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Purpose: Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. Methods: This paper proposes incorporating 18F‐fluorodeoxyglucose (FDG) as a higher‐quality PET image to guide the reconstruction of other lower‐count 11C‐methionine (MET) PET datasets to compensate for the lower image quality by a popular kernel algorithm. Specifically, the FDG prior is needed to extract kernel features, and these features were used to build a kernel matrix using a k‐nearest‐neighbor (kNN) search for MET image reconstruction. We created a 2‐D brain phantom to validate the proposed method by simulating sinogram data containing Poisson random noise and quantitatively compared the performance of the proposed FDG‐guided kernelized expectation maximization (KEM) method with the performance of Gaussian and non‐local means (NLM) smoothed maximum likelihood expectation maximization (MLEM), MR‐guided KEM, and multi‐guided‐S KEM algorithms. Mismatch experiments between FDG/MR and MET data were also carried out to investigate the outcomes of possible clinical situations. Results: In the simulation study, the proposed method outperformed the other algorithms by at least 3.11% in the signal‐to‐noise ratio (SNR) and 0.68% in the contrast recovery coefficient (CRC), and it reduced the mean absolute error (MAE) by 8.07%. Regarding the tumor in the reconstructed image, the proposed method contained more pathological information. Furthermore, the proposed method was still superior to the MR‐guided KEM method in the mismatch experiments. Conclusions: The proposed FDG‐guided KEM algorithm can effectively utilize and compensate for the tissue metabolism information obtained from dual‐tracer PET to maximize the advantages of PET imaging. [ABSTRACT FROM AUTHOR]

BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) imaging is a noninvasive technique that makes it possible to probe biological metabolic processes in vivo. However, PET image reconstruction is challenging due to the ill-posedness of the inverse problem. Many image reconstruction methods have been proposed over the past few years to improve diagnostic performance. However, most of these methods can compromise the reconstruction of important high-frequency structural details after aggressive denoising. To address this problem, in this work, we propose a novel deep learning method that reconstructs PET images using a cascading back-projection neural network (bpNet). This network consists of a domain translation operation, which acts as prior knowledge, cascaded with a modified encoder-decoder network. The image reconstruction pipeline ranges from the sinogram to the back-projection image and then to the PET image. Quantitative results from simulation data and Derenzo phantom experiments with the small animal PET prototype system developed in our laboratory clearly demonstrate that our proposed method provides favorable reconstructed image quality, especially for low-count PET image reconstruction. [ABSTRACT FROM AUTHOR]

PURPOSE: To reduce the cost of positron emission tomography (PET) scanning systems, image reconstruction algorithms for low-sampled data have been extensively studied. However, the current method based on total variation (TV) minimization regularization nested in the maximum likelihood-expectation maximization (MLEM) algorithm cannot distinguish true structures from noise resulting losing some fine features in the images. Thus, this work aims to recover fine features lost in the MLEM-TV algorithm from low-sampled data. METHOD: A feature refinement (FR) approach previously developed for statistical interior computed tomography (CT) reconstruction is applied to PET imaging to recover fine features in this study. The proposed method starts with a constant initial image and the FR step is performed after each MLEM-TV iteration to extract the desired structural information lost during TV minimization. A feature descriptor is specifically designed to distinguish structure from noise and artifacts. A modified steepest descent method is adopted to minimize the objective function. After evaluating the impacts of different patch sizes on the outcome of the presented method, an optimal patch size of 7×7 is selected in this study to balance structure-detection ability and computational efficiency. RESULTS: Applying MLEM-TV-FR algorithm to the simulated brain PET imaging using an emission activity phantom, a standard Shepp-Logan phantom, and mouse results in the increased peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as comparing to using the conventional MLEM-TV algorithm, as well as the substantial reduction of the used sampling numbers, which improves the computational efficiency. CONCLUSIONS: The presented algorithm can achieve image quality superior to that of the MLEM and MLEM-TV approaches in terms of the preservation of fine structure and the suppression of undesired artifacts and noise, indicating its useful potential for low-sampled data in PET imaging. [ABSTRACT FROM AUTHOR]

