On-board CBCT Research Articles

The feasibility of CT simulation-free adaptive radiation therapy.

Novel on-board CBCT allows for improved image quality and Hounsfield unit accuracy. When coupled with online adaptive tools, this may have potential to allow for simulation and treatment to be completed in a single on-table session. To study the feasibility of a high-efficiency radiotherapy treatment workflow without the use of a separate session for simulation imaging. The dosimetric accuracy, overall efficiency, and technical feasibility were used to evaluate the clinical potential of CT simulation-free adaptive radiotherapy. Varian's Ethos adaptive radiotherapy treatment platform was upgraded with a novel CBCT system, HyperSight which reports image quality and Hounsfield unit accuracy specifications comparable to standard fan-beam CT. Using in-house developed MATLAB software, CBCT images were imported into the system and used for planning. Two test cases were completed on anthropomorphic phantoms equipped with small volume ion chambers (cross-calibrated to an ADCL traceable dose standard) to evaluate the feasibility and accuracy of the workflows. A simulated palliative spine treatment was planned with 8Gy in one fraction, and an intact prostate treatment was planned with 60Gy in 20 fractions. The CBCTs were acquired using HyperSight with default thorax and pelvis imaging protocols and reconstructed using an iterative algorithm with scatter removal, iCBCT Acuros. CBCTs were used for contouring and planning, and treatment was delivered via an online adaptive workflow. In addition, an external dosimetry audit was completed using only on-board CBCT imaging in an end-to-end head and neck phantom irradiation. An extended-field CBCT acquisition can be acquired in 12s, in addition to the time for longitudinal table shifts, and reconstructed in approximately 1min. The superior-inferior extent for the CBCT planning images was 38.2cm, which captured the full extent of relevant anatomy. The contouring and treatment planning for the spine and prostate were completed in 30 and 18min, respectively. The dosimetric agreement between ion chamber measurements and the treatment plan was within a range of -1.4 to 1.6%, and a mean and standard deviation of 0.41±1.16%. All metrics used in the external audit met the passing criteria, and the dosimetric comparison between fan-beam and CBCT techniques had a gamma passing rate of 99.0% with a criteria of 2%/2mm. Using an in-house workflow, CT simulation-free radiation therapy was shown to be feasible with acceptable workflow efficiency and dosimetric accuracy. This approach may be particularly applicable for urgent palliative treatments. With the availability of software to enable this workflow, and the continued advancement of on-treatment adaptation, single-visit radiation therapy may replace current practice for some clinical indications.

2V-CBCT: Two-Orthogonal-Projection based CBCT Reconstruction and Dose Calculation for Radiation Therapy using Real Projection Data.

This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for radiation therapy (RT) using real projection data, which is the first 2V-CBCT feasibility study with real projection data, to the best of our knowledge. RT treatments are often delivered in multiple fractions, for which on-board CBCT is desirable to calculate the delivered dose per fraction for the purpose of RT delivery quality assurance and adaptive RT. However, not all RT treatments/fractions have CBCT acquired, but two orthogonal projections are always available. The question to be addressed in this work is the feasibility of 2V-CBCT for the purpose of RT dose calculation. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data.

2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data

Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data. The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections). A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.

Considerations for CT Number Calibration and Imaging Parameter Selection for Adaptive Planning on a Novel On-Board Helical kVCT System

