4D-CBCT Images Research Articles

Image registration of a three-dimensional dynamic phantom in four-dimensional cone-beam computed tomography and four-dimensional computed tomography

Objective To evaluate the image quality and registration accuracy of a three-dimensional (3D) dynamic phantom in four-dimensional computed tomography (4DCT) and four-dimensional cone-beam computed tomography (4DCBCT). Methods The Computerized Imaging Reference Systems Dynamic Thorax Phantom Model 008A was scanned to get 4DCT and 4DCBCT images. Two balls with different diameters (O= 1 cm and O= 2 cm) were used to simulate tumors with different sizes. The motion mode of the balls was 3D sinusoidal motion at 0.25 Hz (the amplitudes along the x, y, and z axes were ±1.0 cm, ±0.4 cm, and ±0.2 cm, respectively). Gross target volumes (GTVs) from 10-phase bins, internal gross target volumes (IGTV), and target volumes on maximum intensity projection (MIP) and mean intensity projection (MeanIP) images were contoured and calculated. Target volumes on 4DCT or 4DCBCT images were compared with the static and dynamic volumes of the balls (VS and VD). The matching index (MI) of target volumes between the 4DCT and 4DCBCT images was analyzed after rigid image registration. Results The GTV in each phase of the image was larger than VS. The difference between the average GTV derived from 10 phases of 4DCT or 4DCBCT images and Vs of the small ball was larger than that of the large ball (35.03% vs. 22.66%; 32.62% vs. 17.00%). All the IGTVs and target volumes on MIP images were slightly larger than VD, but target volumes on MeanIP images were smaller than VD. The average MI of 10-phase bins of the small ball was smaller than that of the large ball (66.76% vs. 82.21%). Moreover, MIs of IGTV, MIP, and MeanIP of the small ball were also smaller than those of the large ball (77.39% vs. 90.29%; 75.90% vs. 89.28%; 74.47% vs. 82.74%). Conclusions In the case of a relatively small tumor volume and a relatively large motion amplitude, 4DCT and 4DCBCT should be used with caution for comparison of image registration. Key words: Tomography, X-ray computed; Tomography, X-ray computed, cone-beam; Imaging registration; Phantom

SU‐E‐J‐153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments

Purpose:To verify that the planned Primary Target Volume (PTV) and Internal Gross Tumor Volume (IGTV) fully enclose a moving lung tumor volume as visualized on a pre‐SABR treatment verification 4D Cone Beam CT.Methods:Daily 3DCBCT image sets were acquired immediately prior to treatment for 10 SABR lung patients using the on‐board imaging system integrated into a Varian TrueBeam (v1.6: no 4DCBCT module available). Respiratory information was acquired during the scan using the Varian RPM system. The CBCT projections were sorted into 8 bins offline, both by breathing phase and amplitude, using in‐house software. An iterative algorithm based on total variation minimization, implemented in the open source reconstruction toolkit (RTK), was used to reconstruct the binned projections into 4DCBCT images. The relative tumor motion was quantified by tracking the centroid of the tumor volume from each 4DCBCT image. Following CT‐CBCT registration, the planning CT volumes were compared to the location of the CBCT tumor volume as it moves along its breathing trajectory. An overlap metric quantified the ability of the planned PTV and IGTV to contain the tumor volume at treatment.Results:The 4DCBCT reconstructed images visibly show the tumor motion. The mean overlap between the planned PTV (IGTV) and the 4DCBCT tumor volumes was 100% (94%), with an uncertainty of 5% from the 4DCBCT tumor volume contours. Examination of the tumor motion and overlap metric verify that the IGTV drawn at the planning stage is a good representation of the tumor location at treatment.Conclusion:It is difficult to compare GTV volumes from a 4DCBCT and a planning CT due to image quality differences. However, it was possible to conclude the GTV remained within the PTV 100% of the time thus giving the treatment staff confidence that SABR lung treatements are being delivered accurately.