X-ray radiation is harmful to human health. Thus, obtaining a better reconstructed image with few projection view constraints is a major challenge in the computed tomography (CT) field to reduce radiation dose. In this study, we proposed and tested a new algorithm that combines penalized weighted least-squares using total generalized variation (PWLS-TGV) and dictionary learning (DL), named PWLS-TGV-DL to address this challenge. We first presented and tested this new algorithm and evaluated it through both data simulation and physical experiments. We then analyzed experimental data in terms of image qualitative and quantitative measures, such as the structural similarity index (SSIM) and the root mean square error (RMSE). The experiments and data analysis indicated that applying the new algorithm to CT data recovered images more efficiently and yielded better results than the traditional CT image reconstruction approaches. [ABSTRACT FROM AUTHOR]

Dynamic cerebral perfusion computed tomography (DCP-CT) is an advanced imaging technique that helps in the clinical diagnosis of cerebrovascular diseases (CVDs). However, radiation dose deposition during repeated CT scans seriously limits its clinical application. Effective DCP-CT imaging method based on low-dose protocol is needed. A regularized least-squares method with high interpretability based on voxel-level time-attenuation curve (TAC) correction (VTC) is proposed in this study for low-dose DCP-CT image reconstruction. The theory of third-order Hermite interpolation (THI) is applied to voxel-level TAC correction during dynamic images reconstruction. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to quantitatively evaluate the proposed method in terms of imaging accuracy and noise reduction, while hemodynamic maps, including cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transition time (MTT), are calculated to validate its ability to restore hemodynamic parameters. It is proven that the proposed VTC method for low-dose DCP-CT imaging has better performance than several state-of-the-art dynamic CT imaging methods. It can be expected that the VTC method can play an important role in the promotion of the clinical application of DCP-CT for the diagnosis of CVD. [ABSTRACT FROM AUTHOR]

Multi-GPUs accelerated BPF algorithm is developed for improving computational efficiency. Three major acceleration techniques are introduced: (1) dividing reconstructed volume into subsets vertically which reduces the computational cost on the boundary term between parallel chords; (2) transposed method is used to avoid low efficiency of access in global memory which is caused by different chords selection; (3) optimized memory allocation scheme is adopted. Experimental data are used to evaluate the image quality and reconstruction time. It takes only 4.118 s to reconstruct a volume image of 512 × 512 × 512 with 360 projection data of 512 × 512 on dual NVIDIA Tesla C2070 cards. Added the time consuming on data read, transfer and storage part, the complete reconstruction process could be finished in less than 9 s. In particular, BPF-based ROI-reconstruction for cone beam Ortho-CT shows promising application prospect. [ABSTRACT FROM AUTHOR]

A mathematical derivation was conducted to illustrate that exact 3D image reconstruction could be achieved for z-homogeneous phantoms from data acquired with 2D general trajectories using the back projection filtration (BPF) algorithm. The conclusion was verified by computer simulation and experimental result with a circular scanning trajectory. Furthermore, the effect of the non-uniform degree along z-axis of the phantoms on the accuracy of the 3D reconstruction by BPF algorithm was investigated by numerical simulation with a gradual-phantom and a disk-phantom. The preliminary result showed that the performance of BPF algorithm improved with the z-axis homogeneity of the scanned object. [ABSTRACT FROM AUTHOR]

Micro-CT with a high spatial resolution in combination with computer-based-reconstruction techniques is considered a powerful tool for morphological study of insects. The quality of CT images crucially depends on the precise knowledge of the scan geometry of the micro-CT system. In this paper, we have proposed a method to calculate the deviation of rotating axis for compensating deficiency of existing methods. A practical application of this geometric calibration method of the micro-CT system for insect imaging is presented. We have performed the computer-simulation study and experimental study with our prototype micro-CT system. The results demonstrate that the proposed technique is accurate and robust. In addition, we have evaluated the imaging characteristics of the detector in terms of modulation-transfer function (MTF). Finally, insect imaging performance and image reconstruction from data acquired with different energies are presented. [ABSTRACT FROM PUBLISHER]

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption. [ABSTRACT FROM AUTHOR]