Agung Hertanto

A patient-specific respiratory model of anatomical motion for radiation treatment planning

The modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration-induced organ motion in the thorax without the commonly adopted assumption of repeatable breath cycles. The model describes the motion of a volume of interest within the patient based on a reference three-dimensional (3D) image (at end expiration) and the diaphragm positions at different time points. The input data are respiration-correlated CT (RCCT) images of patients treated for non-small- cell lung cancer, consisting of 3D images, including the diaphragm positions, at ten phases of the respiratory cycle. A deformable image registration algorithm calculates the deformation field that maps each 3D image to the reference 3D image. A principal component analysis is performed to parameterize the 3D deformation field in terms of the diaphragm motion. We show that the first two principal components are adequate to accurately and completely describe the organ motion in the data of four patients. Artifacts in the RCCT images that commonly occur at the mid-respiration states are reduced in the model-generated images. Further validation of the model is demonstrated in the successful application of the parameterized 3D deformation field to RCCT data of the same patient but acquired several days later. We have developed a method for predicting respiration-induced organ motion in patients that has potential for improving the accuracy of dose calculation in radiotherapy. Possible limitations of the model are cases where the correlation between lung tumor and diaphragm position is less reliable such as superiorly situated tumors and interfraction changes in tumor-diaphragm correlation. The limited number of clinical cases examined suggests, but does not confirm, the models applicability to a wide range of patients.

Evaluation of an automated deformable image matching method for quantifying lung motion in respiration‐correlated CT images

We have evaluated an automated registration procedure for predicting tumor and lung deformation based on CT images of the thorax obtained at different respiration phases. The method uses a viscous fluid model of tissue deformation to map voxels from one CT dataset to another. To validate the deformable matching algorithm we used a respiration-correlated CT protocol to acquire images at different phases of the respiratory cycle for six patients with nonsmall cell lung carcinoma. The position and shape of the deformable gross tumor volumes (GTV) at the end-inhale (EI) phase predicted by the algorithm was compared to those drawn by four observers. To minimize interobserver differences, all observers used the contours drawn by a single observer at end-exhale (EE) phase as a guideline to outline GTV contours at EI. The differences between model-predicted and observer-drawn GTV surfaces at EI, as well as differences between structures delineated by observers at EI (interobserver variations) were evaluated using a contour comparison algorithm written for this purpose, which determined the distance between the two surfaces along different directions. The mean and 90% confidence interval for model-predicted versus observer-drawn GTV surface differences over all patients and all directions were 2.6 and 5.1 mm, respectively, whereas the mean and 90% confidence interval for interobserver differences were 2.1 and 3.7 mm. We have also evaluated the algorithms ability to predict normal tissue deformations by examining the three-dimensional (3-D) vector displacement of 41 landmarks placed by each observer at bronchial and vascular branch points in the lung between the EE and EI image sets (mean and 90% confidence interval displacements of 11.7 and 25.1 mm, respectively). The mean and 90% confidence interval discrepancy between model-predicted and observer-determined landmark displacements over all patients were 2.9 and 7.3 mm, whereas interobserver discrepancies were 2.8 and 6.0 mm. Paired t tests indicate no significant statistical differences between model predicted and observer drawn structures. We conclude that the accuracy of the algorithm to map lung anatomy in CT images at different respiratory phases is comparable to the variability in manual delineation. This method has therefore the potential for predicting and quantifying respiration-induced tumor motion in the lung.

Reduction of irregular breathing artifacts in respiration-correlated CT images using a respiratory motion model

PURPOSE Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data. METHODS Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans. RESULTS Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states. CONCLUSIONS Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the methods performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data.

Evaluation of tumor localization in respiration motion-corrected cone-beam CT: Prospective study in lung

