Proceed ings o f the 40 th A n n u a l A S T R O Meet ing

65 POR I'AI IMAGE VERIF[CA IION OF INTENSITY MODULATED RADIATION THERAPY

157

, • + Chang, Jenghwa, Ph.D,,* Mageras, Gikas S., Ph.D.,* Chui, Chen S., Ph.D., Wu, Qluwen, Ph.D., Lutz, Wendell, Ph.D.*

* Memorial Sloan Kettering Cancer Center, New York, NY, +Medical College of Virginia, Richmond, VA

Purpose: Five-field intensity modulated (IM) plans using multi-leaf collimators are currently used in the treatment of prostate cancer at our institution. Verification of 1M fluence poses new challenges for quality assurance (QA). The purpose of this study is to develop a computer-based QA procedure to assess the delivered relative intensity using electronic portal imaging devices (EPID) or films. Initial studies were done using films. Materials & Methods: In our IM system, each pair of leaves moves across the field with variable speed and width according to a planned (or desired) intensity profile. The measured intensity profile (portal image) should reflect the planned profile i f ( l ) a gap larger than the field size exists between patient and imager facilitating a uniform scatter correction and (2) ray paths through patient for a given field are approximately equal. For pixel(L an error term is defined by E~j = Mo - (k x D~ + s), where Mj is the measured intensity, D~ is the planned intensity, and s is the scatter correction, and k is the normalization. In the above relation, s and k can be found from an optimization process, and the distribution of {Eo} reflects how well the measured profile then matches the desired profile. A linear regression method is used to find the optimizeds and k, and the goodness o f match is

indicated by both the standard deviation, ~ and the average of absolute values, IEJ of {E~}, Different regions of interest, e.g., main beam, penumbra, region between jaws and aperture can be given different weightings. To verify this approach to 1M QA, treatment fields from several prostate patients were used to irradiate a series of experimental setups to simulate different treatment conditions. Integrated exit fluence was measured using verification films with a 3mm thick Cu screen. Mismatches were also introduced in both desired and measured profiles to further test the sensitivity of this method. Examples are: displacing the isocenter of the measured profiles, modifying planned profiles to simulate frozen leaves, changing the jaw settings, and attenuating part of the beam to change the intensity fluence. Planned profiles were calculated using a two-dimensional convolution of x-ray (primary and scatter) sources and a pencil beam kernel, considering the effects of leaf motion, leaf edge transmission, and distance differences between the leaves and jaws. The measured profiles and planned profiles were then compared to obtain the scores using the above method. Integrated exit fluence can be measured with EPID's by summing a series of "snapshot" images taken at regular time intervals, e.g., 1 per second throughout a treatment. Results: As expected, the best match was observed when film was irradiated " in-air" without a phantom, rr, expressed as a % of average desired intensity, varied from 1% to 3% for these cases. An intervening phantom, simulating the presence of a patient, generated ~r values in the range 2 - 4%. These latter values provided a baseline to assess the sensitivity of mismatches, cr increased to 6% when the isocenter was displaced by 3ram, and to 9% for a 5ram displacement. Introducing a "frozen" leaf into the intensity profile increased cr to 34%. cr was 21% when upper jaw settings (Yz=3-8, y2=3.0,) were switched, and 20% when one of the lower jaws clipped i cm of the aperture. When the intensity of one quarter o f the IM field area was reduced by 20%, cr increased to 8%. Finally, when the measured profile from one field was matched against a planned profile for another

field, cr was 40%. Values IEI follow a similar trend as those of ~r. Conclusion: It is important to have an independent method of assessing the consistency between the planned and delivered fluence pattern for IM treatments. The described method uses conventional film portal image as an independent source for comparison and demonstrates good sensitivity to a variety of mismatches. Our expectation is that this method can be applied to EPIDs facilitating real-time IM QA. With EPIDs, the potential also exists for real-time absolute dosimetry.

66 S I M U L A T A N E O U S S E G M E N T A T I O N A N D

R E G I S T R A T I O N O F 2 D P O R T A L A N D 3 D C T I M A G E S F O R T R E A T M E N T S E T U P V E R I F I C A T I O N IN R A D I O T H E R A P Y

RAVI BANSAL, M.S., t LAWRENCE STAIB, PH.D., t ZHE CHEN, PH.D., ± ANAND RANGARAJAN, PH.D., t

JONATHAN KNISLEY, PH.D., ± RAVINDER NATH, PH.D. + and JAMES DUNCAN, PH.D. t

$Department of Electrical Engineering, tDepartment of Diagnostic Radiology and

±Department of Therapeutic Radiology, Yale University, New Haven, CT 06520

P u r p o s e : In CT-based external beam radiotherapy, the treatment plan is designed using three-dimensional CT images of a patient. To verify the setup of such a treatment, the 2D portal images should be registered directly to the 3D CT image set. The aim of this work is to develop an accurate, robust, and automated method to register the patient's 3D pre-treatment CT image set to the 2D treatment portal images for the purpose of treatment setup verification. Methods and Materials: For accurate and robust verification of the three dimensional patient setup from a 3D treatment plan, we developed a novel framework to simultaneously segment and register 2D radiotherapy portal images to 3D treatment planning CT images. The proposed algorithm, termed the minimax entropy algorithm, evaluates appropriate entropies to automatically segment the portal image and to find the registration parameters iteratively. The accuracy and robustness of the proposed algorithm is assessed using actually portal images and digitally reconstructed portal images (DRPIs) from the CT image set, with DRPIs rendered at a defined set of transformation parameters. Increasing amount of Gaussian noise was added to DRPIs to study the robustness of the proposed algorithm. Results: In all the test cases of registering DRPIs to the 3D CT image sets, the in-plane translation error was found to be within 0.8 ram. The in- plane rotation error was within 0.5 ° of the true rotation for added Gaussian noise of standard deviation (c 0 up to 20. The error in the estimated out-of-plane rotation angles was within 0.5 ° for added noise of ~ up to 10. As the noise added to the DRPIs increases, for cr more than 20, the out- of-plane rotation estimates, using only the AP portal image for the registration, became relatively poor. The maximum pixel intensity of DRPIs was 296. The accuracy of the registration parameters, estimated by the proposed algorithm, for the real portal images is presented by visual comparison with the digitally reconstructed radiographs (DRRs) at the estimated parameters. To aid in visual comparison, contours matching anatomical structures on the portal images were drawn and were subsequently mapped, without any distortion, onto the DRRs. C o n c l u s i o n : The proposed algorithm can accurately quantify the patient setup errors in 3D by registering a 2D radiotherapy portal image to the 3D treatment planning CT image set. For robustness in estimated 3D patient parameters, especially the out-of-plane parameters, we propose using orthogonal, anterior-posterior and lateral, portal images, taken during radiotherapy.

Registration, Radiotherapy PortaI.Verificiation~ 3D CT lma8e, Segmentation, Radiation Therapy Treatment Planning, Information Theory.