SU‐E‐J‐151: Day‐To‐Day Variations in Fraction‐Specific Motion Modeling Using Patient 4DCBCT Images

Purpose:The goal of this study is to quantify the interfraction reproducibility of patient‐specific motion models derived from 4DCBCT acquired on the day of treatment of lung cancer stereotactic body radiotherapy (SBRT) patients.Methods:Motion models are derived from patient 4DCBCT images acquired daily over 3–5 fractions of treatment by 1) applying deformable image registration between each 4DCBCT image and a reference phase from that day, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs to derive a motion model. The motion model from the first day of treatment is compared to motion models from each successive day of treatment to quantify variability in motion models generated from different days. Four SBRT patient datasets have been acquired thus far in this IRB approved study.Results:Fraction‐specific motion models for each fraction and patient were derived and PCA eigenvectors and their associated eigenvalues are compared for each fraction. For the first patient dataset, the average root mean square error between the first two eigenvectors associated with the highest two eigenvalues, in four fractions was 0.1, while it was 0.25 between the last three PCA eigenvectors associated with the lowest three eigenvalues. It was found that the eigenvectors and eigenvalues of PCA motion models for each treatment fraction have variations and the first few eigenvectors are shown to be more stable across treatment fractions than others.Conclusion:Analysis of this dataset showed that the first two eigenvectors of the PCA patient‐specific motion models derived from 4DCBCT were stable over the course of several treatment fractions. The third, fourth, and fifth eigenvectors had larger variations.

WE‐G‐207‐06: 3D Fluoroscopic Image Generation From Patient‐Specific 4DCBCT‐Based Motion Models Derived From Physical Phantom and Clinical Patient Images

Purpose:Respiratory‐correlated cone‐beam CT (4DCBCT) images acquired immediately prior to treatment have the potential to represent patient motion patterns and anatomy during treatment, including both intra‐ and inter‐fractional changes. We develop a method to generate patient‐specific motion models based on 4DCBCT images acquired with existing clinical equipment and used to generate time varying volumetric images (3D fluoroscopic images) representing motion during treatment delivery.Methods:Motion models are derived by deformably registering each 4DCBCT phase to a reference phase, and performing principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated by optimizing the resulting PCA coefficients iteratively through comparison of the cone‐beam projections simulating kV treatment imaging and digitally reconstructed radiographs generated from the motion model. Patient and physical phantom datasets are used to evaluate the method in terms of tumor localization error compared to manually defined ground truth positions.Results:4DCBCT‐based motion models were derived and used to generate 3D fluoroscopic images at treatment time. For the patient datasets, the average tumor localization error and the 95th percentile were 1.57 and 3.13 respectively in subsets of four patient datasets. For the physical phantom datasets, the average tumor localization error and the 95th percentile were 1.14 and 2.78 respectively in two datasets. 4DCBCT motion models are shown to perform well in the context of generating 3D fluoroscopic images due to their ability to reproduce anatomical changes at treatment time.Conclusion:This study showed the feasibility of deriving 4DCBCT‐based motion models and using them to generate 3D fluoroscopic images at treatment time in real clinical settings. 4DCBCT‐based motion models were found to account for the 3D non‐rigid motion of the patient anatomy during treatment and have the potential to localize tumor and other patient anatomical structures at treatment time even when inter‐fractional changes occur.This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc., Palo Alto, CA. The project was also supported, in part, by Award Number R21CA156068 from the National Cancer Institute.

WE‐G‐207‐03: Mask Guided Image Reconstruction (MGIR): A Novel Method for Ultra‐Low‐Dose 3D and Enhanced 4D Cone‐Beam Computer‐Tomography

Purpose:Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low‐dose 3DCBCT and 4DCBCT under single mathematical framework.Methods:In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori‐defined mask and 2) outside the mask. Then we update each part of images alternatively thorough solving minimization problems based on CS type IR. For low‐dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously.Results:Examination of MGIR algorithm showed that high quality of both low‐dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low‐dose 3DCBCT, a clinical viable and high resolution head‐and‐neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan.Conclusion:The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low‐dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on‐line clinical applications.Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00

