Proj2Proj: self-supervised low-dose CT reconstruction.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.

Maximum-A-Posteriori Estimation of Retinal Oxygen Tension Based on Photon Counting Statistics.

The regularization of retinal oxygen tension estimation was previously proposed with an assumption that phosphorescence intensity images are corrupted by additive Gaussian noise. Based on this assumption, a regularized least-squares estimate has been shown to be better than a conventional least-squares estimation. However, this assumption is inconsistent with the acquisition process of phosphorescence intensity images acquired using an intensified charge-coupled device camera. Almost the entire acquisition process is governed by the natural aspects of photons. Therefore, a method based on photon counting statistics is more appropriate. In this study, we propose a regularized oxygen tension estimation method based on photon counting statistics and a phosphorescence lifetime imaging model.

A nonlinear total variation based computed tomography (CT) image reconstruction method using gradient reinforcement.

Compressed sensing-based reconstruction algorithms have been proven to be more successful than analytical or iterative methods for sparse computed tomography (CT) imaging by narrowing down the solution set thanks to its ability to seek a sparser solution. Total variation (TV), one of the most popular sparsifiers, exploits spatial continuity of features by restricting variation between two neighboring pixels in each direction as using partial derivatives. When the number of projections is much fewer than the one in conventional CT, which results in much less sampling rate than the minimum required one, TV may not provide satisfactory results. In this study, a new regularizer is proposed which seeks for a sparser solution by reinforcing the gradient of TV and empowering the spatial continuity of features. The experiments are done by using both analitical phantom and real human CT images and the results are compared with conventional, four-directional, and directional TV algorithms by using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and Structural Similarity Index (SSIM) metrics. Both quantitative and visual evaluations show that the proposed method is promising for sparse CT image reconstruction by reducing the background noise while preserving the features and edges.

Postbiotic metabolites, antioxidant and anticancer activities of probiotic Leuconostoc pseudomesenteroides strains in natural pickles.

In this study, five strains of Leuconostoc pseudomesenteroides were thought to have probiotic properties and anticancer activity isolated from natural pickles and identified by performing the 16S rRNA sequence analysis. The probiotic properties, postbiotic amounts, the capacity to adhere to the L-929, HT-29 and Caco-2 cell lines, the effects of postbiotic and bacterial extracts on cell viability and biochemical activities were investigated in the strains. In the results, Leu. pseudomesenteroides Y6 strain was detected to have the best resistance to the stomach and intestinal environments, and the quantities of postbiotic metabolites are similar to each other. The bacterial adhesion capacities were found to be in the range of 1.66-8.5%. Furthermore, postbiotic metabolites of all isolates had good anticancer activity (27.67-86.05%) and the activity of bacterial extractions increased depending on concentration. Leu. pseudomesenteroides Y4 and Y6 strains generally showed better activity than controls and all strains were strong 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavengers in the antioxidant studies. In conclusion, the Y6 strain, which had the best probiotic feature, was found to show significantly good biological activity. It is thought that this isolate will be supported by new in vivo studies and eventually be brought to the food and health industry.

Metagenomic Analysis of Intestinal Microbiota in Florated Rats.

Changes in gut microbiota have shown that it plays an important role in animal health and metabolic diseases. The intestinal microbiota is a complex structure that functions as an organ system with the presence of trillions of microorganisms. In this study, changes in the intestinal microbiota of Wistar rats with high fluorine were evaluated. Water containing 100 ppm NaF was given to 14 male Wistar albino rats as drinking water for 12 weeks. Fluorine is known to be an inducer of protein oxidation, lipid peroxidation, modulation of intracellular redox homeostasis, and oxidative stress. In this study, it was determined that the level of MDA (molandialdehyde), one of the oxidative stress parameters, increased significantly in the intestinal tissue after fluorine intoxication. The decrease in CAT (catalase) and SOD (superoxide dismutase) enzyme activities was found to be statistically significant. Intestinal tissues were taken under aseptic conditions and microorganisms found in flora were replicated by V3-V4 16S rRNA gene-specific primers. As a result of the sequence analysis, a statistical comparison of the control group and the fluorine applied group was made. The study we have done showed that there was a significant difference in species diversity in the intestinal microbiota of mice treated with fluorine. As a result, the composition of the intestinal microflora, especially Lactobacillus species, was significantly changed in rats with high fluorine.

Iterative image reconstruction using non-local means with total variation from insufficient projection data.

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.

An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering.

BACKGROUND: After the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems. METHODS: In this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV. RESULTS: A tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV. CONCLUSIONS: RMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization.

BACKGROUND: Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied. METHODS: In this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices. RESULTS: A 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART. CONCLUSIONS: Computer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.