Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy

Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study

OBJECTIVES

The respiratory variations will lead to inconsistency between the actual delivery dose and the planning dose. How the minor interfractional amplitude changes affect the geometry and dose delivery accuracy remains to be investigated in the context of lung adaptive radiotherapy.

METHODS

Planning 4-dimensional-computed tomography and kV-cone beam computed tomography were scanned based on the Computerized Imaging Reference Systems phantom, which was employed to simulate the minor interfractional amplitude variations. The corresponding synthetic computed tomography for a particular motion pattern can be generated from Velocity program. Then a clinically meaningful synthetic computed tomography was analyzed through the geometrical and dosimetric assessment.

RESULTS

The image quality of synthetic computed tomography was improved obviously compared with cone beam computed tomography. Mean absolute error was minimized when no significant interfractional motion occurs and Velocity can be qualified for dealing with the regular breathing motion patterns. The mean percent hounsfield unit difference of the synthetic hounsfield unit values per organ relative to the planning 4-dimensional-computed tomography image was 22.3%. Under the same conditions, the mean percent hounsfield unit difference of the cone beam computed tomography hounsfield unit values per organ, relative to the planning 4-dimensional-computed tomography image was 83.9%. Overall, the accuracy of hounsfield unit in synthetic computed tomography was improved obviously and the variability of the synthetic image correlates with the planning 4-dimensional-computed tomography image variability. Meanwhile, the dose-volume histograms between planning 4-dimensional-computed tomography and synthetic computed tomography almost coincided each other, which indicates that Velocity program can qualify lung adaptive radiotherapy well when there were no interfractional respiratory variations. However, for cases with obvious interfractional amplitude change, the volume covered at least by 100% of the prescription dose was only 59.6% for that synthetic image.

CONCLUSION

The synthetic computed tomography images generated from Velocity were close to the real images in anatomy and dosimetry, which can make clinical lung adaptive radiotherapy possible based on the actual patient anatomy during treatment.

Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN)

4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images reconstructed by compressed sensing (CS)-based methods suffer from blurred edges. Introducing the average-4D-image constraint to the CS-based reconstruction, such as prior-image-constrained CS (PICCS), can improve the edge sharpness of the stable structures. However, PICCS can lead to motion artifacts in the moving regions. In this study, we proposed a dual-encoder convolutional neural network (DeCNN) to realize the average-image-constrained 4D-CBCT reconstruction. The proposed DeCNN has two parallel encoders to extract features from both the under-sampled target phase images and the average images. The features are then concatenated and fed into the decoder for the high-quality target phase image reconstruction. The reconstructed 4D-CBCT using of the proposed DeCNN from the real lung cancer patient data showed (1) qualitatively, clear and accurate edges for both stable and moving structures; (2) quantitatively, low-intensity errors, high peak signal-to-noise ratio, and high structural similarity compared to the ground truth images; and (3) superior quality to those reconstructed by several other state-of-the-art methods including the back-projection, CS total-variation, PICCS, and the single-encoder CNN. Overall, the proposed DeCNN is effective in exploiting the average-image constraint to improve the 4D-CBCT image quality.

Real-time Markerless Tracking of Lung Tumors based on 2-D Fluoroscopy Imaging using Convolutional LSTM

Purpose

To investigate the feasibility of tracking targets in 2D fluor images using a novel deep learning network.

Methods

Our model design aims to capture the consistent motion of tumors in fluoroscopic images by neural network. Specifically, the model is trained by generative adversarial methods. The network is a coarse-to-fine architecture design. Convolutional LSTM (Long Short-term Memory) modules are introduced to account for the time correlation between different frames of the fluoroscopic images. The model was trained and tested on a digital X-CAT phantom in two studies. Series of coherent 2D fluoroscopic images representing the full respiration cycle were fed into the model to predict the localized tumor regions. In first study to test on massive scenarios, phantoms of different scales, tumor positions, sizes, and respiration amplitudes were generated to evaluate the accuracy of the model comprehensively. In second study to test on specific sample, phantoms were generated with fixed body and tumor sizes but different respiration amplitudes to investigate the effects of motion amplitude on the tracking accuracy. The tracking accuracy was quantitatively evaluated using intersection over union (IOU), tumor area difference, and centroid of mass difference (COMD).

