Cone-Beam Computed Tomography (CBCT) usually suffers from motion blurring artifacts when scanning at the region of the thorax. Consequently, it may result in inaccuracy in localizing the target of treatment and verifying the delivered dose in radiation therapy. Despite that 4D-CBCT reconstruction technology could alleviate the motion blurring artifacts, it introduces severe streaking artifacts due to the under-sampled projections used for reconstruction. Aiming at improving the overall quality of 4D-CBCT images, we explored the performance of the deep learning-based technique on 4D-CBCT images. Inspired by the high correlation among these 4D-CBCT images, we proposed a spatial-temporal plus prior image-based CNN, which is a cascaded network of a spatial CNN and a temporal CNN. For the spatial CNN, it is in the manner of the encoder-decoder architecture that utilizes a pair of a prior image-based channel and an artifact-degraded channel in the encoder stage for feature representation and fuses the feature maps for image restoration in the decoder stage. Next, three consecutive phases of images that are predicted by N-net individually are stacked together for latent image restoration via the temporal CNN. By doing so, temporal CNN learns the correlation between these images and construct the residual map covering streaking artifacts and noise for further artifact reduction. Experimental results of both simulation data and patient data indicated that the proposed method has the capability not only reduces the streaking artifacts but also restore the original anatomic features while avoiding inducing error tomographic information.
Compared to conventional X-ray transmission imaging techniques, imaging systems based on field-emissive cathode based conformal transmission imaging schemes have smaller volumes. This imaging system combines multiple point sources to complete very low dose imaging of the region of interest using a small cone beam, and finally obtain high quality images by image restoration methods. However, more precise system geometry is required by the system architecture of the new imaging scheme and the special image-forming method. If the geometric parameters that are inconsistent with the actual use are used in image restoration, it will cause the geometric distortion in the final restored image. In this work, a proposed geometric calibration applied to the new imaging scheme. First, we use a partial point source on the periphery of the array ray source to project the side ball phantom of the imaged object. Then, a cost function that associates the geometrical parameters with the degree to which the back-projections of the ball phantom in projections from different point sources is constructed. Finally, the particle swarm optimization (PSO) is used for minimizing the cost function, and the resultant value of the cost function is the actual geometric parameter. With this calibration method, transmission imaging and geometric calibration will be done simultaneously. Besides, this calibration method does not depend on a complex calibration phantom and is simple and effective. In addition, the PSO can accelerate the implementation of the algorithm. The effectiveness and accuracy of this calibration method are verified by two sets of simulation experiments.
4D-CBCT reconstruction technique could provide a sequence of phase-resolved images to alleviate motion blurring artifacts as a result of respiratory movement during CT scanning. However, 4D-CBCT images are degraded by streaking artifacts due to the under-sampled projection used for the reconstruction of each phase. Based on the high correlation of these 4D-CBCT images, estimating the deformation vector fields (DVF) among them via a deformable registration algorithm is one of the possible solutions to improve the image quality. Often, the intensity-based similarity metric is utilized in the optimization problem by minimizing the squared sum of intensity differences (SSD) of the reference image and the target image. However, this metric is not suitable for the 4D-CBCT registration case, because the quality of both the reference image and the target image are not always guaranteed. As a result, the registration accuracy of the conventional SSD metric still has room to improve. In our method, by considering the characteristic of the phase-depended images, we design a novel similarity metric: 1) A prior image reconstructed by the whole projection set is regarded as the reference image; 2) Instead of an intensity-based similarity metric alone, we proposed a free-form based optimization function associating the gradient information in spatial domain with the projection-based constraint. To validate the performance of the proposed method, we carried out a phantom data and a patient data to compare with the classical Demons algorithm. To be specific, the quality of the registered image was improved to a great extent, especially in regions of interest of moving tissues. Quantitative evaluations were shown in terms of the rooted mean square error (RMSE) by our method when compared with existing Demons method.
As an alternative to conventional sources, field emission x-ray cold cathodes of nanomaterials have been developed in recent years. Many different imaging geometries with this kind of source have been proposed, which has the merits of fast response, low energy consumption, and individually addressable switching ability. In this work, we proposed a novel digital tomosynthesis (DTS) geometry based on field emission flat-panel X-ray source array (FEF X-ray source array) and a reconstruction method based on this new geometry. The new DTS with designed lighting mode has shorter acquisition time and lower dose compared with the traditional DTS scheme. Due to the designed lighting mode, it cannot use a traditional reconstruction algorithm. The proposed reconstructed algorithm builds the relation on photons to solve the reconstruction problem. The simulated result shows that the proposed method can obtain 5pl / mm in the X-Y plane and 2pl / mm in the Z plane which indicates the potential of the proposed reconstruction modality.
