Cone-beam Computed Tomography Correction Research Articles

A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter.

In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter based on a reference digitally reconstructed radiograph (DRR). A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a nonlocal means filter based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, The image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image qualityshowed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients. This article is protected by copyright. All rights reserved.

Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients.

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCTHD and CBCTHM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the Dmean, Dmax and D5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D2%, in parallel organs such as the parotid glands, the deviations from the CBCTHM plan dose were less than those of the CBCTHD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V30 value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCTHM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Deep learning-based thoracic CBCT correction with histogram matching

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.

Assessment of setup errors of IGRT combined with a six degrees of freedom bed for patients with primary rectal cancer

Objective: To investigate the effect of six degree of freedom (6-DOF) bed combined with cone beam computed tomography (CBCT) in the on-line correction of setup errors in patients with primary rectal cancer. Methods: The clinicopathological data of 17 patients with primary rectal cancer in Department of Radiotherapy, Third Hospital of Peking University from July 2013 to January 2014 were collected. There were 14 males and 3 females, a median age of 65 years. The difference of CBCT and 6-DOF bed combined with CBCT online correction of patients with positioning error were retrospectively analyzed. Results: Before position correction, the first CBCT verification of setup errors in the three translation directions including X (left and right), Y (in and out) and Z (up and down) directions were (0.06±0.25) cm, (0.13±0.40) cm and (-0.28±0.31) cm, respectively. The setup errors of RX (rotation pitch), RY(rolling) and RZ (left and right rotation) directions were (0.62±1.15)°, (-0.19±0.99)°, and (-0.34 ± 0.84)°, respectively . After correction of IGRT combined with six freedom of bed, the setup errors of translation X, Y and Z were (0.01±0.09) cm, (-0.01±0.05) cm and (-0.03±0.08) cm, respectively, and the setup errors of rotation RX, RY and RZ directions were (-0.16±0.40)°, (0.36±0.31)°and (-0.01±0.25)°, respectively. There were significant differences in translation direction (X, Y and Z direction) and rotation direction (Rx, RY and RZ) before and after 6-DOF bed combined with CBCT correction (all P<0.05). In the translation direction, the higher frequency range of Z-direction error value was 0.20-0.79 cm. In the rotation direction, the frequency range of error in Rx direction was 0.20°-2.99°. There was no significant difference between bone mode and gray scale model registration (P>0.05). With the progress of radiotherapy, the setup errors of X, Z, Rx, RY and RZ directions increased except Y direction. Conclusions: In radiotherapy, six freedom bed combined with CBCT is helpful to correct the setup errors of patients with primary rectal cancer. Six freedom bed may be used to correct the setup errors of patients with primary rectal cancer online. Image-guided radiation therapy (IGRT) is recommended for bone pattern registration in patients with rectal cancer.

Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.

The incorporation of cone-beam computed tomography (CBCT) has allowed for enhanced image-guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning-based method for generating high quality corrected CBCT (CCBCT) images is proposed. The proposed method integrates a residual block concept into a cycle-consistent adversarial network (cycle-GAN) framework, called res-cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle-GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end-to-end CBCT-to-CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning-based CBCT correction method. Overall, the MAE, PSNR, NCC, and SNU were 13.0HU, 37.5dB, 0.99, and 0.05 in the brain, 16.1HU, 30.7dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning-based method. The authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