WE-D-303-04: 4DCBCT-Based Dose Assessment for SBRT Lung Cancer Treatment

Purpose: To develop a 4DCBCT-based dose assessment method for calculating actual delivered dose for patients with significant respiratory motion during the course of SBRT or anatomical changes between treatment days. Methods: To address the limitation of 4DCT-based dose assessment, we propose to calculate the delivered dose using time varying (‘fluoroscopic’) 3D patient images generated from a 4DCBCT-based motion model. The method includes four steps: (1) before each treatment, 4DCBCT data is acquired with the patient in treatment position, based on which a patient-specific motion model is created using a principal components analysis (PCA) algorithm. (2) During treatment, 2D time-varying kV projection images are continuously acquired, from which time-varying ‘fluoroscopic’ 3D images of the patient are reconstructed using the motion model. (3) Lateral truncation artifacts are corrected using planning 4DCT images. (4) the 3D dose distribution is computed for each timepoint in the set of 3D fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach is validated using six modified XCAT phantoms with lung tumors and different respiratory motions derived from patient data. The estimated doses are compared to that calculated using ground-truth XCAT phantoms. Results: For each XCAT phantom, the delivered tumor dose values generally follow the same trend as that of the ground truth and at most timepoints the difference is less than 5%. For the overall delivered dose, the normalized error of calculated 3D dose distribution is generally less than 3% and the tumor D95 error is less than 1.5%. Conclusions: XCAT phantom studies indicate the potential of the proposed method to accurately estimate 3D tumor dose distributions for SBRT lung treatment based on 4DCBCT imaging and motion modeling. Further research is necessary to investigate its performance for clinical patients data.

TU‐F‐CAMPUS‐I‐03: Enhancement of 4D CBCT Image Quality Using An Adaptive Prior Image Constrained Compressed Sensing

Purpose:To develop an iterative reconstruction algorithm using a compressed sensing with adaptive prior image constraints to solve 4D CBCT reconstruction problem.Methods:The images reconstructed by the FDK algorithm with a full set of unsorted projections are served as prior images for partial projections in each phase group and are utilized as an initial guess. Additionally, the prior images are clustered into several regions by applying intensity‐based thresholding, which is referred to as the segmented prior images. The segmented prior images are employed to detect any possible mismatched areas compared with the target images generated by partial projection data. With these two prior images, our algorithm alternately performs the simultaneous algebraic reconstruction technique and anisotropic total variation regularization while adjusting a weighted relaxation map during the iterative reconstruction process. The weighted relaxation map depends on binary images created by the voxel‐dependent comparison between the segmented prior and segmented target images. For the segmented target images, the k‐means clustering with a geometric weighting is applied on the reconstruction images generated in each iteration step. The inverse values of the distance map converted from binary images are assigned to be the values of the relaxation map. Evaluations using Catphan504 phantom with a motion platform were carried out.Results:Qualitative and quantitative analyses showed that the method provides high‐quality CBCT reconstruction images when compared with those generated by the FDK, CS, and PICCS algorithms, with higher contrast‐to‐noise ratio and faster convergence caused by minimizing data fidelity. Especially, the proposed method was superior to PICCS in the aspect of updating locally‐mismatched region.Conclusion:The proposed method not only improves the image quality of 4D CBCT by adaptive updates during the reconstruction process, but also leads to a lower imaging dose and faster acquisition time by using a regular 3D CBCT scan.

SU‐C‐207‐01: Four‐Dimensional Inverse Geometry Computed Tomography: Concept and Its Validation

