Browsing by Subject "Cone-beam CT"

Onboard cone beam computed tomography (CBCT) has become a widespread means of three-dimensional target localization for radiation therapy; however, it is susceptible to metal artifacts and beam-hardening artifacts that can hinder visualization of low contrast anatomy. Dual-CBCT provides easy access to techniques that may reduces such artifacts. Additionally, dual-CBCT can decrease imaging time and provide simultaneous orthogonal projections which may also be useful for fast target localization. However, dual-CBCT will suffer from large increases in scattered radiation due to the addition of the second source.

An experimental bench top dual CBCT system was constructed so that each imaging chain in the dual CBCT system mimics the geometry of gantry-mounted CBCT systems commonly used in the radiation therapy room. The two systems share a common axis of rotation and are mounted orthogonally. Custom control software was developed to ensure reproducible exposure and rotation timings. This software allows the implementation of the acquisition sequences required for the cross scatter avoidance and correction strategies studied.

Utilizing the experimental dual CBCT system cross scatter was characterized from 70-145 kVp in projections and reconstructed images using this system and three cylindrical phantoms (15cm, 20cm, and 30cm) with a common Catphan core. A novel strategy for avoiding cross-scatter in dual-CBCT was developed that utilized interleaved data acquisition on each imaging chain. Contrast and contrast-to-noise-ratio were measured in reconstructions to evaluate the effectiveness of this strategy to avoid the effects of cross scatter.

A novel correction strategy for cross scatter was developed wherein the cross scatter was regularly sampled during the course of data acquisition and these samples were used as the basis for low- and high- frequency corrections for the cross-scatter in projections. The cross scatter sampling interval was determined for an anthropomorphic phantom at three different sites relevant to radiation therapy by estimating the angular Nyquist frequency. The low frequency portion of the cross scatter distribution is interpolated between samples to provide an estimate of the cross scatter distribution at every projection angle and was then subtracted from the projections.

The high-frequency portion of the correction was applied after the low-frequency correction was applied. The novel high-frequency correction utilizes the fact that a direct estimate of the high-frequency components was obtained in the cross scatter samples. The high-frequency components of the measured cross scatter were subtracted from the projections in the Fourier domain, a process referred to as spectral subtraction. Each projection is corrected using the cross scatter sample taken at the closest projection angle. In order to apply this correction in the Fourier domain the high-frequency component of the cross scatter must be approximately stationary. To improve the stationarity of the high-frequency cross scatter component a novel two-dimensional, overlapping window was developed. The spectral subtraction was then applied in each window and the results added to form the final image.

The effectiveness of the correction techniques were evaluated by measuring the contrast and contrast-to-noise-ratio in an image quality phantom. Additionally, the effect of the high-frequency correction on resolution was measured using a line pair phantom.

Cross scatter in dual CBCT was shown for large phantoms to be much higher than forward scatter which has long been known to be one of the largest degrading factors of image quality in CBCT. This results in large losses of contrast and CNR in reconstructed images. The interleaving strategy for avoiding cross scatter during projection acquisition showed similar performance to cross scatter free acquisitions, however, does not acquire projections at the maximum possible rate. For those applications in which maximizing the acquisition rate of projections is important, the low- and high-frequency corrections effectively mitigated the effects of cross scatter in the dual CBCT system.

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.

This study aims to (1) investigate methodology for harmonization of radiomics features between planning CT and on-board CBCT and establish a workflow to harmonize images taken from different scanning protocols and over the course of radiotherapy treatments using normalization, and (2) examine feature variability of longitudinal cone-beam CT radiomics for 3 different fractionation schemes and a time-point during treatment indicative of early treatment response.

All CBCT images acquired over the course of lung SBRT for each patient were registered with corresponding planning CT. A volume-of-interest (VOI) in a homogeneous soft-tissue region that would not change over the course of radiotherapy was selected on the planning CT. The VOI was applied to all CBCT images of the same patient taken at different days. The first CBCT was normalized to the planning CT using the ratio of their respective mean VOI pixel values. Subsequent CBCT images were normalized using the ratio of that CBCT’s mean VOI pixel value to the first CBCT’s mean VOI pixel value. Forty-three features characterizing image intensity and morphology in fine and coarse textures were extracted from the planning CT, all original CBCT images, and all normalized CBCT images. T-test on extracted features from CBCT images with and without normalization indicates the effect of normalization on the distribution of various features. Mutual information between the planning CT and the first CBCT with and without normalization was calculated to assess the effectiveness of normalization on harmonizing radiomics features.

Of 72 NSCLC patients treated with lung SBRT, 18 received 15-18 Gy / fraction for 3 fractions; 36 received 12-12.5 Gy / fraction for 4 fractions; 18 received 8-10 Gy / fraction for 5 fractions. We studied 7 sets of CBCT images from the same treatment fraction as a ‘test-retest’ baseline to study the additional daily CBCT images. Fifty-five gray level intensity and textural features were extracted from the CBCT images. Test-retest images were used to determine the smallest detectable change (C=1.96*SD) indicating significant variation with a 95% confidence level. Here, the significance of feature variation depended on the choice of a minimum number of patients for which a feature changed more than ’C’. Analysis of which features change at which moment during treatment with different fractionation schemes was used to investigate a time-point indicative of early tumor response.

T-test on planning CT and CBCT images of the 72 patients indicated that normalization with a soft tissue VOI reduced the number of features with significant variation (p<0.05) by 55%. Following lung SBRT, 30 features changed significantly for at least 10% of all patients. For patients treated with 3 fractions, 49 features changed at Fraction 2, and 49 at Fraction 3; there was 100% overlap between features at both fractions. For patients treated with 4 fractions, 45, 45, and 48 features changed at Fraction 2-4 respectively; there was 92% overlap between features at Fraction 2 and the remaining fractions. For patients treated with 5 fractions, 12, 18, 14, and 25 features changed at Fraction 2-5; there was 36%, 48%, and 48% overlap between features at Fraction 2-4 and the remaining fractions respectively.

Normalization can potentially harmonize radiomics features on both planning CT and on-board CBCT. Feature variability depends on the selection of normalization VOI and extraction VOI. Significant changes in gray level radiomic features were observed over the course of lung SBRT. Different fractionation schemes corresponded to different patterns of feature variation. Higher fractional dose corresponded to a larger number of variable features and high overlap of variable features at an earlier time-point.