Fast shading correction for cone-beam CT via partitioned tissue classification

The quantitative use of cone beam computed tomography (CBCT) in radiation therapy is limited by severe shading artifacts, even with system embedded correction. We recently proposed effective shading correction methods, using planning CT (pCT) as prior information to estimate low-frequency errors in either the projection domain or image domain. In this work, we further improve the clinical practicality of our previous methods by removing the requirement of prior pCT images.Clinical CBCT images are typically composed of a limited number of tissues. By utilizing the low frequency characteristic of shading distribution, we first generate a ‘shading-free’ template image by enforcing uniformity on CBCT voxels of the same tissue type via a technique named partitioned tissue classification. Only a small subset of voxels in the template image are used in the correction process to generate sparse samples of shading artifacts. Local filtration, a Fourier transform based algorithm, is employed to efficiently process the sparse errors to compute a full-field distribution of shading artifacts for CBCT correction. We evaluate the method’s performance using an anthropomorphic pelvis phantom and 6 pelvis patients.The proposed method improves the image quality of CBCT for both phantom and patients to a level matching that of pCT. On the pelvis phantom, the signal non-uniformity (SNU) is reduced from 12.11% to 3.11% and 8.40% to 2.21% on fat and muscle, respectively. The maximum CT number error is reduced from 70 to 10 HU and 73 to 11 HU on fat and muscle, respectively. On patients, the average SNU is reduced from 9.22% to 1.06% and 11.41% to 1.67% on fat and muscle, respectively. The maximum CT number error is reduced from 95 to 9 HU and 88 to 8 HU on fat and muscle, respectively. The typical processing time for one CBCT dataset is about 45 s on a standard PC.

Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations

Image intensity correction is crucial to enable cone beam computed tomography (CBCT) based radiotherapy dose calculations. This study evaluated three different deep learning based correction methods using a U-shaped convolutional neural network architecture (Unet) in terms of their photon and proton dose calculation accuracy.CT and CBCT imaging data of 42 prostate cancer patients were included. For target ground truth data generation, a CBCT correction method based on CT to CBCT deformable image registration (DIR) was used. The method yields a deformed CT called (i) virtual CT (vCT) which is used to generate (ii) corrected CBCT projections allowing the reconstruction of (iii) a final corrected CBCT image.The single Unet architecture was trained using these three different datasets: (Unet1) raw and corrected CBCT projections, (Unet2) raw CBCT and vCT image slices and (Unet3) raw and reference corrected CBCT image slices. Volumetric arc therapy (VMAT) and proton pencil beam scanning (PBS) single field uniform dose (SFUD) plans were optimized on the reference corrected image and recalculated on the obtained Unet-corrected CBCT images.The mean error (ME) and mean absolute error (MAE) for Unet1/2/3 were Hounsfield units (HU) and HU. The 1% dose difference pass rates were better than 98.4% for VMAT for 8 test patients not seen during training, with little difference between Unets. Gamma evaluation results were even better. For protons a gamma evaluation was employed to account for small range shifts, and mm pass rates for Unet1/2/3 were better than and 91%. A 3 mm range difference threshold was established. Only for Unet3 the 5th and 95th percentiles of the range difference distributions over all fields, test patients and dose profiles were within this threshold.A single Unet architecture was successfully trained using both CBCT projections and CBCT image slices. Since the results of the other Unets were poorer than Unet3, we conclude that training using corrected CBCT image slices as target data is optimal for PBS SFUD proton dose calculations, while for VMAT all Unets provided sufficient accuracy.

SU‐F‐I‐03: Correction of Intra‐Fractional Set‐Up Errors and Target Coverage Based On Cone‐Beam Computed Tomography for Cervical Cancer Patients

Purpose:The purpose of this study is to investigate the patient set‐up error and interfraction target coverage in cervical cancer using image‐guided adaptive radiotherapy (IGART) with cone‐beam computed tomography (CBCT).Methods:Twenty cervical cancer patients undergoing intensity modulated radiotherapy (IMRT) were randomly selected. All patients were matched to the isocenter using laser with the skin markers. Three dimensional CBCT projections were acquired by the Varian Truebeam treatment system. Set‐up errors were evaluated by radiation oncologists, after CBCT correction. The clinical target volume (CTV) was delineated on each CBCT, and the planning target volume (PTV) coverage of each CBCT‐CTVs was analyzed.Results:A total of 152 CBCT scans were acquired from twenty cervical cancer patients, the mean set‐up errors in the longitudinal, vertical, and lateral direction were 3.57, 2.74 and 2.5mm respectively, without CBCT corrections. After corrections, these were decreased to 1.83, 1.44 and 0.97mm. For the target coverage, CBCT‐CTV coverage without CBCT correction was 94% (143/152), and 98% (149/152) with correction.Conclusion:Use of CBCT verfication to measure patient setup errors could be applied to improve the treatment accuracy. In addition, the set‐up error corrections significantly improve the CTV coverage for cervical cancer patients.