PURPOSE Target localization accuracy of cone-beam CT (CBCT) images used in radiation treatment of respiratory disease sites is affected by motion artifacts (blurring and streaking). The authors have previously reported on a method of respiratory motion correction in thoracic CBCT at end expiration (EE). The previous retrospective study was limited to examination of reducing motion artifacts in a small number of patient cases. They report here on a prospective study in a larger group of lung cancer patients to evaluate respiratory motion-corrected (RMC)-CBCT ability to improve lung tumor localization accuracy and reduce motion artifacts in Linac-mounted CBCT images. A second study goal examines whether the motion correction derived from a respiration-correlated CT (RCCT) at simulation yields similar tumor localization accuracy at treatment. METHODS In an IRB-approved study, 19 lung cancer patients (22 tumors) received a RCCT at simulation, and on one treatment day received a RCCT, a respiratory-gated CBCT at end expiration, and a 1-min CBCT. A respiration monitor of abdominal displacement was used during all scans. In addition to a CBCT reconstruction without motion correction, the motion correction method was applied to the same 1-min scan. Projection images were sorted into ten bins based on abdominal displacement, and each bin was reconstructed to produce ten intermediate CBCT images. Each intermediate CBCT was deformed to the end expiration state using a motion model derived from RCCT. The deformed intermediate CBCT images were then added to produce a final RMC-CBCT. In order to evaluate the second study goal, the CBCT was corrected in two ways, one using a model derived from the RCCT at simulation [RMC-CBCT(sim)], the other from the RCCT at treatment [RMC-CBCT(tx)]. Image evaluation compared uncorrected CBCT, RMC-CBCT(sim), and RMC-CBCT(tx). The gated CBCT at end expiration served as the criterion standard for comparison. Using automatic rigid image registration, each CBCT was registered twice to the gated CBCT, first aligned to spine, second to tumor in lung. Localization discrepancy was defined as the difference between tumor and spine registration. Agreement in tumor localization with the gated CBCT was further evaluated by calculating a normalized cross correlation (NCC) of pixel intensities within a volume-of-interest enclosing the tumor in lung. RESULTS Tumor localization discrepancy was reduced with RMC-CBCT(tx) in 17 out of 22 cases relative to no correction. If one considers cases in which tumor motion is 5 mm or more in the RCCT, tumor localization discrepancy is reduced with RMC-CBCT(tx) in 14 out of 17 cases (p = 0.04), and with RMC-CBCT(sim) in 13 out of 17 cases (p = 0.05). Differences in localization discrepancy between correction models [RMC-CBCT(sim) vs RMC-CBCT(tx)] were less than 2 mm. In 21 out of 22 cases, improvement in NCC was higher with RMC-CBCT(tx) relative to no correction (p < 0.0001). Differences in NCC between RMC-CBCT(sim) and RMC-CBCT(tx) were small. CONCLUSIONS Motion-corrected CBCT improves lung tumor localization accuracy and reduces motion artifacts in nearly all cases. Motion correction at end expiration using RCCT acquired at simulation yields similar results to that using a RCCT on the treatment day (2-3 weeks after simulation).

TU‐G‐141‐02: Evaluation of Respiration Motion‐Corrected Cone‐Beam CT: Prospective Study in Lung

PURPOSE Respiratory motion adversely affects the ability of cone-beam computerized tomography (CBCT) to guide radiation treatment, due to blurring and streaking artifacts. In a prospective patient study, we evaluate respiratory motion-corrected (RMC) CBCT efficacy in reducing motion artifacts and improving localization accuracy and whether a correction model derived from a respiration-correlated CT (RCCT) at simulation is still valid at treatment. METHODS In an IRB-approved study, lung cancer patients receive an RCCT at simulation, and on one treatment day receive an RCCT, respiratory-gated CBCT, and a one-minute CBCT. A respiration monitor of abdominal displacement is used during all imaging. One-minute CBCT projections are sorted into 10 bins based on abdominal displacement and reconstructed. Each binned CBCT is deformed to the end expiration state using a model derived from RCCT and added to produce an RMC-CBCT. Comparison is made between uncorrected CBCT (NoCorr), RMC(sim) CBCT using simulation RCCT, and RMC(tx) CBCT using treatment RCCT. The gated CBCT, gated at end expiration, serves as the criterion standard for comparison. Each CBCT is registered twice to the gated CBCT, first to align to spine, second to tumor in lung. Localization accuracy is defined as the difference between tumor and spine registration. Agreement in tumor shape with the gated CBCT is evaluated by calculating normalized cross-correlation (NCC) of pixel intensities within the local tumor region. RESULTS NCC from RMC(tx) is higher in 12/13 cases compared to NoCorr, and in 7/13 compared to RMC(sim). Tumor position in RMC(tx) is in closer agreement with gated CBCT than for NoCorr in 11/13 cases. Tumor position between RMC(tx) and RMC(sim) agrees within 2mm in all cases. CONCLUSION This correction method reduces motion artifacts and improves localization accuracy in nearly all cases. Motion correction based on the simulation day RCCT or on the treatment day RCCT yields similar results. NIH/NCI award R01 CA126993 and research grant from Varian.

SU‐E‐J‐119: Comparative Evaluation of Respiratory Motion‐Corrected Cone‐Beam CT Images Derived from Treatment‐Day Vs. Simulation‐Day Respiration‐Correlated CT Scans

