Akihiro Nakata

Assessment of Similarity Measures for Accurate Deformable Image Registration

Purpose: Deformable image registration is widely used in radiation therapy applications. There are several different algorithms for deformable image registration. The purpose of this study was to evaluate the optimal similarity measures needed to obtain accurate deformable image registration by using a phantom. Methods: To evaluate the optimal similarity measures for the deformable image registration, we compared several similarity measures, including the normalized correlation coefficient, the mutual information, the dice similarity coefficient, and the Tanimoto coefficient. In this study, the mutual information was normalized to have a value of 1 when there is complete correspondence between the images in order to compare it with other similarity measures. First, a reference image was acquired with the phantom located in the center of the field of view of a computed tomography. The phantom consisted of two sections a Teflon sphere and four samples of various electron density values. Then, to acquire the moving images, the phantom was scanned for various displacement values as it was moved to the left (range: 1.00-30.0 mm). Second, images for various Teflon sphere diameters (range: 0–25.4 mm) were acquired with the CT scanner. The image similarity for each condition was compared with the reference image by using several similarity measures. Results: In the moved phantom study, although the normalized correlation coefficient, dice similarity coefficient, and Tanimoto coefficient showed the same tendency of sensitivity for measuring image similarity, the mutual information showed significant sensitivity for both of the two distinct sections of the phantom. In the study in which the phantom sphere diameter was varied, the mutual information also showed the best performance among the tested similarity measures. Conclusions: Mutual information appears to have an advantage over other similarity measures for accurate deformable image registration.

Uncertainty in patient set-up margin analysis in radiation therapy

We investigated the uncertainty in patient set-up margin analysis with a small dataset consisting of a limited number of clinical cases over a short time period, and propose a method for determining the optimum set-up margin. Patient set-up errors from 555 registration images of 15 patients with prostate cancer were tested for normality using a quantile-quantile (Q-Q) plot and a Kolmogorov–Smirnov test with the hypothesis that the data were not normally distributed. The ranges of set-up errors include the set-up errors within the 95% interval of the entire patient data histogram, and their equivalent normal distributions were compared. The patient set-up error was not normally distributed. When the patient set-up error distribution was assumed to have a normal distribution, an underestimate of the actual set-up error occurred in some patients but an overestimate occurred in others. When using a limited dataset for patient set-up errors, which consists of only a small number of the cases over a short period of time in a clinical practice, the 2.5% and 97.5% intervals of the actual patient data histogram from the percentile method should be used for estimating the set-up margin. Since set-up error data is usually not normally distributed, these intervals should provide a more accurate estimate of set-up margin. In this way, the uncertainty in patient set-up margin analysis in radiation therapy can be reduced.

Analysis of the optimum internal margin for respiratory-gated radiotherapy using end-exhalation phase assessments using a motion phantom

We aimed to optimize internal margin (IM) determination for respiratory‐gated radiotherapy using end‐expiratory phase assessments using a motion phantom. Four‐dimensional computed tomography (4D CT) data were acquired using a GE LightSpeed RT CT scanner, a respiratory‐gating system, and a motion phantom designed to move sinusoidally. To analyze the accuracy of 4D CT temporal resolution, a 25.4 mm diameter sphere was inserted into the motion phantom, and we measured the differences in sphere diameters between static and end‐exhalation phase images. In addition, the IM obtained from the maximum intensity projection within the gating window (MIPGW) image was compared to theoretical value. Cranial–caudal motion displacement ranged from 5.0 to 30.0 mm, and the respiratory period ranged from 2.0 to 6.0 sec. Differences in sphere diameters between static and end‐exhalation phase images ranged from 0.37 to 4.6 mm, with 5.0 ‐mm and 30 mm target displacements, respectively. Differences between the IM obtained from the MIPGW and the theoretical values ranged from 1.12 to 6.23 mm with 5.0 mm and 30 mm target displacements, respectively. These differences increased in proportion to the target velocity due to a motion artifact generated during tube rotation. In this study, the IMs obtained using the MIPGW image were overestimated in all cases. We therefore propose that the internal target volume (ITV) for respiratory‐gated radiotherapy should be determined by adding the calculated value to the end‐exhalation phase image. We also demonstrate a methodology for subtracting motion artifacts from the ITV using a motion phantom. PACS numbers: 87.53.Kn, 87.55.Gh, 87.56.jk

Analysis of Prostate Deformation during a Course of Radiation Therapy for Prostate Cancer.