Inter- and intrafractional setup errors and baseline shifts of fiducial markers in patients with liver tumors receiving free-breathing postoperative radiation analyzed by cone-beam computed tomography.

This study was to evaluate the interfractional and intrafractional setup errors and baseline shifts of golden fiducial markers in patients receiving postoperative radiotherapy (RT) using cone‐beam computed tomography (CBCT) in order to calculate PTV margins for patients with liver cancer. Twelve patients with liver tumors underwent postoperative RT. CBCT images were acquired before and after the treatment. Off‐line vertebral body match and fiducial marker match were used, respectively. The results of vertebral body match represented the setup errors of the patients, while the results of fiducial marker match represented the absolute position errors of the target volume. Baseline shifts of the target volume were calculated as the absolute target position errors minus setup errors. A total of 12 patients with 214 acquisitions of CBCTs were analyzed. Both Σ and σ of setup errors and baseline shifts in left–right (L/R), superior–inferior (S/I), and anterior–posterior(A/P) directions were calculated, including interfractional and intrafractional uncertainties. Planning target volume (PTV) margins were calculated according to margin=2.5Σ+0.7σ. Margins of 1.8 mm, 3.8 mm, and 1.4 mm in L/R, S/I, and A/P directions are needed to compensate intrafractional errors when daily online CBCT correction is used. When CBCT correction with no action level (NAL) protocol is used, PTV margin should be 2.6 mm, 5.9 mm, and 2.6 mm in L/R, S/I, and A/P directions. Margins of 5.5 mm, 14.6 mm, and 7.2 mm were needed to compensate the baseline shifts when electronic portal imaging devices (EPID) or CBCT with bone match is used for online correction of setup error.PACS number: 87.55.‐x

Digital tomosynthesis (DTS) for verification of target position in early stage lung cancer patients

The ability to verify intrafraction tumor position is clinically useful for hypofractionated treatments. Short arc kV digital tomosynthesis (DTS) could facilitate more frequent target verification. The authors used DTS combined with triangulation to determine the mean temporal position of small-volume lung tumor targets treated with stereotactic radiotherapy. DTS registration results were benchmarked against online clinical localization using registration between free-breathing cone-beam computed tomography (CBCT) and the average intensity projection (AvIP) of the planning 4DCT. In this retrospective study, 76 sets of kV-projection images from online CBCT scans of 13 patients were used to generate DTS image slices (CB-DTS) with nonclinical research software (DTS Toolkit, Varian Medical Systems). Three-dimensional tumor motion was 1.3-4 mm in six patients and 6.1-25.4 mm in seven patients on 4DCT (significant difference in the mean of the groups, P < 0.01). The 4DCT AvIP was used to digitally reconstruct the Reference-DTS. DTS registration and DTS registration combined with triangulation were investigated. Progressive shortening of total DTS arc lengths from 95° to 35° around 0° gantry position was evaluated for different scenarios: DTS registration using the entire arc; DTS registration plus triangulation using two nonoverlapping arcs; and for 55° and 45° total gantry rotation, DTS registration plus triangulation using two overlapping arcs. Finally, DTS registration plus triangulation performed at eight gantry angles, each separated by 45° was evaluated using full fan kV projection data for one patient with an immobile tumor and five patients with mobile tumors. For DTS registration alone, shortening arc length did not influence accuracy in X- and Y-directions, but in Z-direction, mean deviations from online CBCT localization systematically increased for shorter arc length (P < 0.05). For example, using a 95° arc mean DTS-CBCT difference was 0.8 mm (1 SD = 0.6 mm) and for a 35° arc the mean was 2.4 mm (1 SD = 1.7 mm). DTS plus triangulation using nonoverlapping-arcs increased accuracy in Z-direction for tested arc lengths ≤55° (P < 0.01). Overlapping arcs increased accuracy in Y-direction for tumors with motion >4 mm (P < 0.02) but increased Z-direction accuracy was only observed with 55° total gantry rotation. The 95th percentile deviations with this overlapping technique in X-, Y-, and Z-directions were 1.3, 2.0, and 2.5 mm, respectively. For the five patients with mobile tumors where DTS + triangulation was performed with 45° intervals, the pooled deviation from online CBCT correction showed, for X-, Y-, and Z-directions, mean of 1.1 mm, standard deviations (SD) of 0.9, 1.0, and 0.9 mm, respectively. The mean + 2 SD was <3 mm for each direction. Short-arc DTS verification of time averaged lung tumor position is feasible using free-breathing kV projection data and the AvIP of the 4DCT as a reference. Observed differences between DTS and online CBCT registration with AvIP were ≤3 mm (mean + 2 SD), however, the increased temporal resolution of DTS + triangulation also identified short period deviations from the average target position on the CBCT. Short-arc DTS appears promising for intrafraction tumor position monitoring during stereotactic lung radiotherapy delivered with a rotational technique.

