Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology.

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10-2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study.

PURPOSE: This study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. METHODS: Using full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. RESULTS: The 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose =  - 926.28 ± 0.32, BM3D =  - 926.65 ± 0.32, and low-dose =  - 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). CONCLUSIONS: The results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.

Hydrophilic Photocrosslinkers as a Universal Solution to Endow Water Affinity to a Polymer Photocatalyst for an Enhanced Hydrogen Evolution Rate.

A universal approach for enhancing water affinity in polymer photocatalysts by covalently attaching hydrophilic photocrosslinkers to polymer chains is presented. A series of bisdiazirine photocrosslinkers, each comprising bisdiazirine photophores linked by various aliphatic (CL-R) or ethylene glycol-based bridge chains (CL-TEG), is designed to prevent crosslinked polymer photocatalysts from degradation through a safe and efficient photocrosslinking reaction at a wavelength of 365 nm. When employing the hydrophilic CL-TEG as a photocrosslinker with polymer photocatalysts (F8BT), the hydrogen evolution reaction (HER) rate is considerably enhanced by 2.5-fold compared to that obtained using non-crosslinked F8BT photocatalysts, whereas CL-R-based photocatalysts yield HER rates comparable to those of non-crosslinked counterparts. Photophysical analyses including time-resolved photoluminescence and transient absorption measurements reveal that adding CL-TEG accelerates exciton separation, forming long-lived charge carriers. Additionally, the in-depth study using molecular dynamics simulations elucidates the dual role of CL-TEG: it enhances water penetration into the polymer matrix and stabilizes charge carriers after exciton generation against undesirable recombination. Therefore, the strategy highlights endowing a high-permittivity environment within polymer photocatalyst in a controlled manner is crucial for enhancing photocatalytic redox reactivity. Furthermore, this study shows that this hydrophilic crosslinker approach has a broad applicability in general polymer semiconductors and their nanoparticulate photocatalysts.

Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images.

This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitting problem. The volume data were rotated and translated with random intensity and frequency, in three dimensions, and were iterated as the number of slices in the volume data. Then, for every slice, a portion of the motion-free k-space data was replaced with motion k-space data, respectively. In addition, based on the transposed k-space data, we acquired MR images with motion artifacts and residual maps and constructed datasets. For a quantitative evaluation, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient of correlation (CC), and universal image quality index (UQI) were measured. The U-Net models for motion artifact reduction with the residual map-based dataset showed the best performance across all evaluation factors. In particular, the RMSE, PSNR, CC, and UQI improved by approximately 5.35×, 1.51×, 1.12×, and 1.01×, respectively, and the U-Net model with the residual map-based dataset was compared with the direct images. In conclusion, our simulation-based dataset demonstrates that U-Net models can be effectively trained for motion artifact reduction.

Strained Pt(221) Facet in a PtCo@Pt-Rich Catalyst Boosts Oxygen Reduction and Hydrogen Evolution Activity.

Over the last years, the development of highly active and durable Pt-based electrocatalysts has been identified as the main target for a large-scale industrial application of fuel cells. In this work, we make a significant step ahead in this direction by preparing a high-performance electrocatalyst and suggesting new structure-activity design concepts which could shape the future of oxygen reduction reaction (ORR) catalyst design. For this, we present a new one-dimensional nanowire catalyst consisting of a L10 ordered intermetallic PtCo alloy core and compressively strained high-index facets in the Pt-rich shell. We find the nanoscale PtCo catalyst to provide an excellent turnover for the ORR and hydrogen evolution reaction (HER), which we explain from high-resolution transmission electron microscopy and density functional theory calculations to be due to the high ratio of Pt(221) facets. These facets include highly active ORR and HER sites surprisingly on the terraces which are activated by a combination of sub-surface Co-induced high Miller index-related strain and oxygen coverage on the step sites. The low dimensionality of the catalyst provides a cost-efficient use of Pt. In addition, the high catalytic activity and durability are found during both half-cell and proton exchange membrane fuel cell (PEMFC) operations for both ORR and HER. We believe the revealed design concepts for generating active sites on the Pt-based catalyst can open up a new pathway toward the development of high-performance cathode catalysts for PEMFCs and other catalytic systems.

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images.

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm's appropriate application.

Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study.

Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.