Purpose Accurate analysis of the correlation between deformation of the prostate and displacement of its center of gravity (CoG) is important for efficient radiation therapy for prostate cancer. In this study, we addressed this problem by introducing a new analysis approach. Method A planning computed tomography (CT) scan and 7 repeat cone-beam CT scans during the course of treatment were obtained for 19 prostate cancer patients who underwent three-dimensional conformal radiation therapy. A single observer contoured the prostate gland only. To evaluate the local deformation of the prostate, it was divided into 12 manually defined segments. Prostate deformation was calculated using in-house developed software. The correlation between the displacement of the CoG and the local deformation of the prostate was evaluated using multiple regression analysis. Results The mean value and standard deviation (SD) of the prostate deformation were 0.6 mm and 1.7 mm, respectively. For the majority of the patients, the local SD of the deformation was slightly lager in the superior and inferior segments. Multiple regression analysis revealed that the anterior-posterior displacement of the CoG of the prostate had a highly significant correlation with the deformations in the middle-anterior (p < 0.01) and middle-posterior (p < 0.01) segments of the prostate surface (R2 = 0.84). However, there was no significant correlation between the displacement of the CoG and the deformation of the prostate surface in other segments. Conclusion Anterior-posterior displacement of the CoG of the prostate is highly correlated with deformation in its middle-anterior and posterior segments. In the radiation therapy for prostate cancer, it is necessary to optimize the internal margin for every position of the prostate measured using image-guided radiation therapy.

Evaluation of Non-Rigid Image-Registration Algorithms Using DiscrepancyDistance Between Organ Contours

Purpose: Non-rigid image registration (NIR) is useful for adaptive radiotherapy. However, no method has been established for evaluating the quality of the algorithms used in NIR. To remedy this situation, we demonstrate herein a novel method to evaluate NIR algorithms. Methods: We define the NIR error as the discrepancy distance between (i) the organ contours obtained from computed tomography (CT) images acquired during the treatment period (reference contours) and (ii) the contours obtained from the treatment-planning CT images that are constructed by automated propagation during the treatment period (evaluation contours). However, the continuous positional relationship between the points where the reference contour intersects the evaluation contour is assumed to be maintained. In addition, we adapt the proposed method so that it can be applied to the contours of complex organs such as spherical and tubular organs. To demonstrate this method, we measure the contours of the prostate, right seminal vesicle, left seminal vesicle, urinary bladder, and rectum. The obtained NIR error presented in two-dimensional (2D) discrepancy maps. Results: The 2D discrepancy maps show the difference between the reference and evaluation contours from CT images. The proposed method measures the difference between the contours of spherical and tubular organs and evaluates the NIR error based on the positional relationship between the points constituting the contours. Conclusions: This study accounts for and measures the continuous positional relationship between corresponding points in the contours of complex-shaped spherical and tubular organs with irregularities and evaluates NIR algorithms based on these organ contours.

SU-E-T-161: Evaluation of Dose Calculation Based On Cone-Beam CT

PURPOSE The purpose of this study is to convert pixel values in cone-beam CT (CBCT) using histograms of pixel values in the simulation CT (sim-CT) and the CBCT images and to evaluate the accuracy of dose calculation based on the CBCT. METHODS The sim-CT and CBCT images immediately before the treatment of 10 prostate cancer patients were acquired. Because of insufficient calibration of the pixel values in the CBCT, it is difficult to be directly used for dose calculation. The pixel values in the CBCT images were converted using an in-house program. A 7 fields treatment plans (original plan) created on the sim-CT images were applied to the CBCT images and the dose distributions were re-calculated with same monitor units (MUs). These prescription doses were compared with those of original plans. RESULTS In the results of the pixel values conversion in the CBCT images,the mean differences of pixel values for the prostate,subcutaneous adipose, muscle and right-femur were -10.78±34.60, 11.78±41.06, 29.49±36.99 and 0.14±31.15 respectively. In the results of the calculated doses, the mean differences of prescription doses for 7 fields were 4.13±0.95%, 0.34±0.86%, -0.05±0.55%, 1.35±0.98%, 1.77±0.56%, 0.89±0.69% and 1.69±0.71% respectively and as a whole, the difference of prescription dose was 1.54±0.4%. CONCLUSION The dose calculation on the CBCT images achieve an accuracy of <2% by using this pixel values conversion program. This may enable implementation of efficient adaptive radiotherapy.

SU-E-T-325: The New Evaluation Method of the VMAT Plan Delivery Using Varian DynaLog Files and Modulation Complexity Score (MCS)

PURPOSE The aim of the study is to evaluate the use of Varian DynaLog files to verify VMAT plans delivery and modulation complexity score (MCS) of VMAT. METHODS Delivery accuracy of machine performance was quantified by multileaf collimator (MLC) position errors, gantry angle errors and fluence delivery accuracy for volumetric modulated arc therapy (VMAT). The relationship between machine performance and plan complexity were also investigated using the modulation complexity score (MCS). Plan and Actual MLC positions, gantry angles and delivered fraction of monitor units were extracted from Varian DynaLog files. These factors were taken from the record and verify system of MLC control file. Planned and delivered beam data were compared to determine leaf position errors and gantry angle errors. Analysis was also performed on planned and actual fluence maps reconstructed from those of the DynaLog files. This analysis was performed for all treatment fractions of 5 prostate VMAT plans. The analysis of DynaLog files have been carried out by in-house programming in Visual C++. RESULTS The root mean square of leaf position and gantry angle errors were about 0.12 and 0.15, respectively. The Gamma of planned and actual fluence maps at 3%/3 mm criterion was about 99.21. The gamma of the leaf position errors were not directly related to plan complexity as determined by the MCS. Therefore, the gamma of the gantry angle errors were directly related to plan complexity as determined by the MCS. CONCLUSION This study shows Varian dynalog files for VMAT plan can be diagnosed delivery errors not possible with phantom based quality assurance. Furthermore, the MCS of VMAT plan can evaluate delivery accuracy for patients receiving of VMAT. Machine performance was found to be directly related to plan complexity but this is not the dominant determinant of delivery accuracy.