Intrafraction Variation of Mean Tumor Position During Image-Guided Hypofractionated Stereotactic Body Radiotherapy for Lung Cancer

Prolonged delivery times during daily cone-beam computed tomography (CBCT)-guided lung stereotactic body radiotherapy (SBRT) introduce concerns regarding intrafraction variation (IFV) of the mean target position (MTP). The purpose of this study was to evaluate the magnitude of the IFV-MTP and to assess target margins required to compensate for IFV and postonline CBCT correction residuals. Patient, treatment, and tumor characteristics were analyzed with respect to their impact on IFV-MTP. A total of 126 patients with 140 tumors underwent 659 fractions of lung SBRT. Dose prescribed was 48 or 60 Gy in 12 Gy fractions. Translational target position correction of the MTP was performed via onboard CBCT. IFV-MTP was measured as the difference in MTP between the postcorrection CBCT and the posttreatment CBCT excluding residual error. IFV-MTP was 0.2 ± 1.8 mm, 0.1 ± 1.9 mm, and 0.01 ± 1.5 mm in the craniocaudal, anteroposterior, and mediolateral dimensions and the IFV-MTP vector was 2.3 ± 2.1 mm. Treatment time and excursion were found to be significant predictors of IFV-MTP. An IFV-MTP vector greater than 2 and 5 mm was seen in 40.8% and 7.2% of fractions, respectively. IFV-MTP greater than 2 mm was seen in heavier patients with larger excursions and longer treatment times. Significant differences in IFV-MTP were seen between immobilization devices. The stereotactic frame immobilization device was found to be significantly less likely to have an IFV-MTP vector greater than 2 mm compared with the alpha cradle, BodyFIX, and hybrid immobilization devices. Treatment time and respiratory excursion are significantly associated with IFV-MTP. Significant differences in IFV-MTP were found between immobilization devices. Target margins for IFV-MTP plus post-correction residuals are dependent on immobilization device with 5-mm uniform margins being acceptable for the frame immobilization device.

