion tracking using limited MV projections in arc therapy

PURPOSE In-treatment fiducial tracking has recently received attention as a method for improving treatment accuracy, dose conformity, and sparing of healthy tissue. 3-D fiducial localization in arc-radiotherapy remains challenging due to the motion of targets and the complexity of arc deliveries. We propose a novel statistical method for estimating 3-D fiducial motion using limited 2-D megavoltage (MV) projections. METHODS 3-D fiducial motion was estimated by a maximum a posteriori (MAP) approach to integrating information of fiducial projections with prior knowledge of target motion. To obtain the imaging geometries, short sequences of MV projections were selected in which fiducials were continuously visible. The MAP algorithm estimated the 3-D motion by maximizing the probability of displacement of fiducials in the sequences. Prior knowledge of target motion from a large statistical sample was built into the model to enhance the accuracy of estimation. In the case that a motion prior was unavailable, the algorithm can be simplified to the maximum likelihood (ML) approach. To compare tracking performance, a multiprojection geometric method was also presented by extending the typical two-project ion geometric estimation approach. The algorithms were evaluated using clinical prostate motion traces, and the performance was measured in quality indices and statistical evaluation. RESULTS The results showed that the MAP method significantly outperforms the geometric method in all measures. In our simulations, the MAP method achieved an accuracy of less than 1 mm RMS error using only five continuous projections, whereas the geometric method required 15 projections to achieve a similar result. CONCLUSIONS The approach presented can accurately estimate tumor motion using a limited number of continuous projections. The MAP motion estimation is superior to both the ML and geometric estimation methods.

Initial clinical experience with ArcCHECK for IMRT/VMAT QA

Many devices designed for the purpose of performing patient‐specific IMRT/VMAT QA are commercially available. In this work we report our experience and initial clinical results with the ArcCHECK. The ArcCHECK consists of a cylindrical array of diode detectors measuring entry and exit doses. The measured result is a cumulative dose displayed as a 2D matrix. The detector array requires both an absolute dose calibration, and a calibration of the detector response, relative to each other. In addition to the calibrations suggested by the manufacturer, various tests were performed in order to assess its stability and performance prior to clinical introduction. Tests of uniformity, linearity, and repetition rate dependence of the detector response were conducted and described in this work. Following initial testing, the ArcCHECK device was introduced in the clinic for routine patient‐specific IMRT QA. The clinical results from one year of use were collected and analyzed. The gamma pass rates at the 3%/3 mm criterion were reported for 3,116 cases that included both IMRT and VMAT treatment plans delivered on 18 linear accelerators. The gamma pass rates were categorized based on the treatment site, treatment technique, type of MLCs, operator, ArcCHECK device, and LINAC model. We recorded the percent of failures at the clinically acceptable threshold of 90%. In addition, we calculated the threshold that encompasses two standard deviations (2 SD) (95%) of QAs (T95) for each category investigated. The commissioning measurements demonstrated that the device performed as expected. The uniformity of the detector response to a constant field arc delivery showed a 1% standard deviation from the mean. The variation in dose with changing repetition rate was within 1 cGy of the mean, while the measured dose showed a linear relation with delivered MUs. Our initial patient QA results showed that, at the clinically selected passing criterion, 4.5% of cases failed. On average T95 was 91%, ranging from 73% for gynecological sites to 96.5% for central nervous system sites. There are statistically significant differences in passing rates between IMRT and VMAT, high‐definition (HD) and non‐HD MLCs, and different LINAC models (p‐values <<0.001). An additional investigation into the failing QAs and a comparison with ion‐chamber measurements reveals that the differences observed in the passing rates between the different studied factors can be largely explained by the field size dependence of the device. Based on our initial experience with the ArcCHECK, our passing rates are, on average, consistent with values reported in the AAPM TG‐119. However, the significant variations between QAs that were observed based on the size of the treatment fields may need to be corrected to improve the specificity and sensitivity of the device. PACS number(s): 87.55.Qr, 87.56.Fc

SU‐DD‐A4‐05: Investigation of a 3D Gradient Based Method for Volume Segmentation in Positron Emission Tomography for Radiation Treatment Planning

Purpose: Over the past few years Positron Emission Tomography(PET) is being used to supplement radiation treatment planning by assisting physicians in the determination of gross tumor volume. A number of studies have also been investigating the new possibilities that PET offers, such as guiding the delivery of a non‐uniform dose in the tumor volume. Defining the surface of the tumor is an essential step in these applications; hence we investigated the use of a 3D gradient based technique for tumor volume segmentation. Method and Materials: We applied the gradient based technique to FDG‐PET images obtained with a PET/CT scan of NEMA 2001 body phantom, with three spheres of different diameters whose FDG concentration was varied against an essentially constant surrounding activity. We measured the volume of the spheres gravimetrically, calculated them from the CT scan and we also estimated them from the PETimages using three segmentation methods: 1) simple intensity thresholding on the raw PET data, 2) intensity thresholding on the background subtracted PET data and 3) a previously described 3D gradient based method. The obtained volumes were compared with the CT volumes. Results: The percentage error on volume estimation from the gradient based method varied from 15–38%. It had precision and accuracy similar to the background subtracted thresholding over the whole range of volumes and backgrounds, while it was less accurate than simple thresholding for low background activities, but worked better in regions of high background activity where the simple thresholding fails. Conclusion: We have successfully applied a gradient based volume segmentation technique to a set of phantom images. This method does not require any a priori information, only involves minimal user interaction and most importantly does not depend directly on image intensities. The results are promising, but further work is needed to validate its clinical use.