SU‐E‐T‐467: A New Conversion Method of Pixel Values to Hounsfield Units for Cone Beam CT Images

Purpose: The purpose of this study is to develop a new correction method for converting pixel values to Hounsfield units (HUs) using the histograms of pixel values for the cone beam computed tomography (CBCT) to calculate the dose distributions on the CBCT images. Methods: The simulation computed tomography (sim‐CT) and CBCT images of an electron density phantom and 5 prostate cancer patients were acquired. The histograms of pixel values for each slice of the sim‐CT and CBCT images of the electron density phantom and of the prostatic cancer patients were obtained with an in‐house program. To perform correction of the pixel values for each slice of the CBCT images, the shapes of the histograms for each slice of the CBCT images were closed to the shapes of the histograms of the sim‐CT images using linear scaling method with a minimum sum of squared differences for the regions of soft tissue and bone. To evaluate clinical significance, the dose distribution on the mCBCT images was compared with the original plan on the sim‐CT images by using gamma analysis with a 3% / 2 mm criterion. Results: The linear scaling of pixel values in the CBCT images was successfully applied to the histograms. After the linear scaling, the histograms of mCBCT resembled the histograms of sim‐CT. Thus, it is possible to judge pixel values for CBCT images in HUs. However, because the artifacts peculiar to CBCT remained, differences between both HUs were observed The pass rate of gamma analysis (3% / 2 mm) were more than 95%. Conclusion: The proposed novel method using histograms is a feasible method for converting pixel values in CBCT images to HUs; this may facilitate calculation of accurate doses using CBCT images.

SU‐E‐T‐466: A New Evaluation Method of Deformable Image Registration Algorithms for Image‐Guided Radiation Therapy

Purpose: Non‐rigid image registration (NIR) is an essential image processing tool for image‐guided adapted radiation therapy. The current radiotherapy process requires three‐dimensional (3D) quantification of the registration error, which is not accurately achieved by existing evaluation methods. The objective of this study is to develop a method for 3D evaluation of NIR algorithms. Methods: Cone‐beam (reference) and planning (moving) CT scan images of five prostate cancer patients were scanned using the VelocityAI (Velocity Medical Solutions) platform, which employs B‐spline‐based NIR algorithms. The NIR algorithms were evaluated by measuring the distance between two factors: (1) the outlines (reference contours) of the bladder, the dorsal right and left seminal vesicles (SV), the proximal SV, the prostate, and the rectum in the reference images; and (2) other outlines (deformed contours) in the deformed images of the same CT slice. This distance is the difference between two points that indicate the point of intersection of the reference and the deformed contours that cross a straight line every 10° from the center of gravity of the reference contour. However, the difference in direction was determined as being negative if it was inside the reference contours for the deformed contours, and positive if it was outside the reference contours for the deformed contours. The measurement values were displayed on a two‐dimension (2D) difference map. Results: The 2D map showing differences in the prostate indicated an error of −3 to +3 mm on the bladder side, −3 to +3 mm on the proximal SV side, −2 to +8 mm on the rectum side, and −5 to +1 mm on the apex side. Conclusion: This method measures the distance and the difference in direction between the contours in the reference and those in deformable images, and it can be used to accurately evaluate the non‐rigid image registration.

SU‐E‐T‐818: Analysis of Internal Margin Based on Organ Deformation for Prostate IMRT

Purpose: The purpose of this study was to predict organ deformation and the possible CTV‐ITV reduction when adopting daily IGRT for prostate cancer patient. Further, to identify any intra‐fraction time trends in organ deformation. And to analyze internal margin for prostate IMRT based on organ deformation. Methods: It was intended to investigate 30 patients treated with prostate IMRT. The pre‐disposal in radiotherapy was implemented by instructing each patient to drink 500 cc of water each time after defecation and urination. CTimages for radiation treatment planning were acquired 3 times at 10‐minute intervals for 30 minutes after drinking water. CTimages were transferred to treatment planning system. One physician contoured the prostate, rectum, and bladder for all images. The acquired three‐dimensional contour data of the organ were transferred to a customized data processing program. Then, the prostate, rectum and bladder were divided into eight segments. These regions were analyzed with respect to deformation of the entire set of contour points in the 3‐dimensional outline surface in the region of each of the organs. Results: The deformation of the prostate tended to become amplified in the extension direction accompanied by an increase in deformation of the bladder to the anterior and superior direction. But there were not correlated the rectum deformation. The internal margins which not considered organ deformation, were 2.27 mm, 1.76 mm, and 0.99 mm in the L‐R, S‐I, and A‐P direction respectively. However, respect to the organ deformation, these margins were 1.27 mm, 1.56 mm, and 1.53 mm respectively. Conclusions: This analysis based on organ deformation was found to be useful for determination of internal margin. And it will be possible that the extra margin is reduced.