<h3>Purpose/Objective(s)</h3> To evaluate the impact of CT number calibration and imaging parameters on adaptive dose calculation accuracy relative to the CT planning process for an on-board helical kVCT imaging system used in helical tomotherapy treatments. <h3>Materials/Methods</h3> Helical kVCT calibrations were performed with thorax protocols at 120 kVp for <i>Fine</i> and <i>Normal</i> imaging modes using a standard electron density phantom. For this system, <i>Mode</i> determines the longitudinal beam width at isocenter, couch speed, and views per rotation, which impact the scatter signal, scan time, and patient dose, respectively, and offers <i>Fine</i> (50 mm beam width, long relative scan time, high relative mAs/rotation), <i>Normal</i> (100 mm, moderate scan time, moderate mAs/rotation), and <i>Coarse</i> (∼140 mm, short scan time, low mAs/rotation) mode options. An SBRT treatment plan was simulated for CT simulation scans of an anthropomorphic lung phantom and transferred to on-board helical kVCT scans with varying imaging and calibration protocols, and clinically relevant DVH metrics and dose-difference maps were used to compare the calculated dose distributions. <h3>Results</h3> Use of <i>Fine</i> mode for helical kVCT calibration resulted in smaller dose-volume deviations relative to the planning CT, with the magnitude of these deviations dependent on the mode used to acquire the scan of the lung phantom. For scans acquired under <i>Fine</i> mode, use of <i>Fine</i> mode calibration showed average differences in target volume DVH metrics of ± 1.0% (-1.7% max difference) compared to ± 2.5% (-3.6% max difference) with application of <i>Normal</i> mode calibration for the same images. Likewise, for phantom scans acquired under <i>Normal</i> mode, these differences were ± 1.2% (+ 2.1%) for <i>Fine</i> calibration compared to ± 1.4% (-2.1%) for <i>Normal</i> calibration, while for phantom scans acquired under <i>Coarse</i> mode, differences were ± 2.0% (-4.1%) and ± 3.3% (-6.2%), respectively. Dose-difference maps confirmed the greatest deviations in the calculated dose distributions occurred in the high-dose, target volume region, though some differences were also observed at the tissue-air interface of the chest wall. Visual differences relative to the planning CT were lessened with use of <i>Fine</i> mode acquisitions of both the calibration and lung phantoms. <h3>Conclusion</h3> Use of <i>Fine</i> mode for helical kVCT number calibration and image acquisition resulted in dose calculations most consistent with those calculated on diagnostic-quality CT images. If excess patient dose from additional imaging is a concern, <i>Normal</i> or <i>Coarse</i> mode image acquisitions with application of <i>Fine</i> mode calibration provided dose calculation accuracies within a reasonable tolerance as well. Also, while <i>Fine</i> mode increases scan times relative to other modes, this additional time is still significantly lessened compared to on-board CBCT or MVCT systems currently used for CT-based online adaptive planning.

Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images.

PurposeThe aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS).Materials and MethodsA retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm.ResultsThe mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. ConclusionThe artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.

Rotational positional error-corrected linear set-up margin calculation technique for lung stereotactic body radiotherapy in a dual imaging environment of 4-D cone beam CT and ExacTrac stereoscopic imaging.

Accurate calculation of set-up margin is a prerequisite to arrive at the most optimal clinical to planning target volume margin. The aim of this study was to evaluate the compatibility of different on-board and in-room stereoscopic imaging modalities by calculating the set-up margins (SM) in stereotactic body radiotherapy technique accounting and unaccounting for rotational positional errors (PE). Further, we calculated separate SMs one based on residual positional errors and another based on residual + intrafraction positional errors from the imaging data obtained in a dual imaging environment. A total of 22 lung cancer patients were included in this study. For primary image guidance, four-dimensional cone beam computed tomography (4-D CBCT) was used and stereoscopic ExacTrac was used as the auxiliary imaging. Following table position correction (TPC) based on the initial 4-D CBCT, another 4-D CBCT (post-TPC) and a pair of stereoscopic ExacTrac images were obtained. Further, during the treatment delivery, a series of ExacTrac images were acquired to identify the intrafraction PE. If a, b and c were the observed translational shifts in lateral (x-axis), longitudinal (y-axis) and vertical direction (z-axis) and α, β and γ were the rotational shifts in radians about the same axes, respectively, then the resultant translational vectors (A, B and C) were calculated on the basis of translational and rotational values. Set-up margins were calculated using residual errors post-TPC only and also using intrafraction positional errors in addition to the residual errors. Residual and residual + intrafraction SM were calculated from a dataset of 82 CBCTs and 189 ExacTrac imaging sessions. CBCT-based mean ± SD shifts in translational and rotational directions were 0.3 ± 1.8mm, 0.1 ± 1.8mm, - 0.4 ± 1.6mm, 0.1 ± 0.4°, 0.0 ± 1.0° and 0.3 ± 0.7°, respectively, and for ExacTrac - 0.1 ± 1.8mm, 0.2 ± 2.4mm, - 0.6 ± 1.8mm, 0.1 ± 1.2°, - 0.2 ± 1.3° and - 0.1 ± 0.6°, respectively. Residual SM without considering the rotational correction in x, y and z directions were 5.0mm, 4.5mm and 4.4mm; rotation-corrected SM were 4.4mm, 4.0mm and 5.5mm, respectively. Residual plus intrafraction SM were 5.5mm, 6.6mm and 6.2mm without considering the rotational corrections, whereas they were 5.0mm, 6.3mm and 6.2mm with rotational errors accounted for. Accurate calculation of set-up margin is required to find the clinical to planning target volume margin. Primary and auxiliary imaging margins fall in the range of 4.0 to 5.5mm and 5.0 to 7.0mm, respectively, indicating a higher SM for X-ray-based planar imaging techniques over three-dimensional cone beam images. This study established the degree of mutual compatibility between two different kinds of widely used set-up imaging modalities, on-board CBCT and in-room stereoscopic imaging ExacTrac. It also describes the technique to calculate the residual and residual plus intrafraction SM and its variation in a dual imaging environment accounting for rotational PE in stereotactic body radiotherapy of lung.