Purpose:In past few years, the inverse geometry computed tomography (IGCT) system has been developed to overcome shortcomings of a conventional computed tomography (CT) system such as scatter problem induced from large detector size and cone‐beam artifact. In this study, we intend to present a concept of a four‐dimensional (4D) IGCT system that has positive aspects above all with temporal resolution for dynamic studies and reduction of motion artifact.Methods:Contrary to conventional CT system, projection data at a certain angle in IGCT was a group of fractionated narrow cone‐beam projection data, projection group (PG), acquired from multi‐source array which have extremely short time gap of sequential operation between each of sources. At this, for 4D IGCT imaging, time‐related data acquisition parameters were determined by combining multi‐source scanning time for collecting one PG with conventional 4D CBCT data acquisition sequence. Over a gantry rotation, acquired PGs from multi‐source array were tagged time and angle for 4D image reconstruction. Acquired PGs were sorted into 10 phase and image reconstructions were independently performed at each phase. Image reconstruction algorithm based upon filtered‐backprojection was used in this study.Results:The 4D IGCT had uniform image without cone‐beam artifact on the contrary to 4D CBCT image. In addition, the 4D IGCT images of each phase had no significant artifact induced from motion compared with 3D CT.Conclusion:The 4D IGCT image seems to give relatively accurate dynamic information of patient anatomy based on the results were more endurable than 3D CT about motion artifact. From this, it will be useful for dynamic study and respiratory‐correlated radiation therapy.This work was supported by the Industrial R&D program of MOTIE/KEIT [10048997, Development of the core technology for integrated therapy devices based on real‐time MRI guided tumor tracking] and the Mid‐career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.

WE-D-303-05: Dosimetric Accuracy of CBCT Images Estimated by a Motion Modeling and Free-Form Deformation Technique for Radiotherapy of Lung Cancer

Purpose: To investigate the dosimetric accuracy of CBCTs estimated by a motion modeling and free-form deformation(MM-FD) technique for radiotherapy of lung cancer. Methods: Various inter-fractional variations featuring patient motion pattern change, tumor size change and tumor average position change were simulated from planning-CT to on-board images using both digital and physical motion phantoms. The doses calculated on the planning-CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses) and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress(FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the ‘gold-standard’ on-board images (gold-standard doses). The absolute deviations of minimum dose (dDmin), maximum dose (dDmax), mean dose (dDmean) and dose coverage (dV100%) of PTV were evaluated. In addition, 4D on-board dose accumulations were performed using the 4D-CBCT images estimated by MM-FD. The accumulated doses were compared to measurements using OSL detectors and radiochromic films. Results: Of all the 50 scenarios simulated, the average(± standard deviation) dDmin, dDmax, dDmean and dV100% (values normalized by the prescription dose or the PTV volume) between the planned and the gold-standard PTV doses were 34.8% (± 29.2%), 3.2% (± 3.8%), 3.5% (± 3.5%) and 13.0% (± 11.4%), respectively. The corresponding values of FDK PTV doses were 3.1% (± 3.7%), 1.4% (± 1.1%), 2.1% (± 0.8%) and 14.5% (± 14.2%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (± 0.5%), 0.9% (± 0.7%), 0.6% (± 0.4%) and 0.9% (± 0.8%), respectively.For the 4D dose accumulation study, the average(± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.0% (± 2.4%). The average gamma pass-rate(3%/3mm) between the accumulated doses and the radiochromic film measured doses was 96.1%. Conclusion: MM-FD estimated CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. The research was funded by the National Institutes of Health Grant No. R01-CA184173 and a grant from Varian Medical Systems.

TH-CD-303-07: Evaluation of Four Data-Driven Respiratory Motion-Compensation Methods for Four-Dimensional Cone-Beam CT Registration and Reconstruction