Motion blurring artifacts in CBCT can be alleviated by providing a sequence of phase-depended images through 4DCBCT technique. However, it introduces streaking artifacts due to the under-sampled projection problem for each phase. One possible solution is to use deformable registration algorithms to estimate the deformation vector fields (DVF) between different phase-depended images. Among them, the optical flow based Demons registration method is a major technique due to its simplicity and efficiency. However, current Demons algorithms still suffer from relative low registration precision due to only using gradient information of images to calculate the DVFs in different directions. To improve the registration precision, we took the interaction between the DVFs calculated in Demons process into account and then proposed a weighted Demons registration method. In this method, a joint distribution of the gradient magnitude and Laplace of Gaussian (GM-LoG) signal which could represent the edge features of magnitude and orientation was introduced. Such a joint distribution could be used to guide the calculation of DVF to preserve the more detailed features and topology structure of the image during the registration process. Both simulation and real data experiments have been carried out to verify the performance of our method. In specific, the image quality has been improved regarding to distinct features, especially in regions of interest of moving tissues. Quantitative evaluations were shown in terms of the rooted mean square error (RMSE) and correlation coefficient (CC) are achieved by our method when compared with existing single Demons method and double Demons method, respectively.
Conventional Cone-Beam Computed Tomography (CBCT) acquisition suffers from motion blurring artifacts at the region of the thorax, and consequently, it may result in inaccuracy in localizing the target of treatment and verifying delivered dose in radiation therapy. Although 4D-CBCT reconstruction technology is available to alleviate the motion blurring artifacts with the strategy of projection sorting followed by independent reconstruction, under-sampling streaking artifacts and noise are observed in the set of 4D-CBCT images due to relatively fewer projections and large angular spacing in each phase. Aiming at improving the overall quality of 4D-CBCT images, we explored the performance of the deep learning model on 4D-CBCT images, which has been paid little attention before. Inspired by the high correlation among the 4D-CBCT images at different phases, we incorporated a prior image reconstructed from full-sampled projections beforehand into a lightweight structured convolutional neural network (CNN) as one input channel. The prior image used in the CNN model can guide the final output image to restore detailed features in the testing process, so it is referred to as Prior-guided CNN. Both simulation and real data experiments have been carried out to verify the effectiveness of our CNN model. Experimental results demonstrate the effectiveness of the proposed CNN regarding artifact suppression and preservation of anatomical structures. Quantitative evaluations also indicate that 33.3% and 21.2% increases in terms of Structural Similarity Index (SSIM) have been achieved by our model when comparing with gated reconstruction and images tested on CNN without prior knowledge, respectively.
Regularization parameter selection is pivotal in optimizing reconstructed images which controls a balance between fidelity and penalty term. Images reconstructed with the optimal regularization parameter will keep the detail preserved and the noise restrained at the same time. In previous work, we have used CT image statistics to select the optimal regularization parameter by calculating the second order derivates of image variance (Soda-curve). But same as L-curve method, it also needs multiple reconstruction in different regularization parameters which will spend plenty of time. In this paper, we dive into the relationship between image statistics changes and regularization parameter during the iteration. Meanwhile, we propose a method based on the empirical regularity found in the iterations to tune the regularization parameter automatically in order to maintain the image quality. Experiments show that the images reconstructed with the regularization parameters tuned by the proposed method have higher image quality as well as less time when compared to L-curve based results.
Conventional Cone-Beam Computed Tomography (CBCT) acquisition suffers from motion blurring problems of moving organs, especially the respiratory motion at thorax region, and consequently it may result in inaccuracy in the localizing the target of treatment and verifying delivered dose in radiation therapy. Although 4D-CBCT reconstruction technology is available to alleviate the motion blurring artifacts with the strategy of projection sorting tuned by respiratory bins, it introduces under-sampled problems. Aiming to precisely estimate the motion information of individual 4D-CBCT reconstructions, the proposed method combines the motion variable matrixes extracted from independent 4D-CBCT reconstructions using Robust Principal Component Analysis (RPCA) and the prior reconstructed image from fullsampled projections together and incorporate into iterative reconstruction framework, defining the Motion Compensated RPCA (MC-RPCA) method. Both simulation data and real data have been tested to verify the improvement in image quality at individual reconstructed phases by MC-RPCA. It can be obviously observed that the image quality the MC-RPCA method is improved with distinct features, especially in two regions of interest (ROI) with moving tissues. Quantitative evaluations indicate that large improvements in the Structural Similarity Index (SSIM) and Contrast-to-Noise Ratio(CNR) are achieved at the diaphragm slice by our method when comparing with MKB and the Prior Image Constraint Compressed Sensing (PICCS) algorithm, respectively.
KEYWORDS: Breast, Digital breast tomosynthesis, Reconstruction algorithms, Signal attenuation, 3D image processing, Image enhancement, Tissues, Sensors, Image segmentation, X-rays
Digital breast tomosynthesis (DBT) can provide quasi three-dimensional (3D) structural information using a sequence of projection views that are acquired at a small number of views over a limited angular range. Nevertheless, the quantitative accuracy of the image can be significantly compromised by severe artifacts and poor resolution in depth dimension resulting from the incomplete data. The purpose of this work is: (a) investigate a variety of boundary artifacts representing as the decline tendency of the attenuation coefficients which is caused by insufficient projection data; (b) employ the 3D breast surface information we proposed in this study into the simultaneous algebraic reconstruction technique (SART) for artifacts reduction. Numerical experiments demonstrated that such boundary artifacts could be suppressed with the proposed algorithm. Compared to SART without using prior information, a 9.57% decrease in root mean square error (RMSE) is achieved for the central 40 slices. Meanwhile, the spatial resolution of potential masses and micro calcifications (MCs) in the reconstructed image is relatively enhanced. The full-width at half maximum (FWHM) of the artifact spread function (ASF) for proposed algorithm and SART are 17.87 and 19.68, respectively.
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