PO-1717: 4D-segmentation-based anatomy-restricted motion-compensated reconstruction of on-board 4D CBCT scans

PO-1717: 4D-segmentation-based anatomy-restricted motion-compensated reconstruction of on-board 4D CBCT scans

AirNet: Fused Analytical and Iterative Image Reconstruction Method With Deep Learning Regularization for High-Quality Sparse-Data On-Board CBCT

AirNet: Fused Analytical and Iterative Image Reconstruction Method With Deep Learning Regularization for High-Quality Sparse-Data On-Board CBCT

Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN)

Purpose: Previously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, the iterative deformable registration method used for deforming edges from planning-CT to on-board CBCT is time-consuming and user-dependent. This study aims to automate and accelerate PCTV reconstruction by developing an unsupervised CNN model to bypass the conventional deformable registration. Methods: The new method uses unsupervised CNN model for deformation prediction and PCTV reconstruction. An unsupervised CNN model with a u-net structure was used to predict deformation vector fields (DVF) to generate on-board contours for PCTV reconstruction. Paired 3D image volumes of prior CT and on-board CBCT are inputs and DVF are predicted without the need of ground truths. The model was initially trained on brain MRI images, and then fine-tuned using our lung SBRT data. This method was evaluated using lung SBRT patient data. In the intra-patient study, the first n−1 day’s CBCTs are used for CNN training to predict nth day edge information (n = 2, 3, 4, 5). 45 half-fan projections covering 360˚ from nth day CBCT is used for reconstruction. In the inter-patient study, the 10 patient images including CT and first day’s CBCT are used for training. Results from Edge-preserving (EPTV), PCTV and PCTV-CNN are compared. Results: The cross-correlations of the predicted edge map and the ground truth were on average 0.88 for both intra-patient and inter-patient studies. PCTV-CNN achieved comparable image quality as PCTV while automating the registration process and reducing the registration time from 1–2 min to 1.4 s. Conclusion: It is feasible to use an unsupervised CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low-dose CBCT to improve the precision of on-board target localization and adaptive radiotherapy.

First clinical retrospective investigation of limited projection CBCT for lung tumor localization in patients receiving SBRT treatment

To clinically investigate the limited-projection CBCT (LP-CBCT) technology for daily positioning of patients receiving breath-hold lung SBRT radiation treatment and to investigate the feasibility of reconstructing fast 4D-CBCT from 1 min 3D-CBCT scan.Eleven patients who underwent breath-hold lung SBRT radiation treatment were scanned daily with on-board full-projection CBCT (CBCT) using half-fan scan. A subset of the CBCT projections and the prior planning CT were used to estimate the LP-CBCT images using the weighted free-form deformation method. The limited projections are clusteringly sampled within fifteen sub-angles in 360° in order to simulate the fast 1 min scan for 4D-CBCT. The estimated LP-CBCTs were rigidly registered to the planning CT to determine the clinical shifts needed for patient setup corrections, which were compared with shifts determined by the CBCT for evaluation. Both manual and automatic registrations were performed in order to compare the systematic registration errors. Fifty CBCT volumes were obtained from the eleven patients in fifty fractions for this pilot clinical study.For the CBCT images, the mean (±standard deviation) shifts between CBCT and planning CT from manual registration in left–right (LR), anterior–posterior (AP), and superior–inferior (SI) directions are 1.1 ± 1.2 mm, 2.1 ± 1.9 mm, 5.2 ± 3.6 mm, respectively. The mean deviation difference between shifts determined by CBCT and LP-CBCT images are 0.3 ± 0.5 mm, 0.5 ± 0.8 mm, 0.4 ± 0.3 mm, in LR, AP, and SI directions, respectively. The mean vector length of CBCT shift for all fractions is 6.1 ± 3.6 mm, and the mean vector length difference between CBCT and LP-CBCT for all fractions studied is 1.0 ± 0.9 mm. The automatic registrations yield similar results as manual registrations.The pilot clinical study shows that LP-CBCT localization offers comparable accuracy to CBCT localization for daily tumor positioning while reducing the projection number to 1/10 for patients receiving breath hold lung radiation treatment. The cluster projection sampling in this study also shows the feasibility of reconstructing fast 4D-CBCT from 1 min 3D-CBCT scan.