PURPOSE Respiration-induced motion artifacts in cone-beam CT (CBCT) can be corrected using a model of patient motion obtained from respiration-correlated CT (RCCT). This approach assumes that respiration-induced organ deformations at simulation, when RCCT scans are normally acquired, are still valid at treatment. The purpose of this study is to compare lung tumor image quality in motion-corrected CBCT images derived from treatment-day RCCT(tx) to simulation-day RCCT(sim) patient images. METHODS In an IRB-approved study, lung cancer patients receive an RCCT at simulation, and an RCCT, gated CBCT and 1-minute CBCT at one treatment session. CBCT projections from the 1-minute scan are sorted according to breathing amplitude from an external monitor and reconstructed and warped to obtain a motion-corrected MC-CBCT at end expiration. Motion correction uses a model adapted from either RCCT(tx) or RCCT(sim), thus obtaining MC-CBCT(tx) and MC-CBCT(sim) images respectively. A gated CBCT, in which gantry rotation and projection acquisition occur within a gate at end expiration, serves as ground truth for comparison. Quality of MC-CBCT images is evaluated from tumor-to-background contrast ratio (TBCR) values measured by delineating the tumor and annular volume around it on the gated CBCT then transferring the contours and aligning them to each MC-CBCT. RESULTS TBCR is found tobe lower in MC-CBCT(sim) images, relative to MC-CBCT(tx), in four out of five patients with mean 21% reduction in a range 9-39%. In the remaining case, where there was no change in TBCR, tumor motion observed in the RCCT was small (2mm). Tumor motion extent relative to diaphragm is observed to change between RCCT(tx) and RCCT(sim) scans. CONCLUSIONS Preliminary results indicate that deformation patterns in lung do change between simulation and treatment. Such variations may reduce the validity of using simulation data for motion-corrected CBCT at treatment. The findings require confirmation with larger numbers of patients. NIH/NCI award R01 CA126993, research grant from Varian Medical Systems.

SU‐C‐BRB‐04: Impact of Residual Systematic Setup Error in the Treatment of Paraspinal Lesions with Single High Dose Stereotactic Body Radiotherapy

Purpose: To use confidence limit dose volume histograms to evaluate the impact of residual systematic error during para‐spinal stereotactic body radiotherapy(SBRT). Methods: Retrospective analyses of the impact of residual systematic errors were performed for ten patients undergoing para‐spinal SBRT. Residual random errors were modeled by convolving the planned dose with a Gaussian distribution (Stdev 1.5 mm). Residual systematic setup errors were assumed to be normally distributed with standard deviations from 1 to 4 mm and were modeled using a Monte Carlo(MC) simulation. During the MC simulation, it was assumed that actual systematic setup error would be kept to institution‐defined thresholds which would equal one standard deviation. Confidence intervals were calculated by sorting the resulting MC DVHs according to a pre‐selected critical dose or volume (e.g. D95). Dose was calculated with an in‐house planning system and a grid spacing of 1mm. Results: Mean (Stdev) planned cord/cauda D05 was 46.1% (11%) of the prescribed dose. Mean 5% and 95% confidence intervals were 100.8% and 117.0% of the planned D05 for a 1 mm residual error and 94.1% and 155.1% for 4 mm residual error. The expected cord/cauda D05 (i.e., mean over all confidence levels) for tumors partially surrounding these structures was generally within 5% of the planned dose and the 5% and 95% confidence limits were approximately 5% to 15% lower/higher than planned dose for systematic errors no more than 2 mm. For tumors surrounding the cord/cauda, D05 equaled or exceeded the planned dose, regardless of confidence level or magnitude of residual error and the expected cord/cauda D05 could exceed planned D05 by 10% or more. Conclusions: Residual systematic error can result in clinically significant deviations between planned and actual dose particularly for patients with extensive disease. Such analyses can guide the level of treatment intervention required for individual patients.

TH‐C‐201C‐07: Reduction of Irregular Breathing Artifacts in Respiration‐Correlated CT Images Using a Respiratory Motion Model

Purpose: Respiration‐correlated CT (RCCT) images produced with commonly used phase‐based sorting often exhibit discontinuity artifacts between CT slices. Displacement‐based sorting reduces artifacts but missing image data (gaps) may occur. We investigate the application of a respiratory motion model to produce an RCCT image set with reduced artifacts. Method and Materials: Input data consist of CT slices from a cine scan acquired while recording respiration by monitoring abdominal displacement. Model‐based generation of RCCT images consists of 4 processing steps: 1) sorting of CT slices according to respiration signal displacement to form volume images at 10 motion states over the cycle; 2) deformable registration between a reference image at one motion state and each of the remaining images; 3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; 4) application of the motion model to deform the reference image into images at the 9 other motion states. Evaluation is in cine scans of a body phantom programmed to move according to a patient respiratory signal and in patient thoracic scans. Results: Comparison in phantom shows that object distortion caused by variable motion amplitude in phase‐based sorting is visibly reduced with model‐based RCCT. Artifacts in patient images at different motion states are also reduced. Comparison with displacement‐sorted images as a ground truth shows that the model‐based images closely reproduce the ground truth geometry at different motion states. Conclusion: Preliminary results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase‐binned images without the gaps inherent in displacement‐binned images. Further study is needed in phantom and patient cine CT scans including ones with more highly irregular breathing patterns. Research supported by NIH/NCI grant ROI CA126993.