Purpose: To develop and compare four 4D-CBCT reconstruction methods that correct for respiratory motion and enhance image quality. Methods: Four motion-compensation workflows were developed employing a combination of deformable image registration (DIR) and image reconstruction. Groupwise registration was used to simultaneously register all frames of a base Four-Dimensional Cone-Beam CT (4D-CBCT) FDK reconstruction to a reference frame, providing a 4D transformation. This 4D transformation expresses a respiratory motion model and was used to apply a deformation during the backprojection operation to create a motion-compensated reconstruction. Two variables were assessed: reference image of registration (fixed or mean frame) and inclusion or exclusion of the motion-compensated reconstruction step. Fixed frame registration refers to using an actual frame from the 4D image (end of inhalation) as the target image. Mean frame registration refers to using a per iteration-estimated average frame image instead. Image quality was assessed in one clinical case through tissue boundary sharpness, noise, and presence of view-aliasing artifact. Results: Diaphragm edge sharpness (DES) was defined as the slope of the intensity gradient along the lung-diaphragm boundary. DES relative to the base 4D-CBCT reconstruction improved by 26.8% using fixed-frame registration alone; 15.5% using fixed-frame with motion-compensated reconstruction; decreased by 2.9% using mean-frame registration alone; and improved by 12.2% using mean-frame with motion-compensated reconstruction. Noise was reduced in soft tissue by 8.7% and 75.8% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 8.8% and 77.7% for the corresponding mean-frame methods. Reductions in undersampling artifacts were visible for all four methods relative to the base reconstruction while retaining higher frequency spatial details (i.e. blood vessels). Conclusion: A data-driven approach combining groupwise registration and motion-compensation has the potential to improve 4D-CBCT image quality. Adding motion compensation after groupwise registration reduced sharpness somewhat but resulted in reduced noise and structural artifacts. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. The authors have no conflicts of interest.

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we developed and performed initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and used these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparison to ground truth digital and physical phantom images. The performance of 4DCBCT-based and 4DCT-based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

PO-1116 Implementation of 4D-CBCT image guidance into the routine clinical workflow: an RTT led process

PO-1116 Implementation of 4D-CBCT image guidance into the routine clinical workflow: an RTT led process

Preliminary clinical evaluation of a 4D-CBCT estimation technique using prior information and limited-angle projections

Background and purposeA technique has been previously reported to estimate high-quality 4D-CBCT using prior information and limited-angle projections. This study is to investigate its clinical feasibility through both phantom and patient studies. Materials and methodsThe new technique used to estimate 4D-CBCT is called MMFD-NCC. It is based on the previously reported motion modeling and free-form deformation (MMFD) method, with the introduction of normalized-cross-correlation (NCC) as a new similarity metric. The clinical feasibility of this technique was evaluated by assessing the accuracy of estimated anatomical structures in comparison to those in the ‘ground-truth’ reference 4D-CBCTs, using data obtained from a physical phantom and three lung cancer patients. Both volume percentage error (VPE) and center-of-mass error (COME) of the estimated tumor volume were used as the evaluation metrics. ResultsThe average VPE/COME of the tumor in the prior image was 257.1%/10.1mm for the phantom study and 55.6%/3.8mm for the patient study. Using only orthogonal-view 30° projections, the MMFD-NCC has reduced the corresponding values to 7.7%/1.2mm and 9.6%/1.1mm, respectively. ConclusionThe MMFD-NCC technique is able to estimate 4D-CBCT images with geometrical accuracy of the tumor within 10% VPE and 2mm COME, which can be used to improve the localization accuracy of radiotherapy.

Measuring interfraction and intrafraction lung function changes during radiation therapy using four-dimensional cone beam CT ventilation imaging.