Results

In the first comprehensive study, the mean IOU and dice coefficient achieved 0.93±0.04 and 0.96±0.02. The mean tumor area difference was 4.34%±4.04%. And the COMD was 0.16 cm and 0.07 cm on average in SI (superior-interior) and LR (left-right) directions, respectively. In the second amplitude study, the mean IOU and dice coefficient achieved 0.98 and 0.99. The mean tumor difference was 0.17%. And the COMD was 0.03cm and 0.01 cm on average in SI and LR directions, respectively. Results demonstrated the robustness of our model against breathing variations.

Conclusion

Our study showed the feasibility of using deep learning to track targets in x-ray fluoroscopic projection images without the aid of markers. The technique can be valuable for both pre- and during-treatment real-time target verification using fluoroscopic imaging in lung SBRT treatments.

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network

Background

Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation.

Methods

In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. Instead of learning the patient-specific structures, the proposed model learns a generalized pattern of utilizing the patient-specific information in the prior images to facilitate the under-sampled image enhancement. Specifically, the MeCNN consists of a merging-encoder and a decoder. The merging-encoder extracts image features from both the prior CT images and the under-sampled CBCT images, and merges the features at multi-scale levels via deep convolutions. The merged features are then connected to the decoders via shortcuts to yield high-quality CBCT images. The proposed model was tested on both the simulated CBCTs and the clinical CBCTs. The predicted CBCT images were evaluated qualitatively and quantitatively in terms of image quality and tumor localization accuracy. Mann-Whitney U test was conducted for the statistical analysis. P<0.05 was considered statistically significant.

Results

The proposed model yields CT-like high-quality CBCT images from only 36 half-fan projections. Compared to other methods, CBCT images augmented by the proposed model have significantly lower intensity errors, significantly higher peak signal-to-noise ratio, and significantly higher structural similarity with respect to the ground truth images. Besides, the proposed method significantly reduced the 3D distance of the CBCT-based tumor localization errors. In addition, the CBCT augmentation is nearly real-time.

Conclusions

With the prior-image guidance, the proposed method is effective in reconstructing high-quality CBCT images from the highly under-sampled projections, considerably reducing the imaging dose and improving the clinical utility of the CBCT.

A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms

Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated.Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along thex/y/zdirection in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm.Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies.

Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model

Digital tomosynthesis (DTS) has been proposed as a fast low-dose imaging technique for image-guided radiation therapy (IGRT). However, due to the limited scanning angle, DTS reconstructed by the conventional FDK method suffers from significant distortions and poor plane-to-plane resolutions without full volumetric information, which severely limits its capability for image guidance. Although existing deep learning-based methods showed feasibilities in restoring volumetric information in DTS, they ignored the inter-patient variabilities by training the model using group patients. Consequently, the restored images still suffered from blurred and inaccurate edges. In this study, we presented a DTS enhancement method based on a patient-specific deep learning model to recover the volumetric information in DTS images. The main idea is to use the patient-specific prior knowledge to train the model to learn the patient-specific correlation between DTS and the ground truth volumetric images. To validate the performance of the proposed method, we enrolled both simulated and real on-board projections from lung cancer patient data. Results demonstrated the benefits of the proposed method: (1) qualitatively, DTS enhanced by the proposed method shows CT-like high image quality with accurate and clear edges; (2) quantitatively, the enhanced DTS has low-intensity errors and high structural similarity with respect to the ground truth CT images; (3) in the tumor localization study, compared to the ground truth CT-CBCT registration, the enhanced DTS shows 3D localization errors of ≤0.7 mm and ≤1.6 mm for studies using simulated and real projections, respectively; and (4), the DTS enhancement is nearly real-time. Overall, the proposed method is effective and efficient in enhancing DTS to make it a valuable tool for IGRT applications.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN)

Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were [Formula: see text] and [Formula: see text], compared with that of real CT ([Formula: see text]) and CBCT ([Formula: see text]). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials.

Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy.

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.

Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study

To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRIprior. Principal component analysis (PCA) is used to extract three major respiratory deformation modes from the DFMs generated between the prior and remaining phases. VC-MRI at each time-step is considered a deformation of MRIprior, where the DFM is represented as a weighted linear combination of the PCA components. The PCA weightings are solved by minimizing the differences between on-board 2D cine MRI and its corresponding VC-MRI slice. The PCA weightings solved during the initial training period are used to train an ADMLP-NN to predict PCA weightings ahead of time during the prediction period. The predicted PCA weightings are used to build predicted 3D DFM and ultimately, predicted VC-MRIs for 4D target tracking. The method was evaluated using a 4D computerized phantom (XCAT) with patient breathing curves and MRI data from a real liver cancer patient. Effects of breathing amplitude change and ADMLP-NN parameter variations were assessed. The accuracy of the PCA curve prediction was evaluated. The predicted real-time 3D tumor was evaluated against the ground-truth using volume dice coefficient (VDC), center-of-mass-shift (COMS), and target tracking errors. For the XCAT study, the average VDC and COMS for the predicted tumor were 0.92  ±  0.02 and 1.06  ±  0.40 mm, respectively, across all predicted time-steps. The correlation coefficients between predicted and actual PCA curves generated through VC-MRI estimation for the 1st/2nd principal components were 0.98/0.89 and 0.99/0.57 in the SI and AP directions, respectively. The optimal number of input neurons, hidden neurons, and MLP-NN for ADMLP-NN PCA weighting coefficient prediction were determined to be 7, 4, and 10, respectively. The optimal cost function threshold was determined to be 0.05. PCA weighting coefficient and VC-MRI accuracy was reduced for increased prediction-step size. Accurate PCA weighting coefficient prediction correlated with accurate VC-MRI prediction. For the patient study, the predicted 4D tumor tracking errors in superior-inferior, anterior-posterior and lateral directions were 0.50  ±  0.47 mm, 0.40  ±  0.55 mm, and 0.28  ±  0.12 mm, respectively. Preliminary studies demonstrated the feasibility to use VC-MRI and artificial neural networks to predict real-time 3D DFMs of the tumor for 4D target tracking.

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV)

Background

Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method.

Methods

Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods.

Results

EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges.

Conclusions

Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

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.

Technical Note: Imaging dose resulting from optimized procedures with limited-angle intrafractional verification system during stereotactic body radiation therapy lung treatment

PURPOSE

The limited-angle intrafractional verification (LIVE) system was developed to track tumor movement during stereotactic body radiation therapy (SBRT). However, the four-dimensional (4D) MV/kV imaging procedure results in additional radiation dose to patients. This study is to quantify imaging radiation dose from optimized MV/kV image acquisition in the LIVE system and to determine if it exceeds the American Association of Physicists in Medicine Task Group Report 180 image dose threshold.

METHODS

TrueBeam™ platform with a fully integrated system for image guidance was studied. Monte Carlo-simulated kV and MV beams were calibrated and then used as incident sources in an EGSnrc Monte Carlo dose calculation in a CT image-based patient model. In three representative lung SBRT treatments evaluated in this study, tumors were located in the patient's posterior left lung, mid-left lung, and right upper lung. The optimized imaging sequence comprised of arcs ranging from 2 to 7, acquired between adjacent three-dimensional (3D)/IMRT beams, with multiple simultaneous kV (125 kVp) and MV (6 MV) image projections in each arc, for different optimization scenarios. The MV imaging fields were generally confined to the treatment target while kV images were acquired with a normal open field size with a full bow-tie filter.

RESULTS

In a seven-arc acquisitions case (highest imaging dose scenario), the maximum kV imaging doses to 50% of the tissue volume (D50 from DVHs), for spinal cord, right lung, heart, left lung, and the target, were 0.4, 0.4, 0.6, 0.7, and 1.4 cGy, respectively. The corresponding MV imaging doses were 0.1 cGy to spinal cord, right lung, heart, and left lung, and 11 cGy to target. In contrast, the maximum radiation dose from two cases treated with two Volumetric-Modulated Arc Therapy (VMAT) fields and two-arc image acquisitions is approximately 30% of that of the seven-arc acquisition.

CONCLUSIONS

We have evaluated the additional radiation dose resulting from optimized LIVE system MV/kV image acquisitions in two best (least imaging dose) and one worst (highest imaging dose) lung SBRT treatment scenarios. The results show that these MV/kV imaging doses are comparable to those resulting from current imaging procedures used in Image-Guided Radiation Therapy (IGRT) and are within the dose threshold of 5% target dose as recommended by the AAPM TG-180 report.

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.

Image acquisition optimization of a limited-angle intrafraction verification (LIVE) system for lung radiotherapy

PURPOSE

Limited-angle intrafraction verification (LIVE) has been previously developed for four-dimensional (4D) intrafraction target verification either during arc delivery or between three-dimensional (3D)/IMRT beams. Preliminary studies showed that LIVE can accurately estimate the target volume using kV/MV projections acquired over orthogonal view 30° scan angles. Currently, the LIVE imaging acquisition requires slow gantry rotation and is not clinically optimized. The goal of this study is to optimize the image acquisition parameters of LIVE for different patient respiratory periods and gantry rotation speeds for the effective clinical implementation of the system.