Low dose CBCT reconstruction via prior contour based total variation (PCTV) regularization: a feasibility study

Purpose: compressed sensing reconstruction using total variation (TV) tends to over-smooth the edge information by uniformly penalizing the image gradient. The goal of this study is to develop a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT.Methods: the edge information is extracted from prior planning-CT via edge detection. Prior CT is first registered with on-board CBCT reconstructed with TV method through rigid or deformable registration. The edge contours in prior-CT is then mapped to CBCT and used as the weight map for TV regularization to enhance edge information in CBCT reconstruction. The PCTV method was evaluated using extended-cardiac-torso (XCAT) phantom, physical CatPhan phantom and brain patient data. Results were compared with both TV and edge preserving TV (EPTV) methods which are commonly used for limited projection CBCT reconstruction. Relative error was used to calculate pixel value difference and edge cross correlation was defined as the similarity of edge information between reconstructed images and ground truth in the quantitative evaluation.Results: compared to TV and EPTV, PCTV enhanced the edge information of bone, lung vessels and tumor in XCAT reconstruction and complex bony structures in brain patient CBCT. In XCAT study using 45 half-fan CBCT projections, compared with ground truth, relative errors were 1.5%, 0.7% and 0.3% and edge cross correlations were 0.66, 0.72 and 0.78 for TV, EPTV and PCTV, respectively. PCTV is more robust to the projection number reduction. Edge enhancement was reduced slightly with noisy projections but PCTV was still superior to other methods. PCTV can maintain resolution while reducing the noise in the low mAs CatPhan reconstruction. Low contrast edges were preserved better with PCTV compared with TV and EPTV.Conclusion: PCTV preserved edge information as well as reduced streak artifacts and noise in low dose CBCT reconstruction. PCTV is superior to TV and EPTV methods in edge enhancement, which can potentially improve the localization accuracy in radiation therapy.

Motion guided Spatiotemporal Sparsity for high quality 4D-CBCT reconstruction

Conventional cone-beam computed tomography is often deteriorated by respiratory motion blur, which negatively affects target delineation. On the other side, the four dimensional cone-beam computed tomography (4D-CBCT) can be considered to describe tumor and organ motion. But for current on-board CBCT imaging system, the slow rotation speed limits the projection number at each phase, and the associated reconstructions are contaminated by noise and streak artifacts using the conventional algorithm. To address the problem, we propose a novel framework to reconstruct 4D-CBCT from the under-sampled measurements—Motion guided Spatiotemporal Sparsity (MgSS). In this algorithm, we try to divide the CBCT images at each phase into cubes (3D blocks) and track the cubes with estimated motion field vectors through phase, then apply regional spatiotemporal sparsity on the tracked cubes. Specifically, we recast the tracked cubes into four-dimensional matrix, and use the higher order singular value decomposition (HOSVD) technique to analyze the regional spatiotemporal sparsity. Subsequently, the blocky spatiotemporal sparsity is incorporated into a cost function for the image reconstruction. The phantom simulation and real patient data are used to evaluate this algorithm. Results show that the MgSS algorithm achieved improved 4D-CBCT image quality with less noise and artifacts compared to the conventional algorithms.

Liver CBCT Reconstruction by Prior-Knowledge Guided Motion Modeling and Biomechanical Modeling