Adaptive ventilation guided radiation therapy could minimize the irradiation of healthy lung based on repeat lung ventilation imaging (VI) during treatment. However the efficacy of adaptive ventilation guidance requires that interfraction (e.g., week-to-week), ventilation changes are not washed out by intrafraction (e.g., pre- and postfraction) changes, for example, due to patient breathing variability. The authors hypothesize that patients undergoing lung cancer radiation therapy exhibit larger interfraction ventilation changes compared to intrafraction function changes. To test this, the authors perform the first comparison of interfraction and intrafraction lung VI pairs using four-dimensional cone beam CT ventilation imaging (4D-CBCT VI), a novel technique for functional lung imaging. The authors analyzed a total of 215 4D-CBCT scans acquired for 19 locally advanced non-small cell lung cancer (LA-NSCLC) patients over 4-6 weeks of radiation therapy. This set of 215 scans was sorted into 56 interfraction pairs (including first day scans and each of treatment weeks 2, 4, and 6) and 78 intrafraction pairs (including pre/postfraction scans on the same-day), with some scans appearing in both sets. VIs were obtained from the Jacobian determinant of the transform between the 4D-CBCT end-exhale and end-inhale images after deformable image registration. All VIs were deformably registered to their corresponding planning CT and normalized to account for differences in breathing effort, thus facilitating image comparison in terms of (i) voxelwise Spearman correlations, (ii) mean image differences, and (iii) gamma pass rates for all interfraction and intrafraction VI pairs. For the side of the lung ipsilateral to the tumor, we applied two-sided t-tests to determine whether interfraction VI pairs were more different than intrafraction VI pairs. The (mean ± standard deviation) Spearman correlation for interfraction VI pairs was r̄(Inter)=0.52±0.25, which was significantly lower than for intrafraction pairs (r̄(Intra)=0.67±0.20, p = 0.0002). Conversely, mean absolute ventilation differences were larger for interfraction pairs than for intrafraction pairs, with |ΔV̄(Inter)|=0.42±0.65 and |ΔV̄(Intra)|=0.32±0.53, respectively (p < 10(-15)). Applying a gamma analysis with ventilation/distance tolerance of 25%/10 mm, we observed mean pass rate of (69% ± 20%) for interfraction VIs, which was significantly lower compared to intrafraction pairs (80% ± 15%, with p ∼ 0.0003). Compared to the first day scans, all patients experienced at least one subsequent change in median ipsilateral ventilation ≥10%. Patients experienced both positive and negative ventilation changes throughout treatment, with the maximum change occurring at different weeks for different patients. The authors' data support the hypothesis that interfraction ventilation changes are larger than intrafraction ventilation changes for LA-NSCLC patients over a course of conventional lung cancer radiation therapy. Longitudinal ventilation changes are observed to be highly patient-dependent, supporting a possible role for adaptive ventilation guidance based on repeat 4D-CBCT VIs. We anticipate that future improvement of 4D-CBCT image reconstruction algorithms will improve the capability of 4D-CBCT VI to resolve interfraction ventilation changes.

Optimizing 4DCBCT projection allocation to respiratory bins

4D cone beam computed tomography (4DCBCT) is an emerging image guidance strategy used in radiotherapy where projections acquired during a scan are sorted into respiratory bins based on the respiratory phase or displacement. 4DCBCT reduces the motion blur caused by respiratory motion but increases streaking artefacts due to projection under-sampling as a result of the irregular nature of patient breathing and the binning algorithms used. For displacement binning the streak artefacts are so severe that displacement binning is rarely used clinically. The purpose of this study is to investigate if sharing projections between respiratory bins and adjusting the location of respiratory bins in an optimal manner can reduce or eliminate streak artefacts in 4DCBCT images. We introduce a mathematical optimization framework and a heuristic solution method, which we will call the optimized projection allocation algorithm, to determine where to position the respiratory bins and which projections to source from neighbouring respiratory bins. Five 4DCBCT datasets from three patients were used to reconstruct 4DCBCT images. Projections were sorted into respiratory bins using equispaced, equal density and optimized projection allocation. The standard deviation of the angular separation between projections was used to assess streaking and the consistency of the segmented volume of a fiducial gold marker was used to assess motion blur. The standard deviation of the angular separation between projections using displacement binning and optimized projection allocation was 30%–50% smaller than conventional phase based binning and 59%–76% smaller than conventional displacement binning indicating more uniformly spaced projections and fewer streaking artefacts. The standard deviation in the marker volume was 20%–90% smaller when using optimized projection allocation than using conventional phase based binning suggesting more uniform marker segmentation and less motion blur. Images reconstructed using displacement binning and the optimized projection allocation algorithm were clearer, contained visibly fewer streak artefacts and produced more consistent marker segmentation than those reconstructed with either equispaced or equal-density binning. The optimized projection allocation algorithm significantly improves image quality in 4DCBCT images and provides, for the first time, a method to consistently generate high quality displacement binned 4DCBCT images in clinical applications.

