On-board Image Augmentation Using Prior Image and Deep Learning for Image-guided Radiation Therapy
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Cone-beam Computed Tomography (CBCT) has been widely used in image-guided radiation therapy for target localization. 3D CBCT has been developed for localizing static targets, while 4D CBCT has been developed for localizing moving targets. Although CBCT has been used as the gold standard in current clinical practice, it has several major limitations: (1). High imaging dose to the normal tissue from repeated 3D/4D CBCT scans. Low dose CBCT reconstruction is changeling because of streak artifacts caused by limited projection and increased noise by low exposure. Previous methods such as compressed sensing method can successfully remove streak artifacts and reduce noise. However, the reconstructed images are blurred, especially at edge regions due to the uniform image gradient penalty, which will affect the accuracy of patient positioning for target localization. (2). Poor soft tissue contrast due to the inherent nature of x-ray based imaging as well as several CBCT artifacts such as scatter and beam hardening. As a result, the accuracy of using CBCT to localize tumors in the abdominal region is extremely limited. To address these limitations, we propose to use prior images and deep learning techniques to enhance the edge information and soft tissue contrast of 3D/4D CBCT images. The specific aims include: 1) Establish a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT. 2) Establish hybrid PCTV and deep learning methods to achieve accurate and fast low dose CBCT reconstruction.3) Establish deep learning methods to generate virtual on-board multi-modality images to enhance soft tissue contrast in CBCT. The results from this research will be highly relevant to the clinical application of on-board image for head and neck, lung and liver patient treatment to improve target localization while reducing radiation dose of 3D/4D CBCT scan.
To address the first limitation of current clinical CBCT, a novel prior contour based TV (PCTV) method was developed in this dissertation to enhance the edge information in compressed sensing reconstruction. Specifically, the edge information is extracted from the 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 contour 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 proposed 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 image 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. 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. Although edge enhancement was reduced slightly with noisy projections, 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.
The first technique developed in this dissertation demonstrates that 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. However, the accuracy of edge enhancement in PCTV is affected by the registration errors and anatomical changes from prior to on-board images, especially when deformation exists. The next section of the dissertation describes the development of the hybrid-PCTV to further improve the accuracy and robustness of PCTV. Similar to PCTV method, 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 the 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. 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 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. Overall, 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.
In the next section, a technique for predicting daily on-board edge deformation using deep convolutional neural networks (CNN) is described to bypass deformable registration to improve the PCTV reconstruction efficiency. Edge deformation was predicted by deep learning model including a supervised and an unsupervised convolutional neural network (CNN) learning model. In the supervised model, deformation vector field (DVF) registered from CT to full-sampled CBCTs and retrospectively under-sampled low-dose CBCT were obtained on the first treatment day to train the model, which was then updated with following days’ data. In contrast, no ground truth DVF was needed for the unsupervised model and image pair of planning CT and CBCT were used as input to fine-tune the model. The model predicts DVF for low-dose CBCT acquired on the following day to generate on-board contours for PCTV reconstruction. This method was evaluated using lung SBRT patient data. In the intra-patient evaluation study, the first n-1 day’s CBCTs were used for CNN training to predict nth day edge information (n=2, 3, 4, 5). In addition, 10 lung SBRT patient data were obtained for the inter-patient study. The unsupervised model was trained on 9 of 10 patient data with transferring learning and to predict the while DVF for the other patient. 45 half-fan projections covering 360˚ from nth day CBCT was used for reconstruction and results from Edge-preserving (EPTV), PCTV and PCTV-CNN were compared. The cross-correlations between predicted and reference edge maps were about 0.74 for intra-patient study using the supervised CNN model. When using the unsupervised CNN mode, the cross-correlations of the predicted edge map were about 0.88 for both intra-patient and inter-patient. PCTV-CNN enhanced bone edges in CBCT compared to EPTV and achieved comparable image quality as PCTV while avoiding the user dependent and time-consuming deformable registration process. These results demonstrated the feasibility to use CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. Thus, PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low dose 3D CBCT or 4D CBCT to improve the precision of on-board target localization and adaptive radiotherapy.
In the last part of this dissertation, prior images such as high-quality planning CT with deformation was used to generate on-board CT/CBCT image to improve soft tissue contrast for CBCT. The whole deformation vector field (DVF) was predicted using the unsupervised CNN model fine-tuned on liver SBRT patient. The on-board virtual CT in the liver region is obtained by deforming the prior planning CT using the finite element model (FEM) based on the deformation of livers surfaces from planning CT to CBCT. The deformed CT is embedded in the liver region to improve soft tissue contrast for tumor localization in the liver, while on-board CBCT is used for the region outside the liver to verify the positions of healthy tissues close to the tumor. In the current study, we mainly investigated the feasibility to use deep learning to generate accurate liver contours in CBCT. The method was evaluated using 15 SBRT liver patients’ data including planning CT and daily CBCT. Image sets of 14 patients were obtained to train the model while the other one was used to test. Deformed CT with DVF generated from Velocity and predicted DVF were compared using image similarity matrix including mutual information (MI), cross correlation (CC) and structural similarity index measurement (SSIM). The MI, CC and SSIM between predicted deformed CT and first-day on-board CBCT were 1.28, 0.98 and 0.91 for a new patient, respectively. All similarity evaluation results demonstrated the unsupervised CNN model can predict DVF to deform CT equivalently with Velocity. Therefore, it is feasible to apply deep CNN deformation model for fast on-board virtual image generation to improve the precision of the treatment of low contrast soft tissue tumors.
In conclusion, the works presented in this dissertation aim to use prior image and deep learning to improve the image quality of the on-board low dose 3D/4D CBCT by enhancing the edge sharpness and soft tissue contrast. The goals of this dissertation research are to: 1) establish a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT; 2) establish hybrid PCTV to improve the accuracy and robustness of PCTV when deformable registration needed 3) implement deep learning methods to bypass deformable registration to automate and accelerate the low dose CBCT reconstruction; 4) establish deep learning methods to generate virtual on-board multi-modality images to enhance soft tissue contrast in CBCT. Results demonstrated that 1) edge sharpness can be improved for low dose 3D/4D CBCT using prior contour based TV method; 2) virtual image generated by fusing CBCT and deformed CT can improve the soft tissue contrast for the liver patient and 3) deep learning can be applied to improve the efficiency and automation in image deformable registration. Imaging augmentation including high and low contrast improvement using these techniques can improve the precision of dose delivery for image-guided radiation therapy, which might path the way to be applied in the clinic to improve the patient care.
Chen, Yingxuan (2019). On-board Image Augmentation Using Prior Image and Deep Learning for Image-guided Radiation Therapy. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/18721.
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