We developed a new liver CBCT reconstruction technique, which solves a deformation vector field (DVF) to deform a prior high-quality liver CT/CBCT to the new CBCT. The DVF also enables fast on-board liver tumor tracking, dose warping, and adaptive radiation therapy. The new liver CBCT reconstruction technique is based on the foundation of “2D-3D deformation,” which solves the DVF by matching 2D projections simulated from the deformed prior image to 2D on-board CBCT projections. The accuracy of 2D-3D deformation is limited by the low-contrast liver tumors, which lack sufficient intensity variations from liver parenchyma in 2D projections. The new technique applies two strategies to enhance the accuracy of 2D-3D deformation: 1) prior-knowledge guided motion modeling; and 2) finite-element-analysis based biomechanical modeling. To improve the DVF accuracy at the liver boundary, especially for the inferior liver boundary, the liver is contoured on a prior liver 4D-CT and the contoured liver volume at each phase is then density-overridden to enhance the boundary contrasts. Deformable registration is then performed between the 4D-CT phase images to study the liver boundary motion pattern to extract a motion model. Based on the patient-specific liver motion model, an initial DVF is generated and input into 2D-3D deformation for refinement. Based on the refined DVF, biomechanical modeling is further applied to improve the intra-liver DVF accuracy. Biomechanical modeling uses the DVF at the liver boundary to drive finite element analysis, which fine-tunes the DVFs within the liver volume, including those of the tumor. The fine-tuned DVF is fed back into 2D-3D deformation to form an iterative loop until convergence. The efficacy of the reconstruction algorithm was tested on 3 liver cancer patients. Each patient had a prior CT and a new CT. On-board cone-beam projections were simulated from the new CT for reconstruction. The low-contrast liver tumors were contoured on the prior and new CT images. The new tumor contour (“ground-truth”) was compared against those propagated from the prior tumor contour by the solved DVFs. Their DICE coefficients were calculated to evaluate the reconstruction accuracy. Compared with the other techniques, the new technique generated CBCTs with the highest accuracy (Table 1) for the liver tumor.Abstract 206; Table 1The DICE coefficient between the deformed tumor volume and the “ground-truth” tumor volumePatient No.No. of projections for reconstruction2D-3D deformation2D-3D deformation w/ biomechanical modelingThe new technique150.6020.8510.924100.6220.8750.926200.6490.8920.927250.6580.8350.885100.6710.8690.889200.6710.8820.897350.4110.5630.772100.4110.5980.780200.4110.6610.802 Open table in a new tab The new technique substantially improved the accuracy of the reconstructed liver tumor volume. It can also reduce 3D/4D-CBCT imaging dose by acquiring fewer projections for reconstruction.

A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.

To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

Assessment of intraoperative 3D imaging alternatives for IOERT dose estimation

Intraoperative electron radiation therapy (IOERT) involves irradiation of an unresected tumour or a post-resection tumour bed. The dose distribution is calculated from a preoperative computed tomography (CT) study acquired using a CT simulator. However, differences between the actual IOERT field and that calculated from the preoperative study arise as a result of patient position, surgical access, tumour resection and the IOERT set-up. Intraoperative CT imaging may then enable a more accurate estimation of dose distribution. In this study, we evaluated three kilovoltage (kV) CT scanners with the ability to acquire intraoperative images. Our findings indicate that current IOERT plans may be improved using data based on actual anatomical conditions during radiation. The systems studied were two portable systems (“O-arm”, a cone-beam CT [CBCT] system, and “BodyTom”, a multislice CT [MSCT] system) and one CBCT integrated in a conventional linear accelerator (LINAC) (“TrueBeam”). TrueBeam and BodyTom showed good results, as the gamma pass rates of their dose distributions compared to the gold standard (dose distributions calculated from images acquired with a CT simulator) were above 97% in most cases. The O-arm yielded a lower percentage of voxels fulfilling gamma criteria owing to its reduced field of view (which left it prone to truncation artefacts). Our results show that the images acquired using a portable CT or even a LINAC with on-board kV CBCT could be used to estimate the dose of IOERT and improve the possibility to evaluate and register the treatment administered to the patient.

Adaptation of daily dose using CBCT imaging

The feasibility of a technique using analysis of on-board CBCT images to adapt the dose to the target on a fraction by fraction basis was investigated. A 3D phase-correlation registration algorithm was used to retrospectively register CBCT images to the planning CT for 11 patients receiving VMAT prostate treatments of 74 Gy. The original plan was recalculated on the registered CT image to provide daily dose statistics. To determine how the dose could be changed, the DVCs were used as limits such that the dose was increased until the tightest constraint was just met, or if a DVC was already broken for a given fraction the dose was reduced by the minimum amount required to ensure that the DVC was within tolerance. Three of the patients investigated could have received higher accumulative doses during their entire treatment without exceeding their OAR DVCs. In the remaining 8 patients, for only 3 fractions could an increase in dose been given while staying below the DVC limits. If all changes were made, the accumulated increase in dose possible for the three patients were 3.98 Gy, 6.89 Gy, and 7.70 Gy. Adapting the dose to be delivered to the patient on a fraction by fraction basis has the potential to allow for significant dose escalation while staying within institutional DVCs. Although it is unlikely that in the clinic the dose level would be reduced below 2 Gy per fraction, such reductions were included in the calculations here to see how it could theoretically impact the treatment.