Lung 4D-CBCT Reconstruction Using Prior Information and Limited-Angle Projections: Phantom and Patient Studies

Purpose/Objective(s): Current application of 4D-CBCT in clinics is limited due to its long scan time and high imaging dose. This study is to develop and clinically evaluate a 4D-CBCT reconstruction method using limited-angle projections to reduce scan time and dose. Materials/Methods: A motion-modeling and free-form deformation (MMFD) method is proposed to reconstruct 4D-CBCT images using only orthogonal limited-angle projections. In this method, each phase of the onboard 4D-CBCT is viewed as a deformation of a prior CT image. To solve the deformation field, the MM-FD method applies a principal component analysis based motion-modeling to generate a coarse estimation of the deformation field, followed by a constrained free-form deformation algorithm to further fine-tune the field. Data acquired from a dynamic thorax physical phantom and three lung cancer patients were used to evaluate the MM-FD method. 4D-CBCT reconstructions from a full sample of projections were used as the ground truth. The accuracy of the MM-FD method was evaluated by calculating the volume percentage difference (VPD) and center-of-mass shift (COMS) of the reconstructed tumor volume. The accuracy of the FDK, MM-only and FD-only methods was also evaluated for comparison. Results: Results showed that MM-FD was substantially more accurate than FDK, MM-only and FD-only methods. For the phantom study using orthogonal 30 projections, the average ( standard deviation) VPD values for MM-only, FD-only and MM-FD methods were 223.3% ( 2.5%), 29.2% ( 24.9%) and 6.6% ( 2.6%), respectively. The corresponding COMS values were 2.0mm ( 1.6mm), 3.5mm ( 2.7mm) and 1.1mm ( 0.4mm), respectively. For the patient study, the average ( standard deviation) VPD values for MM-only, FD-only and MM-FD methods were 36.0% ( 32.2%), 21.4% ( 13.9%) and 9.6% ( 6.1%). The corresponding COMS values were 2.2mm ( 1.5mm), 1.6mm ( 0.6mm) and 1.1mm ( 0.5mm). The MM-FD method yielded similar reconstruction accuracy using different scan directions. The average ( standard deviation) VPD and COMS were 6.6% ( 2.6%) and 1.1mm ( 0.5mm) using orthogonal 30 projections from anterior-posterior and left-lateral directions, and 8.3% ( 2.5%) and 1.3mm ( 0.4mm) using projections from left-anterior-oblique and left-posterior-oblique directions. The MM-FD method was less accurate when using projections of lower sampling frequency. The average ( standard deviation) VPD and COMS were 6.6% ( 2.6%) and 1.1mm ( 0.4mm) using 1/projection, and 23.1% ( 5.3%) and 1.2mm ( 0.4mm) using 4/projection. Conclusions: The MM-FD method can accurately reconstruct 4D-CBCT images using only limited-angle projections and therefore shows great potential to reduce the scan time and dose for 4D-CBCT imaging. Author Disclosure: Y. Zhang: A. Employee; Duke University Medical Center. F. Yin: A. Employee; Duke University Medical Center. E. Research Grant; Varian Medical Systems, National Institute of Health. Q. Patent/License Fee/Copyright; Patent. S. Leadership; AAPM. T. Pan: A. Employee; The University of Texas, MD Anderson Cancer Center. E. Research Grant; Cancer Prevention Research Institute of Texas. I. Vergalasova: A. Employee; Duke University Medical Center. L. Ren: A. Employee; Duke University Medical Center. E. Research Grant; Varian Medical Systems, National Institute of Health.

Accuracy of 4D Cone Beam CT and 4D Fan Beam CT for Thoracic Radiation Therapy Simulation and Verification Imaging

Recently, respiratory-correlated cone beam CT (4DCBCT) has become commercially available for image verification of thoracic radiation therapy. However, it is unknown if 4DCBCT imaging of respiratory motion substantially differs from 4D fan beam CT (4DCT) imaging at treatment simulation. We tested the hypothesis that contours of a simulated thoracic lesion would be significantly different for 4DCBCT and 4DCT imaging under respiratory motion conditions.