METHOD

Limited-angle intrafraction verification imaging acquisition was optimized using a digital anthropomorphic phantom (XCAT) with simulated respiratory periods varying from 3 s to 6 s and gantry rotation speeds varying from 1°/s to 6°/s. LIVE scanning time was optimized by minimizing the number of respiratory cycles needed for the four-dimensional scan, and imaging dose was optimized by minimizing the number of kV and MV projections needed for four-dimensional estimation. The estimation accuracy was evaluated by calculating both the center-of-mass-shift (COMS) and three-dimensional volume-percentage-difference (VPD) between the tumor in estimated images and the ground truth images. The robustness of LIVE was evaluated with varied respiratory patterns, tumor sizes, and tumor locations in XCAT simulation. A dynamic thoracic phantom (CIRS) was used to further validate the optimized imaging schemes from XCAT study with changes of respiratory patterns, tumor sizes, and imaging scanning directions.

RESULTS

Respiratory periods, gantry rotation speeds, number of respiratory cycles scanned and number of kV/MV projections acquired were all positively correlated with the estimation accuracy of LIVE. Faster gantry rotation speed or longer respiratory period allowed less respiratory cycles to be scanned and less kV/MV projections to be acquired to estimate the target volume accurately. Regarding the scanning time minimization, for patient respiratory periods of 3-4 s, gantry rotation speeds of 1°/s, 2°/s, 3-6°/s required scanning of five, four, and three respiratory cycles, respectively. For patient respiratory periods of 5-6 s, the corresponding respiratory cycles required in the scan changed to four, three, and two cycles, respectively. Regarding the imaging dose minimization, for patient respiratory periods of 3-4 s, gantry rotation speeds of 1°/s, 2-4°/s, 5-6°/s required acquiring of 7, 5, 4 kV and MV projections, respectively. For patient respiratory periods of 5-6 s, 5 kV and 5 MV projections are sufficient for all gantry rotation speeds. The optimized LIVE system was robust against breathing pattern, tumor size and tumor location changes. In the CIRS study, the optimized LIVE system achieved the average center-of-mass-shift (COMS)/volume-percentage-difference (VPD) of 0.3 ± 0.1 mm/7.7 ± 2.0% for the scanning time priority case, 0.2 ± 0.1 mm/6.1 ± 1.2% for the imaging dose priority case, respectively, among all gantry rotation speeds tested. LIVE was robust against different scanning directions investigated.

CONCLUSION

The LIVE system has been preliminarily optimized for different patient respiratory periods and treatment gantry rotation speeds using digital and physical phantoms. The optimized imaging parameters, including number of respiratory cycles scanned and kV/MV projection numbers acquired, provide guidelines for optimizing the scanning time and imaging dose of the LIVE system for its future evaluations and clinical implementations through patient studies.

Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study

AIM

During cancer radiotherapy treatment, on-board four-dimensional-cone beam computed tomography (4D-CBCT) provides important patient 4D volumetric information for tumor target verification. Reconstruction of 4D-CBCT images requires sorting of acquired projections into different respiratory phases. Traditional phase sorting methods are either based on external surrogates, which might miscorrelate with internal structures; or on 2D internal structures, which require specific organ presence or slow gantry rotations. The aim of this study is to investigate the feasibility of a 3D motion modeling-based method for markerless 4D-CBCT projection-phase sorting.

METHODS

Patient 4D-CT images acquired during simulation are used as prior images. Principal component analysis (PCA) is used to extract three major respiratory deformation patterns. On-board patient image volume is considered as a deformation of the prior CT at the end-expiration phase. Coefficients of the principal deformation patterns are solved for each on-board projection by matching it with the digitally reconstructed radiograph (DRR) of the deformed prior CT. The primary PCA coefficients are used for the projection-phase sorting.

RESULTS

PCA coefficients solved in nine digital phantoms (XCATs) showed the same pattern as the breathing motions in both the anteroposterior and superoinferior directions. The mean phase sorting differences were below 2% and percentages of phase difference < 10% were 100% for all the nine XCAT phantoms. Five lung cancer patient results showed mean phase difference ranging from 1.62% to 2.23%. The percentage of projections within 10% phase difference ranged from 98.4% to 100% and those within 5% phase difference ranged from 88.9% to 99.8%.

CONCLUSION

The study demonstrated the feasibility of using PCA coefficients for 4D-CBCT projection-phase sorting. High sorting accuracy in both digital phantoms and patient cases was achieved. This method provides an accurate and robust tool for automatic 4D-CBCT projection sorting using 3D motion modeling without the need of external surrogate or internal markers.