Dosimetric Impact of Online Correction via Cone-Beam CT-Based Image Guidance for Stereotactic Lung Radiotherapy

To evaluate the dosimetric impact of online cone-beam computed tomography (CBCT) guided correction in lung stereotactic body radiation therapy (SBRT). Twenty planning and 162 CBCT images from 20 patients undergoing lung SBRT were analyzed. The precorrection CBCT (CBCT after patient setup, no couch correction) was registered to planning CT using soft tissue; couch shift was applied, with a second CBCT for verification (postcorrection CBCT). Targets and normal structures were delineated on CBCTs: gross tumor volume (GTV), clinical target volume (CTV), cord, esophagus, lung, proximal bronchial tree, and aorta. Dose distributions on all organs manifested on each CBCT were compared with those planned on the CT. Without CBCT guided target position correction, target dose reduced with respect to treatment plan. Mean and standard deviation of treatment dose discrepancy from the plan were -3.2% (4.9%), -2.1% (4.4%), -6.1% (10.7%), and -3.5% (7%) for GTV D(99%), GTV D(95%), CTV D(99%), and CTV D(95%), respectively. With CBCT correction, the results were -0.4% (2.6%), 0.1% (1.7%), -0.3% (4.2%), and 0.5% (3%). Mean and standard deviation of the difference in normal organ maximum dose were 2.2% (6.5%) before correction and 2.4% (5.9%) after correction for esophagus; 6.1% (14.1%) and 3.8% (8.1%) for cord; 3.1% (17.5%) and 6.2% (9.8%) for proximal bronchial tree; and 17.7% (19.5%) and 14.1% (17%) for aorta. Online CBCT guidance improves the accuracy of target dose delivery for lung SBRT. However, treatment dose to normal tissue can vary regardless of the correction. Normal tissues should be considered during target registration, according to target proximity.

Breast Patient Setup Error Assessment: Comparison of Electronic Portal Image Devices and Cone-Beam Computed Tomography Matching Results

To quantify the differences in setup errors measured with the cone-beam computed tomography (CBCT) and electronic portal image devices (EPID) in breast cancer patients. Repeat CBCT scan were acquired for routine offline setup verification in 20 breast cancer patients. During the CBCT imaging fractions, EPID images of the treatment beams were recorded. Registrations of the bony anatomy for CBCT to planning CT and EPID to digitally reconstructed-radiographs (DRRs) were compared. In addition, similar measurements of an anthropomorphic thorax phantom were acquired. Bland-Altman and linear regression analysis were performed for clinical and phantom registrations. Systematic and random setup errors were quantified for CBCT and EPID-driven correction protocols in the EPID coordinate system (U, V), with V parallel to the cranial-caudal axis and U perpendicular to V and the central beam axis. Bland-Altman analysis of clinical EPID and CBCT registrations yielded 4 to 6-mm limits of agreement, indicating that both methods were not compatible. The EPID-based setup errors were smaller than the CBCT-based setup errors. Phantom measurements showed that CBCT accurately measures setup error whereas EPID underestimates setup errors in the cranial-caudal direction. In the clinical measurements, the residual bony anatomy setup errors after offline CBCT-based corrections were Σ(U) = 1.4 mm, Σ(V) = 1.7 mm, and σ(U) = 2.6 mm, σ(V) = 3.1 mm. Residual setup errors of EPID driven corrections corrected for underestimation were estimated at Σ(U) = 2.2mm, Σ(V) = 3.3 mm, and σ(U) = 2.9 mm, σ(V) = 2.9 mm. EPID registration underestimated the actual bony anatomy setup error in breast cancer patients by 20% to 50%. Using CBCT decreased setup uncertainties significantly.

The clinical feasibility and effect of online cone beam computer tomography-guided intensity-modulated radiotherapy for nasopharyngeal cancer