Poster — Thur Eve — 12: Implementation of a Clinical Lung Tumour High Dose Containment Verification Procedure using Respiratory Cone‐Beam CT (4DCBCT) on a Varian TrueBeam Linac

Lung tumours move due to respiratory motion. This is managed during planning by acquiring a 4DCT and capturing the excursion of the GTV (gross tumour volume) throughout the breathing cycle within an IGTV (Internal Gross Tumour Volume) contour. Patients undergo a verification cone‐beam CT (CBCT) scan immediately prior to treatment. 3D reconstructed images do not consider tumour motion, resulting in image artefacts, such as blurring. This may lead to difficulty in identifying the tumour on reconstructed images. It would be valuable to create a 4DCBCT reconstruction of the tumour motion to confirm that does indeed remain within the planned IGTV. CBCT projections of a Quasar Respiratory Motion Phantom are acquired in Treatment mode (half‐fan scan) on a Varian TrueBeam accelerator. This phantom contains a mobile, low‐density lung insert with an embedded 3cm diameter tumour object. It is programmed to create a 15s periodic, 2cm (sup/inf) displacement. A Varian Real‐time Position Management (RPM) tracking‐box is placed on the phantom breathing platform. Breathing phase information is automatically integrated into the projection image files. Using in‐house Matlab programs and RTK (Reconstruction Tool Kit) open‐source toolboxes, the projections are re‐binned into 10 phases and a 4DCBCT scan reconstructed. The planning IGTV is registered to the 4DCBCT and the tumour excursion is verified to remain within the planned contour. This technique successfully reconstructs 4DCBCT images using clinical modes for a breathing phantom. UBC‐BCCA ethics approval has been obtained to perform 4DCBCT reconstructions on lung patients (REB#H12‐00192). Clinical images will be accrued starting April 2014.

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

TH‐E‐17A‐09: High Quality and Artifact‐Free 4D Cone Beam CT and Its Application in Adaptive Treatment Planning

Purpose:To develop a high quality 4D cone beam CT (4DCBCT) method that is immune to patient/couch truncations and to investigate its application in adaptive replanning of lung XRT.Methods:In this study, IRB‐approved human subject CBCT data was acquired using a Varian on‐board imager with 1 minute rotation time. The acquired projection data was retrospectively sorted into 20 respiratory phase bins, from which 4DCBCT images with high SNR and high temporal resolution were generated using Prior Image Constrained Compressed Sensing (PICCS). Couch and patient truncations generate strong data inconsistency in the projection data and artifacts in the 4DCBCT image. They were addressed using an adaptive PICCS method. The artifact‐free PICCS‐4DCBCT images were used to generate adaptive treatment plans for the same patient at the 10th (day 21) and 30th (day 47) fractions. Dosimetric impacts with and without PICCS‐ 4DCBCT were evaluated by isodose distributions, DVHs, and other dosimetric factors.Results:The adaptive PICCS‐4DCBCT method improves image quality by removing residue truncation artifacts; measured universal image quality increased 37%. The isodose lines and DVHs with PICCS‐4DCBCT‐based adaptive replanning were significantly more conformal to PTV than without replanning due to changes in patient anatomy caused by progress of the treatment. The mean dose to PTV at the 10th fraction was 63.1Gy with replanning and 64.2Gy without replanning, where the prescribed dose was 60Gy, in 2Gy × 30 fractions. The mean dose to PTV at the 30th fraction was 61.6Gy with replanning and 64.9Gy without replanning. Lung V20 was 37.1%, 41.9% and 43.3% for original plan, 10th fraction plan and 30th fraction plan; with re‐planning, Lung V20 was 37.1%, 32%, 27.8%.Conclusion:4DCBCT imaging using adaptive PICCS is able to generate high quality, artifact‐free images that potentially can be used to create replanning for improving radiotherapy of the lung. K Niu, K Li, J Smilowitz: Nothing to Disclose.G Chen: General Electric Company Research funded, Siemens AG Research funded, Varian Medical Systems Research funded, Hologic Research funded.