MO-FG-CAMPUS-JeP3-03: Detection of Unpredictable Patient Movement During SBRT Using a Single KV Projection of An On-Board CBCT System: Simulation Study

Purpose: An unpredictable movement of a patient can occur during SBRT even when immobilization devices are applied. In the SBRT treatments using a conventional linear accelerator detection of such movements relies heavily on human interaction and monitoring. This study aims to detect such positional abnormalities in real-time by assessing intra-fractional gantry mounted kV projection images of a patient's spine. Methods: We propose a self-CBCT image based spine tracking method consisting of the following steps: (1)Acquire a pre-treatment CBCT image; (2)Transform the CBCT volume according to the couch correction; (3)Acquire kV projections during treatment beam delivery; (4)Simultaneously with each acquisition generate a DRR from the CBCT volume based-on the current projection geometry; (5)Perform an intensity gradient-based 2D registration between spine ROI images of the projection and the DRR images; (6)Report an alarm if the detected 2D displacement is beyond a threshold value. To demonstrate the feasibility, retrospective simulations were performed on 1,896 projections from nine CBCT sessions of three patients who received lung SBRT. The unpredictable movements were simulated by applying random rotations and translations to the reference CBCT prior to each DRR generation. As the ground truth, the 3D translations and/or rotations causing >3 mm displacement of the midpoint of the thoracic spine were regarded as abnormal. In the measurements, different threshold values of 2D displacement were tested to investigate sensitivity and specificity of the proposed method. Results: A linear relationship between the ground truth 3D displacement and the detected 2D displacement was observed (R2 = 0.44). When the 2D displacement threshold was set to 3.6 mm the overall sensitivity and specificity were 77.7±5.7% and 77.9±3.5% respectively. Conclusion: In this simulation study, it was demonstrated that intrafractional kV projections from an on-board CBCT system have a potential to detect unpredictable patient movement during SBRT. This research is funded by Interfractional Imaging Research Grant from Elekta.

Feasibility of MV CBCT-based treatment planning for urgent radiation therapy: dosimetric accuracy of MV CBCT-based dose calculations.

Unlike scheduled radiotherapy treatments, treatment planning time and resources are limited for emergency treatments. Consequently, plans are often simple 2D image‐based treatments that lag behind technical capabilities available for nonurgent radiotherapy. We have developed a novel integrated urgent workflow that uses onboard MV CBCT imaging for patient simulation to improve planning accuracy and reduce the total time for urgent treatments. This study evaluates both MV CBCT dose planning accuracy and novel urgent workflow feasibility for a variety of anatomic sites. We sought to limit local mean dose differences to less than 5% compared to conventional CT simulation. To improve dose calculation accuracy, we created separate Hounsfield unit–to–density calibration curves for regular and extended field‐of‐view (FOV) MV CBCTs. We evaluated dose calculation accuracy on phantoms and four clinical anatomical sites (brain, thorax/spine, pelvis, and extremities). Plans were created for each case and dose was calculated on both the CT and MV CBCT. All steps (simulation, planning, setup verification, QA, and dose delivery) were performed in one 30 min session using phantoms. The monitor units (MU) for each plan were compared and dose distribution agreement was evaluated using mean dose difference over the entire volume and gamma index on the central 2D axial plane. All whole‐brain dose distributions gave gamma passing rates higher than 95% for 2%/2 mm criteria, and pelvic sites ranged between 90% and 98% for 3%/3 mm criteria. However, thoracic spine treatments produced gamma passing rates as low as 47% for 3%/3 mm criteria. Our novel MV CBCT‐based dose planning and delivery approach was feasible and time‐efficient for the majority of cases. Limited MV CBCT FOV precluded workflow use for pelvic sites of larger patients and resulted in image clearance issues when tumor position was far off midline. The agreement of calculated MU on CT and MV CBCT was acceptable for all treatment sites.PACS numbers: 87.55.D‐, 87.57.Q‐

On the Accuracy of Automatic 4D Tumor Registration Between Planning CT and On-board CBCT Under Different Breathing Conditions: A Phantom Study

On the Accuracy of Automatic 4D Tumor Registration Between Planning CT and On-board CBCT Under Different Breathing Conditions: A Phantom Study

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.