Background and purpose Online adaptive correction in image-guided intensity-modulated radiotherapy appeared to be a promising approach for precision radiation treatment in head and neck tumors. This protocol was designed to evaluate the clinical feasibility and effect of online cone beam computed tomography (CBCT) guidance in IMRT of nasopharyngeal cancer (NPC). Methods and materials The Elekta Synergy system, which integrates an X-ray volumetric imager (XVI), was used to deliver radiation treatment for 22 cases of NPC. The acquired CBCT was registered to the planning CT for online and offline analysis. The systematic and random setup errors, as well as planning target volume (PTV) margin, were calculated at different correction threshold levels. The impact of online setup correction on dosimetry was evaluated by simulation of pre-correction errors. Results The correction-of-setup-errors frequencies for 1, 2 and 3 mm thresholds were 41.3–53.9%, 12.7–21.2% and 6.3–10.3%, respectively. Online correction was effective at the 2 mm threshold level for all three axes. The pre-correction systematic errors for the whole group ranged 1.1–1.3 mm, and the random errors were also 1.1–1.3 mm. After online correction, the systematic and random errors ranged 0.4–0.5 mm and 0.7–0.8 mm, respectively, in the three directions. The PTV margins for the pre-correction, pretreatment and post-treatment positions were 3.5–4.2 mm, 1.6–1.8 mm and 2.5–3.2 mm, respectively, in three directions. Analysis of hypothetical dosimetric change due to a translational isocenter shift of 3 mm showed that if no correction was applied, the mean maximum dose to both the brain stem and spinal cord would be increased by 10 Gy, the mean dose to the left and right parotids would be increased by 7.8 and 8.5 Gy, respectively, and the dose to target volumes would be decreased: 4 Gy for 95% GTV and 5.6 Gy for 95% CTV 60. Conclusions CBCT-based online correction increased the accuracy of IMRT for NPC and reduced irradiated margins, by decreasing both the systematic and random errors. Online CBCT correction reduces the radiation dose to normal tissue and creates room for further dose escalation of tumors.

Analysis of Online Cone-beam CT Images for Non-small Cell Lung Cancer to Define an Appropriate CTV-to-PTV Margins

Kilovoltage cone-beam computed tomography (CBCT) has been developed to provide accurate soft tissue and bony setup information. We evaluated the magnitude of interfraction and intrafraction tumor displacement using online CBCT for non-small-cell lung cancer (NSCLC) patients and estimated the appropriate planning target volume (PTV) margins. Ten NSCLC patients treated with three-dimensional conformal radiotherapy (3D-CRT) were investigated. The study procedure involved (1) initial patient setup using laser alignment to skin marks, (2) initial CBCT acquisition and analysis of these images with planning CT images using automatic and manual registration to assess the interfraction tumor displacement (setup error), (3) couch translation based on the registration results accordingly and treatment delivery, (4) a second CBCT were acquired on couch immediately after treatment to assess intrafraction tumor displacement. Online-guided correction data were recorded in the left-right (LR), cranio-caudal (CC), and anterior-posterior (AP) directions respectively. Each patient underwent this procedure 2∼3 times per week over the course of their treatment. The precorrection and postcorrection PTV margins were calculated using the 2.0 Σ+0.7σ recipe. The criterion to derive the recipe was that on average more than 99% of the CTV should at least get 95% of the dose. Interfraction (Σ inter, σ inter)and intrafraction errors (Σ intra, σ intra) were all involved in the calculation of the precorrection PTV margins, while postcorrection PTV margins accounts for intrafraction errors only. The complete procedure took approximately 6∼8 min, including deployment of CBCT components, scanning, reconstruction, images registration, and couch correction. Total of 159 sets of CBCT images were analyzed. The precorrection PTV margins required to account for total errors are 7.2 mm, 8.8 mm and 10.1 mm in the LR, CC and AP directions, respectively. When the tumor was corrected every fraction, the postcorrection PTV margins required to account for intrafraction errors are 2 mm,1.6 mm,1.6 mm in the LR, CC and AP directions respectively. There exists some extent of errors from setup and organ motion in 3D CRT for NSCLC. The approach based on the CBCT measurements can be used to reduce the impact of setup errors obviously and to estimate the PTV margins. When no CBCT correction was applied, the PTV margins should be 7 mm, 9 mm and 10 mm in the LR, CC and AP directions respectively, and if there is correction before treatment, the PTV margins were 2 mm in every direction.